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Power BI for Data Analytics - Full Course for Beginners

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nerds. Welcome to this full course

tutorial on PowerBI for data analytics.

This is the course I wish I had when I

first started as a data analyst. You're

going to be working right alongside me

as we start with the basics of learning

how to visualize insights and doing this

with a variety of realworld data sets.

This foundation will help us build out

our very first portfolio project. Now,

in the second half of the course, we'll

go to more advanced concepts like data

cleanup with Power Query and data

modeling with DAX. We'll finally put

this all together with our final

customizable project. Now, to master

this tool, we're not going to go

straight for 8 hours. Instead, we're

going to break it down into 10 to 20

minute lessons. During this, we'll have

exercises for you to learn while doing,

not just watching, followed by some

practice problems to reinforce your

newly learned skills. Now PowerBI is one

of the most popular business

intelligence tools in the world and when

looking at holistically across all tools

for data analyst it's in the top five

and for business analysts it's even

higher in the top four. Technically both

these cases PowerBI is the second most

popular BI tool. However over the past

couple years it's been gaining

popularity over its competitor Tableau

and I expect it to eventually become the

number one tool. Now what gives me the

street cred to teach this tool? Well,

PowerBI was the first data analytics

tool I learned after Excel when starting

my data analyst career. I've built

countless dashboards for a global 500

company to improve their supply chain

logistics. And I even implemented

PowerBI when working for Mr. Beast,

though I can't share anything because of

and I've also built a few courses on

data camp that have over 30,000 students

and teaching them how to use PowerBI.

And so over the years, I've been

cataloging all the most important

features inside of PowerBI and I put it

all into this course which is for

beginners. You don't need any previous

analytical or dashboarding experience.

We'll be starting with the first half to

build up your knowledge on the

fundamentals with getting PowerBI

installed and connected to the data for

this course. From there, we'll get you

familiar with working around and how to

manipulate a PowerBI report. Then we'll

shift into practical exercises,

analyzing data, using the most popular

charts, and implementing other tools

like slicers, buttons, and bookmarks. At

the end of these basic chapters, we'll

put your skills to the test to build an

interactive dashboard to help job

seekers gain insights into the top roles

available in data science. For the

second half of the course, we're going

to ramp things up and dive into advanced

analytical features. We'll learn how to

use Power Query and DEP to connect to a

variety of data sets and perform ETL or

extract, transform, and load. Finally,

we'll learn the basics of data modeling

and perform advanced calculations with

the DAX language. By the end of the

advanced chapters, we'll have built an

upgraded version of our dashboard in the

first half that focuses on the top

skills and jobs in data science, which

I'm going to show you how to share both

of these projects so you can demonstrate

your newfound experience in using

PowerBI. Now, I'm a big believer in

open- sourcing education. So, this

course and all the files and content

needed to complete it are completely

free. I not only get you set up with

installing PowerBI, but I also provide

all the different reports and dashboards

needed throughout. Unfortunately, the

AdSense revenue alone for this video is

not going to be enough to supplement all

the different costs associated with

building this out. So, I have an option

for those that want to help support. For

those who purchase my supporter

resources, you're going to get access to

features to help speed up your

learnings, all provided through this

custom dashboard to track your progress.

In here, you'll be able to watch all the

individual lessons from the course, so

it's not one long 8-hour video. Then,

after the video lessons, you'll get

guided practice problems that will not

only provide the solution, you'll even

get my step-by-step lesson plans that

walk through each of the lessons as I

perform them. And finally, when you

complete the course, I'll email you a

certificate of completion that you can

upload to LinkedIn to show your

experience. One quick shout out before

we begin, and that's to Kelly Adams.

She's the brains behind the practice

problems. And if I didn't have her help,

I probably would have never finished.

Anyway, before we actually jump into

this course, we need to understand what

is PowerBI and more specifically, where

the heck did it even come from? Well, it

all started with this. Yep, that's an

Excel spreadsheet. And this bad boy is

used by over 1 billion users monthly.

Now, let me be clear. Excel is great. I

got a whole course on it, and I even

recommend you learning it before

learning PowerBI. However, this is where

our problem actually begins. So, back in

the 1980s, this dude decided he was

going to revolutionize the world. I'm

Bill Gates, chairman of Microsoft.

In this video, you're going to see the

future. So, Excel came on the scene and

its goal was to dominate the spreadsheet

software industry. Now, to be clear, its

primary purpose at the time was people

like analysts that needed to store and

analyze data that was in rows and

columns. This tool has done a heck of a

job since then as it's eaten up most of

all the market share. There's only a few

other competitors and they're not even

close. Now over the years Microsoft has

adding more and more features to this

such as Power Pivot in 2010 which is a

great tool for data modeling and also

using the DAX language. Then Power Query

in 2011 which is my favorite tool and

allows ETL or extract, transform and

load of multiple data sets into Excel.

Anyway, with this supercharged Excel,

this has led managers to demand more and

more out of their employees of what they

can get from spreadsheets. So that way

they don't have to get in the sheets.

And this is where dashboards come in.

Yep. Just like this 1981 Delorean.

Analytical dashboards draw their

inspiration from car dashboards which

allow drivers to get insights at a quick

glance. Now you can overdo upgrades to a

dashboard and get yourself into some

trouble, but that's for an upcoming

lesson to go over. Anyway, building

dashboards in Excel, a tool that was

designed to manipulate data in columns

and rows for mainly analyst, comes with

a host of new problems. If you don't

build it right, you can have your users

dragging stuff all over the place. Hey,

come back here. Drop downs on the

surface, although they look simple, take

a bunch of formulas behind the scenes

even to get those values into the drop

down. And because of this, charts take

even more formulas to work properly and

get them into their visualization. Oh,

and don't even get me started on

sharing. How the heck do you even know

which dashboard is the most up-to-date

Excel file, especially when everybody

send them around? Now, in order to solve

this problem, in 2013, Microsoft

released their business intelligence

solution. And this tool allows you to

create dashboards. Super easy. All I got

to do is load the data in. So, adding

something like a dropown is super

simple. All I do is insert it into my

visual and then add in the appropriate

field. Building a chart is just as

simple. All I got to do is add it in and

add the appropriate fields. Oh, and my

dropown automatically syncs and works

with this. And with a click of a button,

I can publish this dashboard and my

co-workers can have access to my most

up-to-date dashboard. Now, I am getting

ahead of myself. We need to be aware of

the different parts of the PowerBI

ecosystem. First up is this, which we've

been in, and that's the PowerBI app, or

also known as PowerBI Desktop. Unlike

Excel, this bad boy is free.99 on

Microsoft Store and it has slightly

different terminology than Excel in that

this is a report, not a spreadsheet. And

then we can add multiple different pages

to it, not Sheets. Now, this report

saves as its own file. And if you wanted

to, you could go ahead and just share

this file with a co-orker and they can

open it as long as they have PowerBI.

However, you get into a similar problem

with Excel dashboards and that you got

all these files. So, what's the

solution? Well, the PowerBI service.

This is a cloud-based platform. You get

to it in your internet browser. And it

allows you to share dashboards with all

your best friends. So, let's show what

we can do with this. Remember that

previous Excel dashboard that I built?

Well, I recreated in PowerBI. And now I

want to go forward with sharing it. All

I got to do is click publish. And bada

bing, bada boom, it's inside of the

PowerBI service, ready to be shared with

all my co-workers inside of my

workspace. Workspaces are just areas you

can store different dashboards and you

can invite certain friends to certain

workspaces. We'll go in more detail on

this in an upcoming lesson. Anyway, we

could share this a multitude of

different ways. Co-workers could come

inside of the PowerBI service and access

it. If the data is not confidential, I

could publish this data to the web and

you could access it. This is actually

how I went about sharing our different

projects that we're going to be building

in PowerBI. Check them out the links. Or

you could share to other Microsoft

services that you're collaborating on

such as SharePoints or even Microsoft

Teams. Now, there's one big drawback to

PowerBI service and that comes to the

cost. It ain't free. They do have a free

option, but it's super limiting and I

don't recommend it. Anyway, for this

course, I purchased the PowerBI Pro

license, and I'll go through and show

you all the features for it. But to be

very clear, you don't need to purchase a

license for this course at all. I'll

show you everything you need to know.

The only reason why you'd need to buy a

license is if you want to share a

dashboard to the service like I did with

those dashboards. Now, real quick, you

may see this new branding come out of

Microsoft Fabric, and what it's trying

to do is consolidate all of its

different data analytical, data

engineering, data science tools into one

platform. Don't worry, not a big deal.

PowerBI is still in there, still works

just fine. It's just under a different

umbrella. So, let's get into the course

intro next so your co-workers don't ask

you how to export to Excel from PowerBI.

Now, we got that out of the way, let's

get into the course material. We're

going to first start with understanding

all the different resources available

for free along with the supporter

resources and then from there exploring

what data set we're actually going to be

analyzing for this course. With the link

provided, you can navigate to this which

is the Google Drive that has all the

different folders and files necessary

for the course. Each folder has a

different purpose. Up at the top are the

different projects. In each is the final

dashboard along with a write up on it in

a readme file. Under this are the

individual chapters. Right now, there's

four chapters in this course. And then

inside of this, they have the files for

each individual lesson. Conveniently,

I've numbered them all. Next up, after

the chapters is the data folder, which

has our data sets, all in a variety of

different forms. Don't worry, I'll be

walking you through how to use each

individual one of these as we go

through. And finally, we have folders

for resources and read me, which aren't

really important right now. We'll cover

more later. Anyway, how the heck do we

get any of these files? Well, you can

download any folder by just clicking

download or even a file by doing the

same. However, to make it easier on you,

I recommend just using this download all

option up at the top. Now, this bad boy

is pretty big. It's almost a gig and

it's probably going to take over a

minute to download. So, if you have a

slow internet connection, you may want

to just download individual folders as

you go through. Now, for those who

support the course through purchasing my

supporter resources, you'll have access

to this custom dashboard where you'll be

able to go through and watch an

individual lesson. And then after this,

you'll be guided to all the different

practice problems to help refine your

knowledge. Now, on top of this, you're

going to get access to the course notes,

which are broken down by chapter. These

break down concepts in a similar

structure in how I do in the video

lessons, so you can follow right

alongside me if you're more of a visual

learner. Just as a reminder, there's no

requirement to purchase these supporter

resources. They're just a way to help

fund future content like this. Anyway,

what the heck are we actually going to

be analyzing in PowerBI? Well, you're

going to be taking the role of a job

seeker and exploring some of the top

salaries and skills of data nerds. And

for this, we're going to be using data

from my app, which has almost 4 million

job postings right now. Anyway, it tells

based on a job title, such as data

analyst, and a location, such as the

United States, what are the top skills

requested in job postings? Right now,

PowerBI's in almost one in every eight

job postings. And this app not only

tells us about skills, but also about

jobs as well, like what are their

salaries. You can even evaluate trends

of skills over time, which that's how I

did that in the last part of the video.

Now, as I mentioned, the data sets we'll

be using for this course are inside of

this data folder right here. But the

primary one that we're using for the

beginning of the course of this one here

of job postings flat. This bad boy has

job postings from 2024. In fact, it has

almost 500,000 job postings from this

year. And these job postings aren't just

limited to data analysts. We also have a

variety of other different things like

data engineers and data scientists along

with their associated senior roles. And

you're not limited to just exploring the

United States like I'm going to do. You

can explore any host of different

countries. Now, with any course, you're

probably going to get stuck along the

way. So, how do you get help with this?

Well, I don't recommend jumping straight

into the comment section and asking for

help. Instead, you can get an answer a

lot quicker with chat bots like Gemini

or Chat GBT. You can either ask it a

question or put in your error message

and it will go through and provide you

stepbystep instructions on what to do.

Now, feel free to use any free chatbot.

Judge BT and Gemini offer this, but I've

noticed that Gemini has been the best at

working with PowerBI. All right, so if

you haven't done so already, it's your

turn to now go through and download

those course files. In the next lesson,

we're going to be going through and

installing PowerBI and walking through

the guey or graphical user interface in

order to better understand how to make

visualizations. With that, I'll see you there.

All right, welcome to the first chapter.

We're going to be doing a grand tour of

PowerBI. The purpose of this is not for

you to be a master, but more of you to

be able to understand all the different

features and functionality of PowerBI

app and also the service. In this

lesson, we're going to be going over how

to get PowerBI installed and set up on

your computer. After that, we're going

to do a walkthrough of the UI in here,

understanding things like the ribbon,

the different views and panes. In the

second lesson, we're going to take it a

step further and build a very simple

dashboard that goes through and analyzes

our data set and gives us an intro of

what we're going to be capable of doing

later on. And then finally, in the third

lesson, we're going to be moving into

sharing that dashboard you built,

specifically using the PowerBI service,

which is the basically only method

available to actually upload and share

your dashboard. Now, by the end of this

chapter, you're going to have a holistic

understanding of how PowerBI actually

works and all the different

functionality of it. You're not going to

be a master, but you're going to be able

to now take this a step further as we'll

So, first things first, what are the

operating system requirements for this

course? Well, PowerBI is exclusive to

Windows only. right here. I'm going to

go ahead and minimize this down. I'm

running this on Windows. Now, you may be

like me and have a Mac, which that's

what I'm filming this on right here. And

if you try to actually search for

PowerBI on Mac, you're going to find

that it's not available. Once again,

like I said, it's exclusive to Windows.

Even trying to search for it on the App

Store, nothing appears except for a

solution that I actually use. So,

Parallels, which I've been using for the

past 5 years, is a virtual machine and

it allows you to run here. I have

Windows inside of my Mac machine and

inside of that I have PowerBI running.

One neat thing about Parallels is they

have this thing called coherence mode.

So I click this blue icon right here.

It's going into coherence. And from

there it allows me to have that PowerBI

window in its own window alongside any

other windows I may have open. So it's

like I'm basically having a Windows app

inside of Mac and it's pretty seamless

environment. Anyway, I have an affiliate

link for those that have Mac and want to

run Windows on their machine. And they

have a few different options you can do

for this specifically. You can get it

either as a subscription or a one-time

purchase. If you do the one-time

purchase, then you don't get renewing

updates along the way. So, that's why I

stick with the subscription. Personally,

I like to fine-tune of whether I can

have more than just 8 GB of RAM. So, I'm

using the Parallels Desktop Edition,

specifically that Pro Edition. Now, one

note, they do have an option for

students to get it in a very discounted

option. So, take use of that if you can.

Now, now that we're past operating

system, there are a few different

computer requirements you need to think

of because this is a pretty intense

software we're using. Specifically,

Microsoft themselves recommends the

following that you have Windows 10 or

above. They recommend 4 GB or above and

then a CPU that is 64bit. Now, I have my

own personal recommendations which are

based on my experience and seeing how

slow this app could get depending on

your RAM. Specifically, if you have

Windows, you need to get 8 gigabytes or

above of RAM. You can do the four like

they recommend. It's going to be super

slow. For a Mac, I wouldn't do anything

minimum below 16 GB. And that's because

Apple or Mac is already running already,

and that takes up enough space already.

So, if you want to have this VM, this

virtual machine that's taking up 8 GB,

it's going to eat into that 16 GB. I

built this entire course using a virtual

machine with 8 gigabytes of RAM. So, I

know it works for me and I know it's

going to work for you.

Let's get into downloading PowerBI. And

you could Google it and go to this

download link and download it, but I

highly recommend you don't do that.

Instead, we're going to be installing it

from the Microsoft Store. And there's

four key reasons why. First, it has auto

updates. Two, it's a more efficient

download, so it's going to take up less

space. Three, there's no admin

privileges that are going to be

necessary or enabled later on. And then

finally, if you're outside the US, it's

going to adapt to your system languages

and preferences. All right, so here I am

on a fresh Windows install. We're going

to go ahead and open up up the Microsoft

Store. Inside of here, I'm going to

search for PowerBI. We want PowerBI

desktop. This report builder is just a

lightweight version of PowerBI that

doesn't have all the features. You don't

want that. And we're going to go kick

here and click get. Then click one more

time to get get your download completed

in less than a minute. And so I opened

it up. When initially opening up, this

is what you're going to see. And you can

start from any form of data source. They

may give you recommended options. Then

when we start generating more files,

they will appear down here in recent. I

just want a blank report, so I'm going

to go ahead and click that. On opening

it, it prompts me that dark mode is

here. And I'm not gonna lie, normally

I'm very much a fan of dark mode if you

watch any my previous courses, but they

uh for there some reason the contrast

just isn't right in my eyes. I don't

like it that much. So I'm going to

recommend at least when we go through

this tutorial, we're going to leave it

Now, we're going to dive in some

terminology that you need to have down

pad, especially as we're going through

this. Anytime I need to tell you to

navigate somewhere, you need to

understand where I'm telling you to go

to. First up, like any Microsoft

product, is the ribbon, and it's located

up here at the top. There's a variety of

options. We're going to walk through

examples of how we can use each of these

in this lesson. To the left hand side,

we can select a few different views. We

have a report view, which is where we're

going to build our dashboard, table

view, where we can view our data, model

view, and then also DAX where we can run

queries inside of here. Now, after these

views, that's what we've gone through.

I'm going to go back to this report

view. The other thing to know about is

PES. We have PES over here on the right

hand side. The three main ones, which

there's going to be more that we'll get

to, are filters to be able to filter

down our page, visualizations, and then

also data. And this will show our data

model inside of it. Now, the last main

thing to cover is the canvas. That's

right here in the center. That's where

we're going to be building our

dashboard. And unlike Excel where we

have different worksheets, here we have

what are called pages. And so you have

different pages that's going on. And

this is all within within PowerBI. This

is your PowerBI report. In Excel, you

would say this is a workbook with

All right. So, let's start diving into

each one of these. We're going be going

through all of the different ribbon tabs

here and then also through the different

panes that we have available. We're

going to see how this interacts with the

canvas. So, let's start with that home

tab first. I'm going to close that out

and get to home tab. The primary thing

I'm using this tab for is for data and

also editing my queries on how I'm

actually cleaning up my data. As we can

see there, there's a variety of

different source we can choose from. We

can get it from anywhere from Excel

workbook to SQL Server to a text file

and even the internet. Now, I want to

make this portion of the video

interactive. So, feel free to follow

along with me. We're going to actually

put in data into our PowerBI file by

saying enter data. And I have this popup

that comes up that says, hey, create

table. Specifically, I have this data I

want to input into there. It's very

simple. It's just a column of different

job titles and salaries associated with

it. So, I'm going to go through and put

all those different values into here.

First one of business analyst. I'll

press enter and I'll start a new row and

also put the three others of that

analyst, engineer and also scientist.

Now also I want another column. So I'll

click insert column. From there I'll put

in the different salary values for each

of these. I don't want these column

names to be just column one and column

2. So I'm going to double click inside

of here and change it. And now that we

have that, looks like our table's almost

complete. I just want to give it a

better name than just table. We'll give

it salary

data. From here I'm going to click load.

We're not going to do edit. That's going

to open the Power Query editor. We'll

worry about that in another chapter.

It's going to go through now and load

this data into here. So, it's going to

go anytime you load data, it's going to

go through that loading process. And we

can see that it's inside of this PowerBI

report because inside of our data pane,

we have that table salary data with our

two columns, job title and salary. I can

also go to the table view and I can view

it here showing all the different values

inside here. I kind of like this view a

little bit better to inspect it. Anyway,

so back to that home menu. As you can

see, there's a variety of different

sources that we can connect to and

actually use up here in that home menu.

Well, we're going to be diving deep into

this topic in chapter 3. Specifically,

it's focus on Power Query, which is the

tool behind the scene to perform ETL,

extract, transform, load, and get a

variety of different sources, clean it

up, and use it, and use it all within

All right, we're going to jump real

quick away from the ribbon because as

you can see, we have a few different

options available. But now that we

covered that home, I want to jump into

these other panes of filter

visualizations and data. Let's focus on

visualizations first. As you can see up

underneath the section of build visuals,

we have a host of different

visualizations to choose from. In

chapter 2, we're going to be working

through pretty much every single one of

these so you understand what they can do

and their capabilities. So, I'm going to

go ahead and click this button of this

stacked column chart. It's going to go

ahead and throw it into here. I'm going

to spread it over the middle. And the

visualization right now is blank that

showing this gray bars right here

showing there's nothing inside of it.

Now, underneath these visualization

options, you have different options.

This is, as you notice, I've h I have

the actual graph selected itself. And

so, I have this x-axis, y-axis, and

legend selected. If I have just the page

selected, those go away. I have to

actually select the visualization.

Anyway, these have field wells and they

allow you to put things inside of here.

Specifically, over here we have our

data. Let's put some data into it. So,

I'm going to take job title and drag it

on down into the x-axis and it goes into

this field. Well, nothing's appearing

because we don't have any values. So,

I'll need to take now the salary and

drag it on down and put it into the

yaxis. Now, you may notice the

difference between these two. Job title

stay the same, but salary changed to an

aggregation of sum of salary. It's doing

a sum. If I click this down arrow right

here, I could actually change it to a

host of different things. If I wanted to

do count, I could do that. Every one of

these has one value inside of there. So

that's why it says one. We're going to

just go with sum to see keep it easy.

Now for the visualization itself,

whenever I hover over it, it will

actually display information from it.

This is called a tool tip. So for this

one, I have the data scientist and it

tells me that the sum of salary is 105,000.

105,000.

And I can scroll over all the other ones

to see theirs as well. PowerBI also does

this thing where it automatically gives

it a title. Right here, it's saying sum

of salary by job title. We'll go with

that for the time being. Now, that's the

visualization. That's the data pane.

What happens if we want to filter the

data? Well, we can use the filters pane.

Now, inside of this, there's two main

fields. filters on this page and filters

on all pages because right now I'm

clicked to the actual page itself. If I

click to the visual three things popped

up filters on this visual so I'm select

that filters on this page and filters on

all pages. Let's say there's a case

where I just don't for this visual

specifically I don't want to see

business analysts. I don't really care

about it. What I can do is click this

expand arrow right here and it has it

selected to basic filtering and I can

say select all but then remove business

analyst. Now I could also do filtering

on the salary and I could do it in a

dynamic way is less than or greater than

a certain amount but we're not going to

Let's now get through the rest of these

ribbon tabs. In insert we have different

options to insert. Specifically, if I

wanted to insert some sort of new

visual, I can do that to insert it.

Personally, I'm not really a fan of that

because then I still have to come over

here and then let's say I wanted

something like the clustered bar chart.

I have to click that for it to change

it. So, we have this bar chart in here.

Now, let's actually go in and fill it

out as well. As you can see, it also has

a y-axis and x-axis. Similarly, it's

opposite, right? So, I'm going to put

job title up in the y ais and then

salary down in the x-axis. And as you

note, we have four values here because

this one where if we look at the

filters, we have this filter that's

removing business analyst. But when I

click this visual, it does not have a

filter on it. Anyway, getting back into

that insert tab, as we can see, most of

these options are methods to insert

certain objects into here. Next up is

the modeling tab, and this allows us to

do things like create measures, columns,

tables, and even parameters. Chapter 4

is going to be heavily focused on that

using DAX for this or data analysis

expressions. We're going to touch all

those different buttons in that modeling

tab, but let's give a demo real quick of

it. Quick note, this is the last chapter

and the most advanced chapter of this.

So, what we're going to cover right now

is highly advanced. Don't get

discouraged if you're not following

along. We're going to repeat it a bunch

later. Anyway, I could do something like

create a new measure. And inside the

formula bar here, let's say I wanted

something like the average salary. That

would be the name in this case of the

measure. And then I could use a DAX

function such as average and run this on

a column name. Specifically, I want to

run this on the salary column. If you're

familiar with Excel functions, DAX

functions have a very similar syntax,

although works a little bit different

with data modeling. Cover again in

chapter 4. Anyway, go ahead and run this

by pressing enter. And when I open up

that data pane and look under salary

data, I can see now here that we have

this measure of average salary. And I

can see it that it's also a measure

because it has a little calculator right

next to it. With this measure, I could

do something like if I wanted to put it

inside of a card, I have this card here.

I could draw average salary to that

field. Well, and it looks like the

average salary is around 90,000. I don't

really want this visual. So, I'm going

to click these three dots here and click

remove. Next up is the view tab, and we

can change our view. Specifically, if

you had a certain color format you

wanted to use, like dark mode, you could

do that. However, we're going to keep it

uh light theme for this. We can also

change the different layout options.

Now, something that's important up here

in this views is the show panes. Right

now, we have filters selected. If I were

to click it again, filters disappears.

There's other panes. So, I went over

filters, visualization, data, but you

also have the bookmark pane, selection

pane, performance analyzer, and sync

slicers. This gets a mess if you have

all these enabled. We're going to cover

all of these in upcoming chapters, but

for right now, we'll just leave the

filters one enabled. Next up is the

optimize tab. And the one that I'm

finding myself use from time to time is

this performance analyzer, which is also

a pain that can pop up from here to

there. Anyway, like before, remember we

had that cross filtering you can do.

Sometimes if there's a lot of visuals,

you may notice that your report's

slowing down. You can actually inspect

this by doing going to performance

analyzer, selecting start recording.

Whenever I click on it, it actually

records how long the cross highlighting

takes and in this case, how long it

takes to undo the cross highlighting.

Here it is in milliseconds. Not that

much, but I promise you will be build

bigger reports. And this will be a great

way to find bottlenecks. Last tab up is

help. And unfortunately with any type of

help ribbon in any Microsoft product, I

don't find that it's very useful at all.

If I have any sort of comment, I'm

typically going to a chatbot such as

chat GPT in this case. And it's actually

pretty good at going through and telling

me how to manipulate all the different

fields to make different things in

PowerBI. More recently, I've had better

luck, especially with PowerBI, using

Google's model of Gemini, specifically

2.5 Pro, especially when it gets into

advanced things like DAX and Power

Query. I found that it's been pretty

realistic in providing me what actually

the guey interface is like. Vice Chachi

All right, next up is the file menu,

which you can over here on the lefth

hand side. This looks very similar to

whenever we first open PowerBI except we

have some additional features over here

such as saving, sharing, exporting, and

publishing. I'm going to go ahead and

save this report right now. I'll call

this chapter 1 intro. It's going to be

saving it as a PBX file. Saving this to

my desktop. With PowerBI, you need to

save often. Sometimes it will end up

crashing on you and there's no autosave

feature or enablement with it. So

practice often just clicking save or

control S. One other thing about the

file menu. Let's go back into it. And

that's down here. And that's in options

and settings. If we need to update any

options, we're going to go here. There's

a host of different things you can go

to. But let's say in our case that we

enabled that dark mode and we want don't

want to do it anymore. Under report

settings, I can go to customize

appearance and I could change it to the

different modes here. Another tab that I

find myself frequently using is preview

features. Now, since you download from

the Microsoft Store, it's going to

frequently get updates and you'll get

new preview features available. In order

sometimes to enable those features, you

actually have to go through and select

them on whether you want to enable them

or not. They're not just going to be

enabled by default. This is where you

control that. The other thing to note

real quick is this on Copilot preview.

It says Copilot isn't available because

you're not signed in. I'm going to go

ahead and close out of this and go to

home. You can see you have copilot up

here. If I go try to click it, you're

going to have to enter a work or school

email address that has a PowerBI service

associated with Copilot access. I don't

have this enabled. And so that's why I'm

recommending CHBT and Gemini. During

this course, we're basically not going

to be able to use Copilot at all because

you basically have to pay for it and

there's so many other free options out

there. I'm not paying for it. All

right, last thing to cover is all the

different views. We're spending a lot of

time looking here at this report view.

We also have the table view where we can

actually view our data. Notice here our

salary column right now is formatted

using general formatting. We can

actually format it as currency which if

we go back to that report view, we can

see that that sum of salary here and sum

of salary here isn't using currency. So

under table view, I can select that

salary column and I can make it into

currency. And now that that's enabled,

whenever I go back to my visualization,

it actually updates all the different

ones that are attached to it with

currency. Pretty neat. So this data view

is not only great for viewing our data,

but also cleaning it up. Next up right

here is our model view. And what this is

showing is our model. Right now, we only

have one table inside of here, and it

has our two columns along with our

measure. Now, this is a sneak peek of

the file of the last lesson that we're

going to be doing, and we're going to be

building a pretty complex data model by

the end of this, seeing how it all works

together. So, this view is really great

at understanding how all these different

tables in this case interact with each

other. Right now, in our current file,

pretty useless. All right, the last view

is the DAX query view. This is actually

a newer view that was introduced

recently. But anyway, if I wanted to do

something like look at the salary data

and go to quick queries. I rightcicked

that by the way. I could show the top

100 rows, but I'd argue that that data

or that table view is actually better

for this. The other option I have here

is I can rightclick it, go to quick

queries, I could go to column

statistics, which I actually do find

useful in that it provides statistics

about the different columns in the data

set. You can get a holistic view really

quick. Anyway, the results appear in

this table below and it automatically

generates the DAX is all DAX right here

above this. So, you don't really need to

know understand what's going on whenever

you're going through this. You can just

get the results you need. I could even

do something like if I want to look at

average salary and going under quick

queries, I can go into something like

define and evaluate and it has the DAX

here in order to look at this measure

and what is the value here. Anyway, like

I mentioned, this is going to be the

focus in chapter 4 on DAX. This is just

an intro to get you understanding what's

going on here, but in no way do you

understand what's going on with these

formulas here. All right, so that's the

grand tour to PowerBI. Once again, don't

be discouraged if you didn't follow

along with every little thing that I did

there. Every single step that I did

during this, I'm going to be repeating

not only in the next lessons, but also

in the next chapters coming on. This was

only done to basically show you a

holistic view of what the PowerBI app is

actually capable of. I promise you we're

going to go over everything again. All

right, for those that purchase the

supporter resources, first of all, thank

you. Second of all, you now have some

practice problems to go through and get

more familiar of the UI in PowerBI. In

the next lesson, we're going to be

jumping into building our very first

dashboard. With that, I'll see you there.

All right, welcome to this lesson on

building your first dashboard in

PowerBI. Purpose once again is not for

you to become a master of this, but more

for us to understand the PowerBI app,

its capabilities, and what it's capable

of. The dashboard they're building is

pretty simple actually. Let's check it

out. Here I am inside the PowerBI app

with our final dashboard and it's going

to be using the jobs data set that we're

going to be using for a large portion of

this course and specifically we have

some attributes showing we have a title

up at the top three cards displaying the

job count average yearly salary and then

also average hour salary for the jobs

that we have selected. Specifically, we

have three jobs in here of data

engineers, data analysts, and data

scientists. And with that, we have a map

too displaying it. Be able to see it

throughout the world. And we can zoom in

on different locations on where

But before we dive into that, we need to

dive a little bit deeper into

understanding what are the different

data sources we can import in and

actually visualize in PowerBI. Now,

there's hundreds of different sources,

but I like to break it down into four

major types. The first are files such as

Excel or CSV files, which we're going to

demonstrate. You could also do things

like databases or cloud services such as

Salesforce, maybe even Snowflake. And

then finally, the other major one are

web sources, which we will demonstrate

in a later chapter. In a real world

scenario, the most popular of these is

going to be either something like a

database or a cloud service that

probably hosts something like a database

inside of it. For example, let's take my

app data.te.

This aggregates jobs across the world

and right now I have 3.6 million jobs in

it. Here I am logged onto my Google

Cloud account. I host this data in Big

Query. Don't worry, you don't need to

know anything about it. This is

demonstration only. Anyway, I host these

3.6 million jobs inside of here. And

this is where I'm extracting the data

from to load into data.te.

And so, let's just demonstrate how easy

it is to connect to something like this

BigQuery database. This is for

demonstration only. You don't have

access to the database. I'm not giving

it to you. It would cost too much money

to give it to everybody out there. So,

it's demo only. In the ribbon under the

home tab, I can go to get data and they

have a variety of sources, but I'm going

to go to more. Inside of here, I know

it's a BigQuery database. So, I'm going

to search for BigQuery, and I'm going to

go with this first option right here.

I'm going to click connect. Now, anytime

you're connecting to a database, there

could be multiple different tables

inside of there. You could specify a

different table, a different project, or

even a specific SQL statement that you

want it to run in order to extract

certain data. I'm going to just go ahead

and leave all this blank, and click

okay. From there, it's going to prompt

me to go and sign in into my BigQuery

account. So, I have to log through

Google, do my different signin. Then,

once that's complete, I can go ahead and

actually connect because I've now

authorized my credentials to go through

with this. Now, this navigator window

pops up and I have access to all these

different projects within BigQuery. You

don't need to understand that. I'm just

going to go in and actually select now

the table that I want access to.

Specifically, it's this one right here.

I can scroll through and see all the

different columns with it. Comparing

those columns to what I see here in

BigQuery, I know that I got the correct

table. Now, all I have to do now is just

go forward with loading the data in. And

I can see the data loaded in because

it's got this table over here on the

right hand side. Now all I want to

figure out is how many different rows

are in this data set. So I can just drag

any old field. I'm going to drop this ID

field over here. Set the aggregation to

count. And bam, we have 3.62

million jobs at our fingertips in

PowerBI right here. If you remember from

data nerd.te, that's exactly how many

jobs we have in there. So we have access

to all the different data there. I say

we, I don't necessarily mean you. We're

actually going to access some different

data. All right, here I am inside of our

course folder. And as you recall, we

have all the different chapters up here.

Then I have this one folder on data.

We're going to be using all this

different data throughout the course,

but specifically for this lesson, we're

going to be focusing on this job posting

flat, and it's actually a CSV or a

commaepparated values file, but if you

have Excel, it can open up inside of

that. This data set has job postings

from 2024. If we scroll on down to the

bottom, we can see that we have around 478,000.

478,000.

Not that 2.6 million cuz remember my

data set goes back multiple years. I

didn't want to break a computer, so

that's why we limited only to 2024.

Anyway, this is the data set on job

postings that we want. Let's go ahead

and import it in. For this, we're going

to start with a blank PowerBI file. So,

I'm just going to pop it open and we're

going to select blank report. Now we're

going to get data. Now it's not Excel

workbook. This is a CSV file or

commaepparated values. So instead we're

going to come down here and we're going

to select text CSV. I'm going to

navigate into where that project folder

is into data and then into job postings

flat and then click open. As we just

looked at that other file, we can see

that this file in this navigator window

is a lot similar or is similar to what

we saw previously. It looks like

everything's importing specifically. I

want to make sure that it has the

columns updated correctly. Other than

that, looks good. This is just a data

preview and it shows only the first 200

rows. So, we can either load it, which

is going to load it in, or transform

data. You can go ahead and click load,

or if we were to click transform data,

this is going to be opening the Power

Query editor, which we have a whole

chapter on this in chapter 3, going over

how to interact with this completely new

guey that has all the different ribbons

up here. It's completely different than

what we've seen. Don't worry about that.

If you happen to open this up, all you

got to do is hit close and apply right

here. It's the same as clicking load,

and we're going to load the data set in.

Depending on the size of your data set,

this one's not too big. It could take

anywhere from a few seconds to I've had

data sets take a few minutes. And we can

verify that we've loaded it into this

PowerBI app by going over to that data

pane, selecting down on job postings

flat, the name of this. We can see all

the different columns inside of this

Anytime you import any data in, you want

to verify it imported incorrectly and

you want to inspect it. So, I go

automatically into this table view.

There's a few columns I want to call out

real quick. Job title short is the main

column we're going to focus on. You

notice next to it, we also have this

other job title column. But if I click

this drop-down arrow here, we can see

that there's a host of different oh my

gosh, there's so many different real job

titled names. However, when we look at

job title short, there's only 10

distinct names and they revolve around

like data an data analyst, data

engineers, data scientists. Anyway, for

the majority of this course, we're going

to be primarily focus on the job title

short column. Other things we're going

to be caring about, especially for this

lesson, is the job country. And I'm

scraping this data from around the

world. So, we have all these different

countries in here. And then also this

salary data. Right now, you can't see

any salary data because some of it is

blank. But if I were to sort this column

in, let's say, descending order, I can

actually start to see some of the

different values in there. Same thing

for looking at salary, our average. I

can see these values as well. Quick

backstory on why we have these called

salary year average and salary hour

average. If we actually look at the core

data set inside of BigQuery, I actually

have columns depending on how the

salaries are reported in a job posting.

It could have a min value and a max

value. So what I do in this case between

60,000 120,000 I average it and

therefore we get 90,000 for the salary

year average. This just makes it a lot

easier for you. But I want to give you

the backstory of why the column's name

like this. Anyway, similar to how I'm

going through anytime I import a data

set, I'm checking it out. I'm also going

to be cleaning it up as we go. In this

case, salary, hour, average. I can see

it's formatted right now using just

general. I want to format it as

currency. So, I'm going to click this

currency icon. And then also, since it's

hour, I want to have a decimal place.

Specifically, I want two decimal places.

Now, I also want to format salary or

average. I want to be able to see it.

So, I'll sort descending and then format

it as currency. I don't need any decimal

places for this. It says auto, but

sometimes it will give decimal places.

So, I'm going to just put it to zero.

Now, every other column in here looks

fine to me. I'm okay with it. But I do

like to also go inside of the model view

anytime and inspecting. Make sure that

the model is inspected or imported

correctly. And if it's connected to any

tables, it's connected properly. Right

now, we just have one table of job

postings flat. So, everything's looking good.

So, as a reminder, we're going to be

building this dashboard right here.

We're going to focus on first building

these three cards, two visuals

underneath it, and then putting the

title up at the top. Right now, this

visual because I'm not signed in. I was

signed in previously. I'm not signed in.

You're not probably signed in either.

This map right here that normally I have

here is not it's disabled. We need to

enable it. And it gives instructions for

this. Now, let's walk through this and

actually enable this so when we get to

the map section, we know it's going to

work properly for you. So, inside your

notebook, we're going to go into file

and then options and settings and click

options. And then underneath global, it

was telling us to go into security.

Underneath this, we want to enable the

different maps. So, this map and field

map visual should be enabled. We also

want to enable this one on ARJS for

PowerBI. If it isn't enabled, it's also

a map. We'll be covering that in the

maps lesson in an upcoming chapter.

Anyway, click go ahead and click okay.

And in my case, with the dashboard

already built, I can now see the map

visual. You don't have map visual built,

but you will see it built whenever you

build it. If for some reason it doesn't

work when you go to build it, all you

got to do is close out of PowerBI. I'm

going to open it back up. Anyway, let's

create those three cards first. So, I'll

go ahead and throw a card up here, and

I'm going to minimize out of this to try

to make this as big as possible. The

first thing is we want to count. Anytime

we're doing count, normally you want to

use some sort of ID column. We don't

have an ID column in a data set. So all

every time that I do count throughout

this entire course, you're going to see

me throw the job title short inside of

here. Now, one thing to note about this,

this is showing job title short, but

it's saying first job title short inside

of this fields. Well, what I've done

thrown it into the aggregation method

that it's doing, it's doing first. If I

click this down arrow on it, I can

change the aggregation method to

something like last or to count

distinct. In this case, there's only 10

job title shorts or count. And bam, this

shows us all the different counts of the

rows in this data set. 478,000.

And I want this card right here. And

then I'm going to want the two others

next to it. So I'm actually going to

select it and press Ctrl C. And then

clicking inside of here to make sure the

visual is not selected. I'm going to

press commandV and it's going to repost

it. And what I can do is I can drag it

until it's centered in the middle and

centered on this visual as well. Once

again, I'll click outside of that. Press

commandV and take the next one over

here. Make sure it's aligned up

properly. And bam. Okay, this one we

want the average yearly salary. So, all

I'm going to take is that salary year

average column, drag it right into the

fields. It's going to replace it. Now

notice it automatically did for the

aggregation sum sum of salary. Once

again we can go in and then change it to

what we want whether we want something

like the average or median. We'll go

ahead with average. Now with these type

of aggregations that are happening

automatically I can go into that table

view and then I can select a column.

Selecting salary or average I can see

that the summarization automatically

goes to sum. In our case, I'm going to

change it to something like average.

Same thing for salary hour average. I'm

going to change that one to

automatically to average. And now when I

go back, I want to change this one now

to use the salary hour average column. I

drag it in. It automatically does

average. Now let's build these two

charts underneath a bar chart and then

the map chart. For this, we can select

either the stacked bar chart or the

clustered bar chart. We're not doing

multiple values, so it doesn't really

matter on each. I'm going to go ahead

and move it and position it so it's

taking up this bottom half. In this, we

want to count the different job titles.

So, I'm going to take that job title

short to the y-axis and then take also

job title short to the x-axis, which

it's going to aggregate automatically by

count. The next visualization to put up

is the map, and it's located right here.

It's called map. But I'm going not want

you to watch something. If I click map,

it actually changes that visual that I

had selected. So, I'm going to press

Ctrl +-Z to undo that. Or you can just

come up here and click undo last action.

Anytime you're adding a new visual, you

want to make sure you're clicked out of

it. And then click map. And then I'll

just drag it to make sure that it's in

the center where I want it to be. Once

again, we want to see counts of jobs by

location. So, we're not going to use

that job location. And we're going to

aggregate by country because it's more

distinct in what it offers. And when I

drag this in, it's not loading. It's cuz

I need to restart PowerBI. Also, I've

noticed I haven't saved my file yet. So,

this is a good time to save the file.

So, I'm just going to save with the

title of something like jobs dashboard.

And then all I do is I'm going to

rightclick the PowerBI icon down there.

Some recent files going to come up and

just going to open up jobs dashboard

directly. That bottom map visual is

working because we enabled it in

options. We just had to restart PowerBI

to get it to work. Anyway, right now

it's showing all the different

countries. Like this right here is

United States. This bad boy up here is

the Canadians. But what I want in this

is actually well, I need to actually

click in this map visual is we have the

locations, the legend, latitude,

longitude, and then bubble size. I want

the bubble size to be the size or the

count of the different jobs. Once again,

we're going to use that job title short

column and use the counts of that. And

now we can see this. Now, this visual is

actually a little hard to see. Anytime

you want to expand visuals, up here in

the top, they have this focus mode, and

it allows you to drill into a certain

visualization. And now I can see it a

lot more up close. Can zoom into it,

scroll around. It makes it a lot easier

to use. Anyway, scrolling over something

like the United States, I see that it

has around 140,000 job postings in it.

All right, so let's go back to the

report. All right, last thing we need to

do is put a title up at the top.

Navigate to the insert tab. I'm going to

go to insert a text box. And I'm going

to reposition this across the top up

here. I'll just give a simple title like

data jobs dashboard. And this is only a

size 10 font. This isn't really going to

work for what we need. I bump this bad

boy up to 60. Put it at bold. I'm also

going to center it. Also going to make

the title a little less dark. We'll do

this uh black 20% lighter. So, bam. Not

looking too bad. just need to do some

cleanup now.

So, the first thing we're going to focus

on with this is formatting the page

itself, not necessarily the visuals. And

to make sure I'm formatting the page

itself, you need to click down here,

make sure no visuals are selected. And

what I can see over on this

visualizations pane is we have format

your report page. However, if I'm

clicked inside of a visual, it's going

to have format visual or analytics or

whatnot. We want the page. So, we click

into the page format for report page. I

can change things like the page

information such as page one or I can

even change it down here. I'm going to

change it to data jobs and press enter.

Also updates right here. Other common

things that I control inside of here are

things like the canvas settings. Right

now, it's at a 16.9 ratio. You could

change it to something like a letter. I

typically leave it as 16x9. The other

thing that I find myself altering is the

background. If I'm doing some sort of

formatting and I want a specific color,

I could change it to something like

black. If I want this to work though,

the trans. So, right now, actually, I

have to move this over and to be able to

actually see the back of here. Anyway,

this is format black, but it's still, as

I can see back here, because I'm

clicking on there whenever I go to it,

it's still not showing. It's because the

transparency is at 100%. I have to take

the transparency off and I can see, oh,

here's black along these edges or

whatnot. I don't want it to be black. I

just want to show you that's a common

way to do it. Now, after format page,

the other thing to know is format

visual. So, I can click on a visual and

see that hey, it now says format visual.

There's only two major things of a card.

The callout value and then the label.

The label I can toggle on or off. And

opening up, I can change things like the

font size and even the color of the

font. I'm going to leave it like it is.

For the category value, I'll make it a

little bit bigger. I'll make it 50. Now,

what's really neat is if I click on a

similar visual. So, in this case, this

other card, it will also open to that

spot as well. So, it makes it quickly or

easy for us to now go in change this one

to 50. I select this one and I'm going

to change this one as well to 50. Also,

while I'm in here, I want to format the

decimal places showing. I'm fine with

the two decimal places for the hour. The

yearly, yeah, we did format it earlier

to show zero, but now it's doing an

aggregation, so it goes resorts to two.

So, I'm going to change the value

decimal places to zero for this one and

also for the count. All right, let's

move on to these other visuals to clean

them up. I'm going to go into this bar

chart. I'm going to go into focus mode

to make it a little bit easier to

actually see it. In this view, I can

also format my visual and go into

analytics. We'll get to analytics

eventually. Now, we can control things

like the y x-axis grid lines. Let's go

into the yaxis first. You could turn off

something like the values, but I think

they're pretty much necessary. I will

say the uh the title here of job title

short off to the left hand side not

necessary so I'm going to turn that off.

We're going to be giving this

visualization a title. So I don't feel

it's necessary. Anytime it's not

necessary I'm going to remove it. Going

into something like the x-axis I could

set something like a minimum and maximum

range. I could also adjust the values to

be a bigger font or a smaller font.

Overall it's looking good for x-axis.

The other thing is grid lines right

here. Right now they have vertical grid

lines. I never really find grid lines to

be that helpful and they are I find

distracting. So I'm going to go ahead

and just turn those off. Now that's

enough with everything with the visual

portion. We can now go into general.

This holds values that we can affect

such as the title data formats or even

things like the tool tips. Remember tool

tips are whenever you hover over you

actually see the values or whatnot.

Anyway, the first thing I want to change

is up here at the top. I want to change

the title. I typically like to have a

descriptive title or a title that's

asking a question. So, I'm going to give

it this of what are top data jobs. Now,

if I try to adjust this right here, it's

not going to really do anything in this

view that we're doing. I'm going to go

back to the report to actually see

what's going on. I'm going to change the

title to a 20oint font to make it a

little bit bigger. And then also, I'm

going to center it. Next is the map

chart. And there's not a lot of things

that we need to go on except for the

title. I don't really like it. I'm going

to update it to where are data jobs. Put

it to that 20 point and then also center

it. Now there's one other or multiple

minor little things that I want to

update on this and that are well that is

all the different labels associated with

this because right now we have count of

job title short that kind of label

especially for a stakeholder they may

like they may like what the heck is

that? We need to have something that's

more descriptive. The easiest way to

rename this is pretty simple actually.

We're going to click on the visual that

we want to go to and then you go to the

field well associated whatever it is and

in this count case I'm going to double

click on this and I can now alter this.

I can select this all and I want it to

be something simple such as job count.

Press enter. Now when somebody comes

here they can go like oh yeah this is

job count. I'm going to update all the

rest of these as well with these now

being updated to average yearly salary

and average hour salary. The only other

thing that I'm seeing is here on this

bar chart and I'm not liking this count

of job tiles short. So, I'm going to

change this to job count. Now, one thing

to note when I actually scroll over this

to look at a value, I have my tool tips

pop up and it's still going to say this

the column of job title short for the

value and then job count. So, I do

recommend anytime you're doing any of

these to update every single value that

may appear on a tool tip. So I updated

to job title and now when I scroll over

it says job title job count. Similarly I

updated the map visual now and when I

scroll over this tool tip I see country

Last thing to get into is filtering the

data down to what is applicable to our

stakeholders. In this case I know that

my audience only really cares about data

engineers, data analysts and data

scientist. Now, if you recall back from

the last lesson, we can go into that

filters pane and it allows you, if I'm

selecting on a visual, to apply a filter

on a visual. So, in this case, I could

go through and select those three of

data analyst, data engineer, and data

scientist. However, as you saw as I did

that, none of these other cards or none

of these other visuals updated with

this. So, I don't want to do this. I can

come up here and select clear filter.

Selecting onto the page itself. I can

see I have filters on this page. All I

need to do is drag this job title short

over here. And in this case, select data

analyst. You see everything updated.

Data engineer and data scientist. Okay,

that's looking good. And I can close out

of this. So looking good. Now we can

have some data nerd come to this and

hopefully in the way that we've built

it, they can get some common

characteristics out of this. They can

see the different counts, what the

average yearly, what the average hourly

salary is. If they wanted to, they could

drill into just data analyst to see what

the job count is, where the different

salaries, and where they are around the

world. In the next lesson, we're going

to be going through now uploading this,

you pressing this publish button button,

and putting it into the PowerBI service.

And we'll give more details on that in

the next lesson. All right. All right,

for those that purchased the supporter

resources for this course, you have some

practice problems to go through and get

more familiar with the guey of PowerBI.

And with that, I'll see you in the next lesson.

Welcome to this final chapter in the

grand tour of PowerBI. Specifically, in

this, we're going to be focusing on the

PowerBI service and understanding how

you can actually go about sharing your

dashboards and how you're actually going

to share it in the real world. Now, for

the first half, I'm going to give you

the background on the PowerBI service.

I'm actually going to walk you through

the service here on my computer, show

you what it's actually all about, and

then next, we're going to get into

understanding what are the different

licenses you need to access the PowerBI

service. Now, a little bit of a spoiler

alert. You'll only be able to access the

free account from PowerBI if you have a

work or school email account. those that

have only just something like a Gmail

account like me, you can't register and

get a free account. You actually have to

pay for the PowerBI pre uh pro service.

Anyway, we'll get to that when we get

there. Anyway, I give that as a spoiler

because in the second half, we're

actually going to go through and I'm

going to purchase a pro account and show

you how to set up an account so that we

can get our dashboard into the PowerBI

Pro service. And from there, we can do

things like share it to the internet for

anybody without even PowerBI to use,

which you can check out the final

dashboard for this entire course at the

link below. That dashboard is hosted on

the PowerBI service and it makes it

accessible to anybody that accesses that

link. Anyway, we're going to be going

through all of that, setting it up if you want to. PowerBI Pro purchasing of

you want to. PowerBI Pro purchasing of that is not required to complete this

that is not required to complete this course whatsoever. But it is good for

course whatsoever. But it is good for you to go through and understand how to

you to go through and understand how to use this because like I said, you're

use this because like I said, you're going to be using this in the real

going to be using this in the real world.

All right, before we just dive head first in the PowerBI service and explain

first in the PowerBI service and explain that, you first need to understand what

that, you first need to understand what are the different methods to share a

are the different methods to share a PowerBI report. And overall, I found

PowerBI report. And overall, I found that there's two main ones. The first is

that there's two main ones. The first is sharing the PowerBI file itself. And the

sharing the PowerBI file itself. And the second one that we're going to get to

second one that we're going to get to for the remainder of this is the PowerBI

for the remainder of this is the PowerBI service. I do want to let you know that

service. I do want to let you know that this is an option. With the PowerBI file

this is an option. With the PowerBI file or PowerBI report, there's no account

or PowerBI report, there's no account needed. You don't need any type of

needed. You don't need any type of license or prolic. The con, however, is

license or prolic. The con, however, is you as obviously building it need a

you as obviously building it need a PowerBI desktop and whoever you send it

PowerBI desktop and whoever you send it to has to go and download PowerBI

to has to go and download PowerBI desktop. So this PowerBI report that we

desktop. So this PowerBI report that we have has everything we need in order to

have has everything we need in order to send it. This data and everything is

send it. This data and everything is actually all inside of it. So we only

actually all inside of it. So we only need to send this file. Going to the

need to send this file. Going to the file of jobs dashboard. I just want to

file of jobs dashboard. I just want to show it's about 19 megabytes with all

show it's about 19 megabytes with all the data. It's not that big. So it is

the data. It's not that big. So it is possible for you to go through and

possible for you to go through and actually just email to somebody else and

actually just email to somebody else and then for the open it up and be able to

then for the open it up and be able to use this. However, whenever I was

use this. However, whenever I was working for like Mr. beast. We use the

working for like Mr. beast. We use the PowerBI service in order for everybody

PowerBI service in order for everybody on my team to go to a central location

on my team to go to a central location location to access a different PowerBI

location to access a different PowerBI dashboard. It ensures that there's a

dashboard. It ensures that there's a single source of truth and so everybody

single source of truth and so everybody can access the same thing and ensuring

can access the same thing and ensuring that they don't have something that's

that they don't have something that's out ofd, some file that shouldn't be

out ofd, some file that shouldn't be used anymore. The drawback to this is

used anymore. The drawback to this is that anybody that needs access to this

that anybody that needs access to this needs to have a PowerBI account.

All right, so let's jump into the PowerBI service. Once again, you don't

PowerBI service. Once again, you don't have an account yet, so you can't log

have an account yet, so you can't log into this. This is more of a demo

into this. This is more of a demo purpose, so that way if you do or don't

purpose, so that way if you do or don't decide to pursue getting an account, you

decide to pursue getting an account, you know what you're actually getting

know what you're actually getting yourself into. Anyway, I'm here at

yourself into. Anyway, I'm here at app.powerbi.com.

app.powerbi.com. This is my home screen and similar to

This is my home screen and similar to how PowerBI looks like you open it up,

how PowerBI looks like you open it up, they have things like, hey, you can

they have things like, hey, you can scroll down here and get to your recent

scroll down here and get to your recent files. So, one of the key features

files. So, one of the key features inside of here is I can access a report.

inside of here is I can access a report. Let's actually look at our dashboard.

Let's actually look at our dashboard. Uh, future Luke has uploaded it into the

Uh, future Luke has uploaded it into the system and it's available inside of

system and it's available inside of here. Anyway, it's displaying all the

here. Anyway, it's displaying all the different information that we had

different information that we had previously. I can even interact with it

previously. I can even interact with it like we did and it still has all the

like we did and it still has all the different information that we need with

different information that we need with it. In here, I can do other things by

it. In here, I can do other things by going up to the file menu. I could

going up to the file menu. I could download this file and use it locally. I

download this file and use it locally. I can manage permissions of who has access

can manage permissions of who has access to this dashboard within my company. I

to this dashboard within my company. I could waste a bunch of paper and print

could waste a bunch of paper and print it. And I can even do this, which we're

it. And I can even do this, which we're going to be doing later, is embed the

going to be doing later, is embed the report. Specifically, I like to do this

report. Specifically, I like to do this of publish to web. This provides us with

of publish to web. This provides us with an embedded code that we can either link

an embedded code that we can either link that we can send something like an email

that we can send something like an email or you could post it inside of a

or you could post it inside of a website. I'm going to go ahead and just

website. I'm going to go ahead and just copy this link. And then here inside of

copy this link. And then here inside of an incognito window, so I'm not logged

an incognito window, so I'm not logged into anything in this window, I can

into anything in this window, I can paste in this link. And then when I

paste in this link. And then when I navigate to it, I'm able to access this

navigate to it, I'm able to access this dashboard and anybody with this link can

dashboard and anybody with this link can access the dashboard, go through it, and

access the dashboard, go through it, and actually filter down, use tool tips, and

actually filter down, use tool tips, and actually be able to interact with our

actually be able to interact with our data. Now, there's a few other features

data. Now, there's a few other features I want to call out real quick inside the

I want to call out real quick inside the PowerBI service. Over here on the lefth

PowerBI service. Over here on the lefth hand side we have create. This gives you

hand side we have create. This gives you the option to build PowerBI reports

the option to build PowerBI reports without even installing the desktop and

without even installing the desktop and doing it here. Do not do this. Highly

doing it here. Do not do this. Highly don't recommend it. It's not as

don't recommend it. It's not as functional. Build in the desktop app and

functional. Build in the desktop app and then upload to the service. Next is

then upload to the service. Next is browse to let you go through any

browse to let you go through any recents, favorites or even shared with

recents, favorites or even shared with you. Then is probably the most important

you. Then is probably the most important is workspaces. And workspaces is what I

is workspaces. And workspaces is what I create in order to share with certain

create in order to share with certain groups. Now I have my own workspace

groups. Now I have my own workspace where I maintain all the different

where I maintain all the different dashboards that I have and I could give

dashboards that I have and I could give people access to this although that's

people access to this although that's not a good idea. Instead what I want to

not a good idea. Instead what I want to do is I can create other workspaces by

do is I can create other workspaces by creating this new workspace and I can

creating this new workspace and I can store in this one here called data job

store in this one here called data job postings different dashboards within it

postings different dashboards within it that I want people to have access to. So

that I want people to have access to. So this was the dashboard we built which

this was the dashboard we built which we're going to get to the end of this of

we're going to get to the end of this of actually uploading to the PowerBI

actually uploading to the PowerBI service. But I can have other reports as

service. But I can have other reports as well like this one here that connects to

well like this one here that connects to my BigQuery database. And remember we

my BigQuery database. And remember we had 3.6 million jobs in that database.

had 3.6 million jobs in that database. Anyway, this dashboard on the service

Anyway, this dashboard on the service has a direct query access to that

has a direct query access to that database in this dashboard. Pretty neat.

database in this dashboard. Pretty neat. Anyway, I have it all centralized in

Anyway, I have it all centralized in that one workspace that I can give

that one workspace that I can give certain people access to. All I have to

certain people access to. All I have to do is go into that workspace, go into

do is go into that workspace, go into manage access, and add or remove any

manage access, and add or remove any type of people or groups into here. One

type of people or groups into here. One note with this, those that have a free

note with this, those that have a free license, which we're about to get into

license, which we're about to get into license types, but those that have a

license types, but those that have a free license, you won't have the ability

free license, you won't have the ability to create new workspaces. You'll only

to create new workspaces. You'll only have my workspace. But that's actually a

have my workspace. But that's actually a great segue.

So, what are your options to get access to the PowerBI service? Well, they have

to the PowerBI service? Well, they have a few different options that you'll be

a few different options that you'll be able to choose from. The first is free.

able to choose from. The first is free. And like I mentioned, you're going to

And like I mentioned, you're going to need either a work or school email

need either a work or school email account. You won't be able to use any

account. You won't be able to use any personal email accounts. They won't let

personal email accounts. They won't let you use this to create a free account.

you use this to create a free account. Additionally, with the free account, not

Additionally, with the free account, not only can you not create workspaces, but

only can you not create workspaces, but you also won't be able to share any of

you also won't be able to share any of your work. So, that share to web not

your work. So, that share to web not going to be able to do. Basically,

going to be able to do. Basically, you're really limited. Next up is the

you're really limited. Next up is the pro license, and I feel it's the perfect

pro license, and I feel it's the perfect license. If you want to get anything, I

license. If you want to get anything, I would get that one. And that allows you

would get that one. And that allows you to share and collaborate with others.

to share and collaborate with others. Not only can you build reports, but you

Not only can you build reports, but you can also share them to the web like I

can also share them to the web like I demonstrated earlier. Now, the other two

demonstrated earlier. Now, the other two of preamp per user and then also

of preamp per user and then also embedded. Those are just more advanced

embedded. Those are just more advanced PowerBI that we're not even going to get

PowerBI that we're not even going to get into that more has to deal with when

into that more has to deal with when you're dealing with even larger data

you're dealing with even larger data sets or more frequent data refreshes.

sets or more frequent data refreshes. But based on what we're just trying to

But based on what we're just trying to accomplish with this course, that's way

accomplish with this course, that's way out of the scope of what you'd need.

out of the scope of what you'd need. However, this would be something that

However, this would be something that you may need to consider in the real

you may need to consider in the real world when you get to a company and

world when you get to a company and you're working with some really large

you're working with some really large data sets.

All right, the remainder of this lesson is going to be going through actually

is going to be going through actually purchasing the PowerBI Pro license,

purchasing the PowerBI Pro license, setting it up, and then uploading a

setting it up, and then uploading a dashboard so that way we can share with

dashboard so that way we can share with this. As a reminder, this portion is

this. As a reminder, this portion is completely optional. In no way do you

completely optional. In no way do you need to purchase a license to complete

need to purchase a license to complete this course. Mainly, this is just doing

this course. Mainly, this is just doing this for a learning experience in order

this for a learning experience in order for you to see what is actually done and

for you to see what is actually done and what you can accomplish with the PowerBI

what you can accomplish with the PowerBI service. For the remainder of the

service. For the remainder of the course, you will have the option to

course, you will have the option to share your dashboard to the PowerBI

share your dashboard to the PowerBI service, but I'll also be providing

service, but I'll also be providing other methods of how you can share the

other methods of how you can share the file instead in order to be able to

file instead in order to be able to share your work. All right, so let's let

share your work. All right, so let's let me get into it of purchasing this Pro

me get into it of purchasing this Pro license. First thing you got to do is

license. First thing you got to do is verify you're not a robot. And then

verify you're not a robot. And then you'll need to go through and after you

you'll need to go through and after you put in your email, can be a personal

put in your email, can be a personal email. You need to put in all your

email. You need to put in all your different personal information. After

different personal information. After this, they're going to be setting you up

this, they're going to be setting you up with a custom domain name because you're

with a custom domain name because you're not going to use your personal email

not going to use your personal email account to sign in. You're actually

account to sign in. You're actually going to be using something they

going to be using something they assigned to you to sign in further. So,

assigned to you to sign in further. So, don't lose this email that they give

don't lose this email that they give you. And also, don't forget the password

you. And also, don't forget the password that they that you make for this. After

that they that you make for this. After you've gone through and applied all your

you've gone through and applied all your payment information to set up those

payment information to set up those recurring monthly payments, they do want

recurring monthly payments, they do want you to set up a security measure of

you to set up a security measure of using the authenticator app from

using the authenticator app from Microsoft to verify that it's actually

Microsoft to verify that it's actually you. Anyway, this will require you to

you. Anyway, this will require you to log onto your phone and actually use

log onto your phone and actually use that to verify you are who you are.

that to verify you are who you are. Anytime you log into PowerBI, you're

Anytime you log into PowerBI, you're going to have to do this. Once that's

going to have to do this. Once that's done, you'll have a confirmation method

done, you'll have a confirmation method giving you that email in case you didn't

giving you that email in case you didn't write down earlier and you can jump

write down earlier and you can jump right in of start using PowerBI Pro. And

right in of start using PowerBI Pro. And so let's jump right in. And it's going

so let's jump right in. And it's going to take us immediately to the admin

to take us immediately to the admin center within Microsoft 365. If we want

center within Microsoft 365. If we want to get to PowerBI, we go to the corners

to get to PowerBI, we go to the corners up to the left and we click PowerBI. And

up to the left and we click PowerBI. And then bam, we're here inside the service

then bam, we're here inside the service itself. All right. So we've already done

itself. All right. So we've already done a quick tour of this. Now what we need

a quick tour of this. Now what we need to do is we need to link our PowerBI,

to do is we need to link our PowerBI, our desktop app to this service.

So now I'm here back inside of our desktop app. We want to actually go

desktop app. We want to actually go through and sign in. So up in that top

through and sign in. So up in that top top right hand corner, I'm going to

top right hand corner, I'm going to click sign in. Going to put in the email

click sign in. Going to put in the email account that they gave me for my PowerBI

account that they gave me for my PowerBI Pro account. And then after entering my

Pro account. And then after entering my credentials of my login and also my

credentials of my login and also my password, I need to then go to the

password, I need to then go to the authenticator app and inside of here

authenticator app and inside of here insert the number that's on the screen

insert the number that's on the screen in order to access. I'm going to select

in order to access. I'm going to select yes that I wanted to have access to all

yes that I wanted to have access to all apps. But now we want to get this

apps. But now we want to get this PowerBI dashboard into the service.

PowerBI dashboard into the service. Specifically here I am inside the

Specifically here I am inside the service and underneath workspaces we can

service and underneath workspaces we can either upload it into my workspace or I

either upload it into my workspace or I have this one called data job postings.

have this one called data job postings. If you don't you can create a workspace

If you don't you can create a workspace if you did create that pro license by

if you did create that pro license by just clicking new workspace and then

just clicking new workspace and then from there all you got to really do is

from there all you got to really do is put a name for that. You can even assign

put a name for that. You can even assign an image. Click apply and you got this

an image. Click apply and you got this new workspace. Anyway, we're going to be

new workspace. Anyway, we're going to be using my data jobs postings one. So,

using my data jobs postings one. So, feel free to copy that name if you want.

feel free to copy that name if you want. Right now, I only have one dashboard

Right now, I only have one dashboard inside of here. And just a quick note

inside of here. And just a quick note with this right here, it says, hey, this

with this right here, it says, hey, this is the report. So, this is the actual

is the report. So, this is the actual dashboard itself. When I navigate to it,

dashboard itself. When I navigate to it, I can actually see it here. And then the

I can actually see it here. And then the other one is the semantic model.

other one is the semantic model. Basically, it's the data set, all the

Basically, it's the data set, all the data and all the different information,

data and all the different information, the metadata behind it. So, anytime you

the metadata behind it. So, anytime you upload anything, you're going to see

upload anything, you're going to see two. You're going to see the report and

two. You're going to see the report and the semantic model. Anyway, back in the

the semantic model. Anyway, back in the PowerBI app, I can come up here now. I

PowerBI app, I can come up here now. I know I want it up there. I'm logged in.

know I want it up there. I'm logged in. I'm going to click publish and it's

I'm going to click publish and it's going to say, hey, select a destination.

going to say, hey, select a destination. I can either go to my workspace or that

I can either go to my workspace or that one I created of data jobs postings. I'm

one I created of data jobs postings. I'm going to do that. Select select. And now

going to do that. Select select. And now it's going to go through actually

it's going to go through actually uploading it to the service. After less

uploading it to the service. After less than a minute, you'll get the success

than a minute, you'll get the success message and you then from there can open

message and you then from there can open it inside of PowerBI. Let's open another

it inside of PowerBI. Let's open another tab and then bam, here is the dashboard

tab and then bam, here is the dashboard inside of here. And looks like it's

inside of here. And looks like it's working just fine. Filtering all the

working just fine. Filtering all the different data has everything in it.

different data has everything in it. Good to go.

Now, what happens now if you want to go ahead and go into file embed reports and

ahead and go into file embed reports and you actually want to publish this bad

you actually want to publish this bad boy to the web. Well, for you, if you've

boy to the web. Well, for you, if you've just logged in, you're not going to be

just logged in, you're not going to be able to do it unless you enable some

able to do it unless you enable some extra features. Don't worry, they don't

extra features. Don't worry, they don't cost any money. We just have to actually

cost any money. We just have to actually go through and actually set it up.

go through and actually set it up. Anyway, we need to go into settings up

Anyway, we need to go into settings up in the top right hand corner and then

in the top right hand corner and then scroll down until we see something

scroll down until we see something called the admin portal. That's where we

called the admin portal. That's where we want to go to. This has all the

want to go to. This has all the different settings and control at a high

different settings and control at a high level for the service. The first thing

level for the service. The first thing we're going to come over to this filter

we're going to come over to this filter on the right hand side and we're going

on the right hand side and we're going to search for publish to web. I'm going

to search for publish to web. I'm going to go ahead and click this arrow to open

to go ahead and click this arrow to open it up. Right now, mine's enabled. Most

it up. Right now, mine's enabled. Most likely yours is not enabled. I think

likely yours is not enabled. I think it's disabled actually. Let's see. Yeah,

it's disabled actually. Let's see. Yeah, it's disabled. You want to enable it.

it's disabled. You want to enable it. So, make sure that's enabled so you can

So, make sure that's enabled so you can actually publish to the web. You should

actually publish to the web. You should allow it for all users and it should be

allow it for all users and it should be for the entire organization. After you

for the entire organization. After you do this, click apply. Now, there's one

do this, click apply. Now, there's one other setting you have to do as well.

other setting you have to do as well. This is under advanced networking

This is under advanced networking specifically under tenant level private

specifically under tenant level private link. This needs to be disabled. So, if

link. This needs to be disabled. So, if it's not if you're in your case, it's

it's not if you're in your case, it's probably enabled. you need to disable it

probably enabled. you need to disable it anyway. Whenever you disable it, click

anyway. Whenever you disable it, click apply. Once again, with like the last

apply. Once again, with like the last one, these things take up to 15 minutes

one, these things take up to 15 minutes to complete. So, you're not going to be

to complete. So, you're not going to be able to publish your report just yet.

able to publish your report just yet. You can try. It's probably not going to

You can try. It's probably not going to work. Didn't work for me. It took about

work. Didn't work for me. It took about 15 minutes. Anyway, let's assume 15

15 minutes. Anyway, let's assume 15 minutes passed. Okay. So, here I'm on

minutes passed. Okay. So, here I'm on the data jobs dashboard. Going to go to

the data jobs dashboard. Going to go to file, embed report, and publish to web.

file, embed report, and publish to web. It's going to ask me or ask it's going

It's going to ask me or ask it's going to basically say, "Hey, this link's

to basically say, "Hey, this link's going to include on a public website."

going to include on a public website." That's okay. Yep. I'm going to click

That's okay. Yep. I'm going to click continue. And it's going to make sure

continue. And it's going to make sure that hey, make sure you're not

that hey, make sure you're not publishing any confidential proprietary

publishing any confidential proprietary pro proprietary information. So if you

pro proprietary information. So if you have any confidential data, this is not

have any confidential data, this is not confidential. You can share it, but

confidential. You can share it, but especially if you're dealing with work

especially if you're dealing with work type of data, you don't want to be doing

type of data, you don't want to be doing this. This is specifically for open free

this. This is specifically for open free data. And now with this, I have two

data. And now with this, I have two different links like we talked about

different links like we talked about before. I can just copy that link and

before. I can just copy that link and then even going into this browser right

then even going into this browser right here, I can see that whenever I enter

here, I can see that whenever I enter the link in, it's live on the web, free

the link in, it's live on the web, free for anybody to access. So that's an

for anybody to access. So that's an overview of PowerBI service and how you

overview of PowerBI service and how you can go about sharing something like this

can go about sharing something like this dashboard. We do now have a quiz for the

dashboard. We do now have a quiz for the practice problems to go through and test

practice problems to go through and test your knowledge and make sure you

your knowledge and make sure you understand how to use the PowerBI

understand how to use the PowerBI service. Now, in the next chapter, we're

service. Now, in the next chapter, we're going to be jumping into building

going to be jumping into building visualizations. We're going to be

visualizations. We're going to be tackling all the different ones. super

tackling all the different ones. super excited about that. With that, I'll see

excited about that. With that, I'll see you there.

Welcome to chapter 2. We're going to be covering visualizations. We're going to

covering visualizations. We're going to be walking through every single type

be walking through every single type that you're actually making here and

that you're actually making here and which ones are actually useful. Now, in

which ones are actually useful. Now, in this lesson here, we're going over

this lesson here, we're going over column and bar charts. But before that,

column and bar charts. But before that, we really need to understand what we're

we really need to understand what we're going to be covering for this entire

going to be covering for this entire chapter. So, let's jump into my

chapter. So, let's jump into my computer.

So let's dive into the PowerBI report that we're going to be using for this

that we're going to be using for this chapter here in the second folder for

chapter here in the second folder for visualizations. We just have a single

visualizations. We just have a single file for this. When you open this up,

file for this. When you open this up, you should be navigated first to this

you should be navigated first to this homepage. And these are actually

homepage. And these are actually different buttons that you can use to

different buttons that you can use to navigate to the different buttons. You

navigate to the different buttons. You just have to press control and then

just have to press control and then click and then you can navigate to

click and then you can navigate to anything. And then if I want to go back

anything. And then if I want to go back to that home menu, I have this

to that home menu, I have this convenient home menu icon up here. Once

convenient home menu icon up here. Once again, I have to press control and then

again, I have to press control and then click and it navigates me back. We'll be

click and it navigates me back. We'll be covering buttons in the last lesson of

covering buttons in the last lesson of the chapter, but let's look what we're

the chapter, but let's look what we're going to cover now. In this lesson,

going to cover now. In this lesson, we're going to be covering column and

we're going to be covering column and bar charts. We're going to go over four

bar charts. We're going to go over four different examples and distinguish

different examples and distinguish between what is a bar chart and what is

between what is a bar chart and what is a column chart. Second lesson is going

a column chart. Second lesson is going to use time series data in order to make

to use time series data in order to make line charts and also area charts. The

line charts and also area charts. The third lesson is going to go into common

third lesson is going to go into common charts that I find myself using beyond

charts that I find myself using beyond those other ones that we just covered in

those other ones that we just covered in lesson one and two, specifically pie

lesson one and two, specifically pie charts, donut charts, scatter plots, and

charts, donut charts, scatter plots, and tree maps. Lesson four, we'll get into

tree maps. Lesson four, we'll get into maps. And PowerBI has three different

maps. And PowerBI has three different options that you can choose through for

options that you can choose through for this. And then lesson five, we'll get

this. And then lesson five, we'll get into some uncommon charts. Mainly, I'm

into some uncommon charts. Mainly, I'm just going to show these in order for

just going to show these in order for you to understand what charts you

you to understand what charts you probably shouldn't be using most of the

probably shouldn't be using most of the time, but have familiarity with it.

time, but have familiarity with it. Lesson six, we'll get into building not

Lesson six, we'll get into building not only tables, but also matrices. Matrices

only tables, but also matrices. Matrices allow us to actually dive into the data

allow us to actually dive into the data a little bit more uh in depth. Anyway,

a little bit more uh in depth. Anyway, we'll also be covering with this

we'll also be covering with this conditional formatting. So, we'll just

conditional formatting. So, we'll just do things like do these color icons or

do things like do these color icons or color bars. Lesson seven will be on

color bars. Lesson seven will be on cards because everybody loves a good

cards because everybody loves a good card that tells a good data point. And

card that tells a good data point. And then lesson eight will be on slicers

then lesson eight will be on slicers because we don't always want to use just

because we don't always want to use just filters alone to actually filter down

filters alone to actually filter down our data. And then like I mentioned,

our data. And then like I mentioned, lesson 9 will be buttons where we'll

lesson 9 will be buttons where we'll actually build this out here and

actually build this out here and understanding how buttons work and also

understanding how buttons work and also bookmarks. Now, this report also

bookmarks. Now, this report also includes the two pages for our

includes the two pages for our dashboard. This is our first official

dashboard. This is our first official project dashboard that we're going to be

project dashboard that we're going to be building and it's on our data set and it

building and it's on our data set and it allows us to actually dive into if I

allows us to actually dive into if I want to dive into data engineers can

want to dive into data engineers can filter down for it and then it provides

filter down for it and then it provides a drill through. If I click this to get

a drill through. If I click this to get more in-depth data points for this

more in-depth data points for this particular topic of data engineer want

particular topic of data engineer want to navigate back I just click this back

to navigate back I just click this back arrow right here holding control

arrow right here holding control navigates me back to the dashboard.

navigates me back to the dashboard. We'll get to the dashboard when we get

We'll get to the dashboard when we get to the project session, but I wanted to

to the project session, but I wanted to warn you that's in this report because

warn you that's in this report because we're going to be using a lot of the

we're going to be using a lot of the visualizations that we build throughout

visualizations that we build throughout this chapter in this dashboard.

this chapter in this dashboard. Basically, we're not going to be wasting

Basically, we're not going to be wasting any of our work. Now, one quick note on

any of our work. Now, one quick note on the purpose of this chapter. This is not

the purpose of this chapter. This is not meant for us to go through and you to be

meant for us to go through and you to be a basic technical nerd about how to

a basic technical nerd about how to build each of these visuals, although we

build each of these visuals, although we will cover that. I feel the more

will cover that. I feel the more important part is understanding when you

important part is understanding when you should apply each of these visuals given

should apply each of these visuals given a certain problem you need to tackle. So

a certain problem you need to tackle. So yes, pay attention how they're built,

yes, pay attention how they're built, but more importantly, pay attention to

but more importantly, pay attention to when they are actually used.

So let's get into our first of four visualizations we're going to be

visualizations we're going to be building for this. And in this one, we

building for this. And in this one, we need to understand what the difference

need to understand what the difference is between a column and bar chart. For

is between a column and bar chart. For this, we're going to be asking this

this, we're going to be asking this question. What is the highest paying job

question. What is the highest paying job in data? And for this we only want to

in data? And for this we only want to look at remember our data set contains

look at remember our data set contains 10 distinct job titles. We just want to

10 distinct job titles. We just want to limit it to these uh six of basically

limit it to these uh six of basically data analysts, scientists, engineers,

data analysts, scientists, engineers, and also their senior roles. So for you,

and also their senior roles. So for you, let's start out in a brand new PowerBI

let's start out in a brand new PowerBI file. I'll select blank report. Peeking

file. I'll select blank report. Peeking in the data pane. There's no data in

in the data pane. There's no data in here. So let's import in our data set.

here. So let's import in our data set. Remember, it's a text CSV file. It's

Remember, it's a text CSV file. It's inside of our data folder and it's that

inside of our data folder and it's that job postings flat CSV. Everything's

job postings flat CSV. Everything's looking good with this navigator pop-up

looking good with this navigator pop-up that pops up and we'll load it in. Data

that pops up and we'll load it in. Data set completed, loaded in. Everything's

set completed, loaded in. Everything's looking well underneath this data pane.

looking well underneath this data pane. But like always, I want to go into table

But like always, I want to go into table view and just go through and make sure

view and just go through and make sure that everything is formatted correctly.

that everything is formatted correctly. Specifically, if you remember from last

Specifically, if you remember from last time, we had that salary year average.

time, we had that salary year average. If I sort it to sending, it's only right

If I sort it to sending, it's only right now a whole number. I'm going to change

now a whole number. I'm going to change this to a currency. And for the decimal

this to a currency. And for the decimal places, I'm going to change this to

places, I'm going to change this to zero. We're also going to update the

zero. We're also going to update the salary hour average to a currency as

salary hour average to a currency as well. And for this, we'll give it two

well. And for this, we'll give it two decimal places. As always with every

decimal places. As always with every file, we need to make sure that we're

file, we need to make sure that we're saving it often. So, I'm just going to

saving it often. So, I'm just going to save it here on my desktop. So, let's

save it here on my desktop. So, let's actually get into building this

actually get into building this visualization on our canvas. I'm going

visualization on our canvas. I'm going to go ahead and select stacked bar chart

to go ahead and select stacked bar chart to add it in. I'm going to resize it to

to add it in. I'm going to resize it to take up the top quarter. For this, we

take up the top quarter. For this, we want the job titles along the Y ais and

want the job titles along the Y ais and the count of them along along the X-

the count of them along along the X- axis. We can use both these fields for

axis. We can use both these fields for this. So, I'm going to drag job titles

this. So, I'm going to drag job titles short into both of these. From here, I'm

short into both of these. From here, I'm going to go into focus mode so we can

going to go into focus mode so we can drill into it closer. All right. Right.

drill into it closer. All right. Right. So, this is a bar chart. Bar charts go

So, this is a bar chart. Bar charts go horizontally. Let's compare this to a

horizontally. Let's compare this to a column chart. We'll use a the same

column chart. We'll use a the same format. Specifically, we'll use that

format. Specifically, we'll use that stacked column chart. And the column

stacked column chart. And the column charts go up and down. The way I

charts go up and down. The way I remember this is pretty easy. Columns

remember this is pretty easy. Columns like that of a building go up and down.

like that of a building go up and down. And the column chart does the same

And the column chart does the same thing, but we're building a bar chart

thing, but we're building a bar chart for this. So, we're going to change this

for this. So, we're going to change this back to a stacked bar chart. And

back to a stacked bar chart. And navigating back to our what our final

navigating back to our what our final visualization look like. I realize I

visualization look like. I realize I made a grave mistake. I didn't read this

made a grave mistake. I didn't read this fully. We're trying to plot or make a

fully. We're trying to plot or make a visualization of what is the highest

visualization of what is the highest paying job in data. We don't need to be

paying job in data. We don't need to be doing a count of jobs. We need to be

doing a count of jobs. We need to be doing the median salary of jobs. So

doing the median salary of jobs. So let's actually update this visual. I'm

let's actually update this visual. I'm going to take that salary year average

going to take that salary year average column and I'm going to drag it into the

column and I'm going to drag it into the xaxis. Right now we have a sum of the

xaxis. Right now we have a sum of the salary year average. We want to actually

salary year average. We want to actually change that to a median. And right now

change that to a median. And right now it's a right stack bar chart. So that we

it's a right stack bar chart. So that we have multiple different values going

have multiple different values going here. We don't want that count of job

here. We don't want that count of job tiles short. So I'm going to go ahead

tiles short. So I'm going to go ahead and click that to exit out. All right.

and click that to exit out. All right. Navigating back to the canvas area

Navigating back to the canvas area itself. I want to put a title first

itself. I want to put a title first because that influences my decision on

because that influences my decision on how I'm going to format the rest of the

how I'm going to format the rest of the chart. If we want to format the visuals

chart. If we want to format the visuals title, remember we can't we have to be

title, remember we can't we have to be selected on the visual. If I were to

selected on the visual. If I were to click this, it's not going to give us

click this, it's not going to give us what we want. So, actually select the

what we want. So, actually select the visual, select format your visual. And

visual, select format your visual. And then underneath the general selection,

then underneath the general selection, that's where the title is. We're going

that's where the title is. We're going to change this to what is the highest

to change this to what is the highest paying job in data. I'm going to give

paying job in data. I'm going to give this a size 20 point font. and also

this a size 20 point font. and also we're going to center it. I like to

we're going to center it. I like to typically ask a question with my charts

typically ask a question with my charts to guide the user or the end user on

to guide the user or the end user on what they should be looking for in the

what they should be looking for in the visual. Going back into focus mode,

visual. Going back into focus mode, understanding what this title is, we can

understanding what this title is, we can see that this is clearly job titles. So,

see that this is clearly job titles. So, I don't need a y-axis label. So, under

I don't need a y-axis label. So, under format your visual under the visual

format your visual under the visual section, I can go into y-axis. And we

section, I can go into y-axis. And we don't want to toggle on or off the

don't want to toggle on or off the values, but instead we want to toggle

values, but instead we want to toggle off the uh the title itself. Next, let's

off the uh the title itself. Next, let's format the Xaxis label. We could do that

format the Xaxis label. We could do that here underneath the title section. You

here underneath the title section. You can update it right here. It's auto

can update it right here. It's auto right now. I don't recommend doing it

right now. I don't recommend doing it here. Instead, we're going to double

here. Instead, we're going to double click the field well. And then we'll

click the field well. And then we'll replace this value here with the value

replace this value here with the value of median yearly salary. And then

of median yearly salary. And then typically, I like to provide what are

typically, I like to provide what are the units of currency. In this case,

the units of currency. In this case, it's USD. So, not bad. If I scroll over

it's USD. So, not bad. If I scroll over this visual, we can see that data

this visual, we can see that data scientists are getting paid $155,000.

scientists are getting paid $155,000. Notice that that it says job title short

Notice that that it says job title short in front of that. Because of that, I'm

in front of that. Because of that, I'm going to also update this y-axis right

going to also update this y-axis right here to say job title. So now whenever I

here to say job title. So now whenever I scroll over the tool tip, it looks a lot

scroll over the tool tip, it looks a lot cleaner. So let's get into filtering

cleaner. So let's get into filtering these values down. Remember, we want

these values down. Remember, we want data analyst, data scientist, data

data analyst, data scientist, data engineers, and also their senior roles.

engineers, and also their senior roles. The common thing about them all is they

The common thing about them all is they contain the word data. So, opening up

contain the word data. So, opening up the filters pane and underneath filters

the filters pane and underneath filters on this visual, we want to filter the

on this visual, we want to filter the job title. Now, I could go through and

job title. Now, I could go through and select those six, but I'm lazy, so I'm

select those six, but I'm lazy, so I'm actually going to go into advanced

actually going to go into advanced filtering. And it says, hey, we can show

filtering. And it says, hey, we can show the items when the value contains, in

the items when the value contains, in our case, we want it to contain the word

our case, we want it to contain the word data. Apply the filter. Bam, we get

data. Apply the filter. Bam, we get those six roles. All right, so now we

those six roles. All right, so now we can actually sit back and analyze it. We

can actually sit back and analyze it. We see that senior roles are typically paid

see that senior roles are typically paid higher except in the case of senior data

higher except in the case of senior data analyst. Little questionable there. I

analyst. Little questionable there. I don't know what's going on, but overall

don't know what's going on, but overall all these median salaries are where I

all these median salaries are where I expect. Also, quick note, we're going to

expect. Also, quick note, we're going to be doing or focusing on median values

be doing or focusing on median values throughout this entire course over

throughout this entire course over something like average. If I go to that

something like average. If I go to that table view and sort so uh salary year

table view and sort so uh salary year average in descending order, you can see

average in descending order, you can see we have a lot of high values here. In

we have a lot of high values here. In this case, we have one job that has

this case, we have one job that has $920,000

$920,000 as a salary. If we were to use average,

as a salary. If we were to use average, it's going to distort this value that

it's going to distort this value that we're going to seeing. It's going to

we're going to seeing. It's going to make it much higher than what we'd

make it much higher than what we'd expect. That's why we're using median

expect. That's why we're using median because it more or less normalizes what

because it more or less normalizes what we should see for the salary. And as

we should see for the salary. And as proof of this, I can just show you

proof of this, I can just show you senior data scientists are at 155,000

senior data scientists are at 155,000 for their median salary. If I were to

for their median salary. If I were to change this to average, they go up to

change this to average, they go up to 155,900.

155,900. And I don't know if you noticed, but all

And I don't know if you noticed, but all the other ones also increased. So, it's

the other ones also increased. So, it's really unrealistic for us to display

really unrealistic for us to display these average values. That's why we're

these average values. That's why we're going to do median because that's more

going to do median because that's more realistic of what you would expect to

realistic of what you would expect to see if you were applying for these jobs.

Next question to get into relates on these lines and that is what is the

these lines and that is what is the highest paying job globally. So similar

highest paying job globally. So similar before we're going to be looking at

before we're going to be looking at those same six job titles but for this

those same six job titles but for this we're going to be looking at the top

we're going to be looking at the top four countries or the four countries

four countries or the four countries that have the most amount of jobs. So

that have the most amount of jobs. So back in our canvas I don't like starting

back in our canvas I don't like starting from scratch if I don't need to. So I'm

from scratch if I don't need to. So I'm actually going to copy this visual by

actually going to copy this visual by pressing Ctrl + C. Make sure that it's

pressing Ctrl + C. Make sure that it's actually selected and then press Ctrl +V

actually selected and then press Ctrl +V to copy it down and paste it. I'm going

to copy it down and paste it. I'm going paste it underneath. I'm going to update

paste it underneath. I'm going to update the title to what is the highest paying

the title to what is the highest paying job globally so we don't have a repeat

job globally so we don't have a repeat of last time me doing the aggregation in

of last time me doing the aggregation in the wrong column. So with this, let's

the wrong column. So with this, let's change this into the correct chart type

change this into the correct chart type that we want to use. We want to use this

that we want to use. We want to use this clustered column chart. And if you

clustered column chart. And if you notice, it went through and actually

notice, it went through and actually swapped that x-axis and y-axis to make

swapped that x-axis and y-axis to make sure the values right. Unfortunately, it

sure the values right. Unfortunately, it didn't keep our label. So, I'm going to

didn't keep our label. So, I'm going to go ahead and update that. All right. So,

go ahead and update that. All right. So, remember we want this with the country

remember we want this with the country along the x-axis and basically have the

along the x-axis and basically have the different job titles aggregated in

different job titles aggregated in between it. So, navigating to our visual

between it. So, navigating to our visual going into focus mode. I'm going to take

going into focus mode. I'm going to take job country and let's just drag it into

job country and let's just drag it into the x-axis. Now, if you go through this,

the x-axis. Now, if you go through this, we can actually see that it combines

we can actually see that it combines every job title and also job country.

every job title and also job country. So, in this case, this is in Armenia.

So, in this case, this is in Armenia. This is for data analysts and this is

This is for data analysts and this is their median salary. This is basically

their median salary. This is basically highly unreadable and highly unusable.

highly unreadable and highly unusable. What we actually want to do is we want

What we actually want to do is we want the job we want to keep job country on

the job we want to keep job country on that x-axis, but we're going to take the

that x-axis, but we're going to take the job title and we're going to drag it on

job title and we're going to drag it on down. Specifically, I want to take this

down. Specifically, I want to take this down into legend. And bam, there's what

down into legend. And bam, there's what we want. Although also highly unreadable

we want. Although also highly unreadable because we have close almost 200

because we have close almost 200 countries. There's too many countries on

countries. There's too many countries on here to actually use. So for this

here to actually use. So for this visual, we need to apply a filter on it

visual, we need to apply a filter on it based on so filter on this visual in the

based on so filter on this visual in the job country column. Now we could filter

job country column. Now we could filter I could scroll through this and see the

I could scroll through this and see the different counts and select the ones

different counts and select the ones that I want. But you know I'm lazy. I

that I want. But you know I'm lazy. I like to automate it. So we're going to

like to automate it. So we're going to instead use the filter type and we're

instead use the filter type and we're going to change this now to use top N.

going to change this now to use top N. Specifically want the top four

Specifically want the top four countries. But what do we want the top

countries. But what do we want the top four countries based on? Well, we want

four countries based on? Well, we want them based on the count of those

them based on the count of those countries. So, I'll change this to count

countries. So, I'll change this to count from first and then I'll click apply

from first and then I'll click apply filter. And there we have it. United

filter. And there we have it. United Kingdom, United States, France, and also

Kingdom, United States, France, and also India in there. Now, there's a lot of

India in there. Now, there's a lot of different data points going on in here.

different data points going on in here. And I'm noticing right now too with it

And I'm noticing right now too with it that this x-axis I need to update it to

that this x-axis I need to update it to just country so that way it's more

just country so that way it's more readable. But there's a lot of different

readable. But there's a lot of different data points in here to actually view. If

data points in here to actually view. If for some reason you wanted to get those

for some reason you wanted to get those data points or maybe somebody else did,

data points or maybe somebody else did, you click the three dots up the top

you click the three dots up the top right hand corner and then you could go

right hand corner and then you could go here to show as table and then you have

here to show as table and then you have all these different values here and you

all these different values here and you can actually see them more visually here

can actually see them more visually here if you wanted to. Also, you could just

if you wanted to. Also, you could just export the data as well and it's going

export the data as well and it's going to exported it out. All right, so let's

to exported it out. All right, so let's go back to our report. I want to clean

go back to our report. I want to clean up one thing real quick. If we look at

up one thing real quick. If we look at that filters tab, remember we have job

that filters tab, remember we have job titles contains data right here on this

titles contains data right here on this visual and also right here on this

visual and also right here on this visual. I actually want to apply it to

visual. I actually want to apply it to the entire page. So what I'm going to do

the entire page. So what I'm going to do is I'm going to just take this and I'm

is I'm going to just take this and I'm going to drag it into filters on this

going to drag it into filters on this page. And as you notice, it's not

page. And as you notice, it's not actually working. So instead, what I'm

actually working. So instead, what I'm going to do is I'm going to drag job

going to do is I'm going to drag job tile short onto here. Do that advanced

tile short onto here. Do that advanced filtering for those jobs that contain

filtering for those jobs that contain data. and then click apply filter. Now

data. and then click apply filter. Now for each one of these uh visuals, we

for each one of these uh visuals, we don't need to maintain it on here long.

don't need to maintain it on here long. It's just sort of redundant. I'll remove

It's just sort of redundant. I'll remove it on this visual. Selecting this

it on this visual. Selecting this visual, I'll also remove it by se

visual, I'll also remove it by se selecting clear filter. So now it's

selecting clear filter. So now it's removed on both of these, but it's

removed on both of these, but it's applied when I click the page. It's

applied when I click the page. It's applied on the page. Also, sometimes

applied on the page. Also, sometimes whenever you just click onto here, it's

whenever you just click onto here, it's going to generate these other visuals.

going to generate these other visuals. It's sort of annoying. Anytime you need

It's sort of annoying. Anytime you need to remove them, you just click those

to remove them, you just click those ellipses and click remove. All right.

ellipses and click remove. All right. So, this, as a reminder, is a clustered

So, this, as a reminder, is a clustered column or if I wanted to, I could change

column or if I wanted to, I could change it to a clustered bar chart.

All right. Next up is a stacked column, or if you will, stacked bar chart. We're

or if you will, stacked bar chart. We're going to make it into a stack column

going to make it into a stack column chart. With this visualization, we want

chart. With this visualization, we want to see not only what are the counts of

to see not only what are the counts of the different job titles, but we want to

the different job titles, but we want to see the breakdown of whether they

see the breakdown of whether they mention a degree requirement in the job

mention a degree requirement in the job posting. If we go into our table view,

posting. If we go into our table view, we have this column here on job no

we have this column here on job no degree mention. It's a true or false

degree mention. It's a true or false value. If it's true, there's no mention

value. If it's true, there's no mention of a degree requirement in the job

of a degree requirement in the job posting. Doesn't mean that doesn't

posting. Doesn't mean that doesn't require a degree. it just means that

require a degree. it just means that they don't mention it. So, in the case

they don't mention it. So, in the case of it being false, there is a degree

of it being false, there is a degree requirement mentioned in the job

requirement mentioned in the job posting. Anyway, let's actually

posting. Anyway, let's actually visualize this for those top six jobs. I

visualize this for those top six jobs. I don't like starting from scratch, so I'm

don't like starting from scratch, so I'm going to copy this first visual, press

going to copy this first visual, press commandV, drag it over to the top right

commandV, drag it over to the top right hand corner. Personally, I like whenever

hand corner. Personally, I like whenever we have these labels here uh going into

we have these labels here uh going into f mode written in this manner and so

f mode written in this manner and so keeping it as a bar chart. But like I

keeping it as a bar chart. But like I said, we're going to be using a stacked

said, we're going to be using a stacked column chart for this instead. Right

column chart for this instead. Right now, we're doing the median salary, but

now, we're doing the median salary, but we need a count of the job titles. So,

we need a count of the job titles. So, I'll drag the job title short into that

I'll drag the job title short into that y-axis. Click that median off here. Now,

y-axis. Click that median off here. Now, we have the count, but we want to see

we have the count, but we want to see the breakdown of job no degree mention.

the breakdown of job no degree mention. So what we can do with this is throw

So what we can do with this is throw this into the legend. So taking job no

this into the legend. So taking job no degree mention put it there. And now

degree mention put it there. And now these values are stacked. We're going to

these values are stacked. We're going to do some clean up of the columns.

do some clean up of the columns. Changing y-axis to job title. Changing

Changing y-axis to job title. Changing the legend to no degree mentioned. And

the legend to no degree mentioned. And then lastly changing that title. So

then lastly changing that title. So under format your visual under general

under format your visual under general under title we change it to what are the

under title we change it to what are the top jobs with no degree mentioned. And

top jobs with no degree mentioned. And looking at it, data engineers by far

looking at it, data engineers by far have some of the the highest amounts of

have some of the the highest amounts of jobs that have no degree mentioned in

jobs that have no degree mentioned in the job posting, but data analysts

the job posting, but data analysts aren't far behind.

Last visual to make is a 100% stacked column or bar chart. In this case, we're

column or bar chart. In this case, we're looking at obviously a bar chart. Now,

looking at obviously a bar chart. Now, this is great anytime you want to

this is great anytime you want to visualize proportions like in our last

visualize proportions like in our last case. Yeah, it's great that we can see

case. Yeah, it's great that we can see what is the overall quantity values, but

what is the overall quantity values, but say we are stuck in something like

say we are stuck in something like senior data engineers, senior data

senior data engineers, senior data scientists, we may want to better

scientists, we may want to better understand what are the proportions of

understand what are the proportions of jobs that this is likely to happen. In

jobs that this is likely to happen. In that case, we could build a

that case, we could build a visualization like this that shows what

visualization like this that shows what portions of jobs mention a degree. In

portions of jobs mention a degree. In this case, we could use something like a

this case, we could use something like a 100% stacked bar column chart to

100% stacked bar column chart to visualize this proportion to see what

visualize this proportion to see what portion of JSP should agree. So, let's

portion of JSP should agree. So, let's build this bad boy. So, a lot of the

build this bad boy. So, a lot of the stuff is going to be using this visual

stuff is going to be using this visual right here. I'm going to go ahead and

right here. I'm going to go ahead and copy it and paste it. And then come up

copy it and paste it. And then come up here and change this into a 100% stacked

here and change this into a 100% stacked bar chart. Moving this into focus mode.

bar chart. Moving this into focus mode. We pretty much have this completely

We pretty much have this completely built. We just got to update a few

built. We just got to update a few titles and update it to what portion of

titles and update it to what portion of top jobs have no degree mentioned. And

top jobs have no degree mentioned. And the only other thing to clean up on this

the only other thing to clean up on this is the actual x-axis title. I'll do that

is the actual x-axis title. I'll do that from here. And we'll change this to job

from here. And we'll change this to job count. So with this, although we did see

count. So with this, although we did see from last time that data analysts and

from last time that data analysts and data engineers had some of the highest

data engineers had some of the highest quantities, when we actually look at the

quantities, when we actually look at the 100% stack view, we can see that things

100% stack view, we can see that things like senior data engineers and data

like senior data engineers and data engineers are both pretty much it's like

engineers are both pretty much it's like half the postings don't have a

half the postings don't have a requirement or don't mention a

requirement or don't mention a requirement of a degree. And data

requirement of a degree. And data analysts correlate as well. They're

analysts correlate as well. They're around 40%. and then sat uh data

around 40%. and then sat uh data scientists apparently pretty stingy.

scientists apparently pretty stingy. Seven almost 7% on both have a no

Seven almost 7% on both have a no mention of a degree whereas like 93

mention of a degree whereas like 93 require some sort of degree. So if you

require some sort of degree. So if you don't have a degree, if you're not

don't have a degree, if you're not focused on data analyst jobs, you should

focused on data analyst jobs, you should also be focusing on data engineer jobs.

also be focusing on data engineer jobs. All right, so boom, that is the

All right, so boom, that is the different column and bar charts. They're

different column and bar charts. They're all along the top line here. The last

all along the top line here. The last thing I'm going to do on this is just

thing I'm going to do on this is just update this page title to be called

update this page title to be called column and bar and then make sure you

column and bar and then make sure you save it. We now have some practice

save it. We now have some practice problems for you to go through and get

problems for you to go through and get more familiar with when you should be

more familiar with when you should be using bar and also column charts and

using bar and also column charts and these different aggregation or

these different aggregation or variations of them each. In the next

variations of them each. In the next lesson, we're going to be going into

lesson, we're going to be going into line and area charts. With that, I'll

line and area charts. With that, I'll see you there.

Welcome to this lesson on line and area charts. And after things like bar and

charts. And after things like bar and column charts, which we covered in the

column charts, which we covered in the previous lesson, this is the second most

previous lesson, this is the second most common type of charts that I find myself

common type of charts that I find myself using. Let's jump into the final report

using. Let's jump into the final report to see what we're going to be building

to see what we're going to be building in this lesson. First, we're going to

in this lesson. First, we're going to start simple, building a simple line

start simple, building a simple line chart, understanding what is the trend

chart, understanding what is the trend of jobs in 2024. Remember, this data set

of jobs in 2024. Remember, this data set that we're working with only includes

that we're working with only includes jobs from 2024. Next, we'll transition

jobs from 2024. Next, we'll transition this over into an area chart, which if

this over into an area chart, which if you see, it has a similar trend that our

you see, it has a similar trend that our line chart did, but in this case, we're

line chart did, but in this case, we're able to now see what are the different

able to now see what are the different jobs that compromise or compose those

jobs that compromise or compose those different job counts. And anytime you

different job counts. And anytime you have any type of stacked area chart, you

have any type of stacked area chart, you have probably some sort of 100% stacked

have probably some sort of 100% stacked area chart. Finally, we'll wrap it up

area chart. Finally, we'll wrap it up with this visualization looking how we

with this visualization looking how we can combine column charts with also line

can combine column charts with also line charts. In this case, we're going to be

charts. In this case, we're going to be comparing what is the yearly median

comparing what is the yearly median salary compared to hourly median salary

salary compared to hourly median salary of those top 10 jobs.

So, let's get into building this bad boy of understanding what is the trend of

of understanding what is the trend of jobs in 2024. In the PowerB report we've

jobs in 2024. In the PowerB report we've been working on, I'm going to create a

been working on, I'm going to create a new page and I'm going to change the

new page and I'm going to change the title of this to line and area. Clicking

title of this to line and area. Clicking inside the canvas, I'm going to insert

inside the canvas, I'm going to insert in a line chart. We'll drag this into

in a line chart. We'll drag this into the top quadrant. So along the bottom

the top quadrant. So along the bottom along the x-axis, we want to use the job

along the x-axis, we want to use the job posted date column. We're going to dive

posted date column. We're going to dive into this a little bit more. Right now,

into this a little bit more. Right now, nothing's appearing. We need to put

nothing's appearing. We need to put something into the yaxis. Specifically,

something into the yaxis. Specifically, want the counts of jobs. Remember, we're

want the counts of jobs. Remember, we're going to just use that job title short

going to just use that job title short column because we don't have necessarily

column because we don't have necessarily a job ID column. This is going to be

a job ID column. This is going to be good enough. Okay. We can see from our

good enough. Okay. We can see from our visualization right now that if I hover

visualization right now that if I hover over it, it's only showing one data

over it, it's only showing one data point and it's a dot. It's not even a

point and it's a dot. It's not even a line. because it's only showing this for

line. because it's only showing this for the year of 2024. If I wanted to, I can

the year of 2024. If I wanted to, I can navigate down. We're going to dive in

navigate down. We're going to dive in more into drill downs right after this,

more into drill downs right after this, but mainly I just show you that we will

but mainly I just show you that we will be able to navigate into all the

be able to navigate into all the different job titles depending on what

different job titles depending on what we want. The main thing to understand is

we want. The main thing to understand is that for this x-axis, I'm going to

that for this x-axis, I'm going to actually close it out. Remember when we

actually close it out. Remember when we drag this job posted date over, it put

drag this job posted date over, it put this date hierarchy which is over here

this date hierarchy which is over here in the column. So I can actually open

in the column. So I can actually open this up and similarly it has year

this up and similarly it has year quarter month day. Here I have year

quarter month day. Here I have year quarter month day. For the time being

quarter month day. For the time being all we're going to do before we get into

all we're going to do before we get into covering that this drill down

covering that this drill down functionality that's highly complex I

functionality that's highly complex I feel. We're going to just remove these

feel. We're going to just remove these other fields of year, quarter, and then

other fields of year, quarter, and then also day. And we're just going to keep

also day. And we're just going to keep the month for right now. I'm going to

the month for right now. I'm going to open it up into focus mode. We're going

open it up into focus mode. We're going to build out this line chart to make

to build out this line chart to make sure that it has everything right and

sure that it has everything right and correct in it. And then we're going to

correct in it. And then we're going to jump into drill down using those arrows

jump into drill down using those arrows to navigate up and down in this. First

to navigate up and down in this. First thing I'm going to change is the title.

thing I'm going to change is the title. Going to format your visual under

Going to format your visual under general to title. I only have jobs in

general to title. I only have jobs in 2024. So we'll call this what is trend

2024. So we'll call this what is trend of jobs in 2024. The x-axis label is a

of jobs in 2024. The x-axis label is a little redundant because we're already

little redundant because we're already saying that hey we're looking at dates.

saying that hey we're looking at dates. So I'm going to turn off the title. Then

So I'm going to turn off the title. Then for the yaxis label I'm going to change

for the yaxis label I'm going to change this to instead be something more

this to instead be something more readable of job count. All right. So

readable of job count. All right. So this is looking good. We have everything

this is looking good. We have everything formatted as we wanted. If we remember,

formatted as we wanted. If we remember, we had a trend line previously. How do

we had a trend line previously. How do we add something like a trend line?

we add something like a trend line? Well, previously we've looked at this

Well, previously we've looked at this build visual. We've looked at this

build visual. We've looked at this format visual. And now we're going to

format visual. And now we're going to look at this analytics underneath the

look at this analytics underneath the visualization pane. Now, this is really

visualization pane. Now, this is really great anytime you want to add any kind

great anytime you want to add any kind of reference lines in here. such as if I

of reference lines in here. such as if I wanted a minimum line, I could come in

wanted a minimum line, I could come in here under minline, select add line, and

here under minline, select add line, and it adds this line in. Scrolling on down,

it adds this line in. Scrolling on down, I can even go into and turn on the data

I can even go into and turn on the data label. It's positioned on the left hand

label. It's positioned on the left hand side. It's above it. And what we want to

side. It's above it. And what we want to show, you could do data value name, or

show, you could do data value name, or in my case, I'd probably like something

in my case, I'd probably like something like min. Not too bad. I would dress it

like min. Not too bad. I would dress it up a little bit. I don't really like the

up a little bit. I don't really like the name of min one, so I'm going to edit it

name of min one, so I'm going to edit it and change that to minimum job count.

and change that to minimum job count. Now, I could also do the same and add an

Now, I could also do the same and add an average line as I've done here and also

average line as I've done here and also change the name. The formatting isn't

change the name. The formatting isn't necessarily correct. So, I could change

necessarily correct. So, I could change the value decimal places to just do zero

the value decimal places to just do zero and much more readable. But if we jump

and much more readable. But if we jump forward to what we're going to be

forward to what we're going to be building finally, I had on here a trend

building finally, I had on here a trend line yet it's not visible. If we

line yet it's not visible. If we actually go through our navigation menu,

actually go through our navigation menu, specifically coming back here and

specifically coming back here and looking under at further analysis,

looking under at further analysis, there's nothing for trend line.

there's nothing for trend line. Unfortunately, whenever we just drag one

Unfortunately, whenever we just drag one of these column values over under date

of these column values over under date hierarchy doesn't and it doesn't no

hierarchy doesn't and it doesn't no longer provides the opportunity to

longer provides the opportunity to provide this trend line. So, we'll add

provide this trend line. So, we'll add it later. For the time being, I'm going

it later. For the time being, I'm going to remove this average line and then I'm

to remove this average line and then I'm also going to remove this min line. I

also going to remove this min line. I don't want to bother.

So, let's get into understanding drill down. I'm going to go ahead and we're

down. I'm going to go ahead and we're going to go ahead and add all these

going to go ahead and add all these column values back. Now you notice as I

column values back. Now you notice as I added all these back, one these arrows

added all these back, one these arrows appeared and then two if I actually go

appeared and then two if I actually go into this add further analysis trend

into this add further analysis trend line appears now and I can click on I

line appears now and I can click on I can actually get a trend line. I can

can actually get a trend line. I can also change the formatting here into a

also change the formatting here into a light blue color. Anyway, I digress.

light blue color. Anyway, I digress. Let's get back into drill down. So of

Let's get back into drill down. So of all these, the easiest one is drill up.

all these, the easiest one is drill up. Right now I'm down in day. I can just

Right now I'm down in day. I can just click up and navigate all the way back

click up and navigate all the way back up to that year. I'm also going to

up to that year. I'm also going to navigate back here. The first down hour

navigate back here. The first down hour is to click to turn on drill down mode.

is to click to turn on drill down mode. It's going to be highlighted black. And

It's going to be highlighted black. And in this case, I can click on points on

in this case, I can click on points on the chart and then drill down into it.

the chart and then drill down into it. In that case, I drilled into 2024. And

In that case, I drilled into 2024. And now, if I wanted to drill into quarter

now, if I wanted to drill into quarter 2, I can click that. Then, if I want to

2, I can click that. Then, if I want to drill into May, I could do that. If I

drill into May, I could do that. If I wanted to drill further into May 13th,

wanted to drill further into May 13th, can't do that because that's as far as

can't do that because that's as far as low down as we can go. If I wanted to go

low down as we can go. If I wanted to go back up, I just click back up. And at

back up, I just click back up. And at any point I can turn off this drill mode

any point I can turn off this drill mode by clicking on the drill mode and then

by clicking on the drill mode and then just navigating where I want to. The

just navigating where I want to. The next one is to go to the next level in

next one is to go to the next level in the hierarchy. So as expected this we're

the hierarchy. So as expected this we're going to click on it. We actually do

going to click on it. We actually do navigate down. In this case we're

navigate down. In this case we're getting quarter 1, quarter 2, quarter 3,

getting quarter 1, quarter 2, quarter 3, quarter 4. I'll click down again, get

quarter 4. I'll click down again, get the months. But I click down one more

the months. But I click down one more time and we see that it's a list of

time and we see that it's a list of numbers for month numbers. This double

numbers for month numbers. This double down arrow would be used in cases where

down arrow would be used in cases where you have multiple years and maybe you

you have multiple years and maybe you wanted to look at something like every

wanted to look at something like every single August or every single September.

single August or every single September. In our case, we only have one year of

In our case, we only have one year of 2024. And so that's why when we drill

2024. And so that's why when we drill all the way down to this, we're getting

all the way down to this, we're getting these following values, which

these following values, which technically this puts together for this

technically this puts together for this case, if we were looking at the second,

case, if we were looking at the second, this puts together January through

this puts together January through December, the counts on the 2nd. And

December, the counts on the 2nd. And then something like the 31st is super

then something like the 31st is super low at like 9,000 because 31st only

low at like 9,000 because 31st only happens one, two, three, like six times

happens one, two, three, like six times in a year. Actually, I think it's seven.

in a year. Actually, I think it's seven. Anyway, I bring that up because

Anyway, I bring that up because typically you actually don't want that.

typically you actually don't want that. Anytime I'm navigating to something like

Anytime I'm navigating to something like this, the down arrow that I want to use

this, the down arrow that I want to use is this expand all down one level in the

is this expand all down one level in the hierarchy. I'm going to go down and

hierarchy. I'm going to go down and notice with this one, it's actually has

notice with this one, it's actually has the year next to it. If we go back up,

the year next to it. If we go back up, go down. This one says just quarter 1,

go down. This one says just quarter 1, quarter 2. So, we know that we're

quarter 2. So, we know that we're navigating down in the hierarchy fully.

navigating down in the hierarchy fully. So we have each of these which is still

So we have each of these which is still quarterly and then we get into monthly

quarterly and then we get into monthly for that year and then we see

for that year and then we see everybody's dailies across the entire

everybody's dailies across the entire year. Our data has a lot of seasonality

year. Our data has a lot of seasonality to this. Specifically, if you were to

to this. Specifically, if you were to count each one of these ups and downs,

count each one of these ups and downs, you count 52 for 52 weeks in a year.

you count 52 for 52 weeks in a year. These low points are typically on the

These low points are typically on the weekend, usually Saturday and Sunday,

weekend, usually Saturday and Sunday, because that's when job postings are not

because that's when job postings are not getting uploaded because people aren't

getting uploaded because people aren't applying to jobs. Then the high points

applying to jobs. Then the high points are during the week. Anyway, this in my

are during the week. Anyway, this in my opinion is not very readable. So, I'm

opinion is not very readable. So, I'm going to drill up one level and we'll

going to drill up one level and we'll keep it in this monthly. So, one last

keep it in this monthly. So, one last note on this. I just went through over

note on this. I just went through over the last couple minutes explaining this

the last couple minutes explaining this drill down functionality to you cuz it's

drill down functionality to you cuz it's not that intuitive. Anytime you're

not that intuitive. Anytime you're building any type of report, it's good

building any type of report, it's good practice to put it and then save it on

practice to put it and then save it on what it is you want them to view because

what it is you want them to view because they're most likely not going to

they're most likely not going to understand how to do the drill down and

understand how to do the drill down and drill up unless you teach them how to

drill up unless you teach them how to use it.

Next visual we're going to get into building is a stacked area chart. Now,

building is a stacked area chart. Now, they do have an option for an area

they do have an option for an area chart, and if there's only a couple

chart, and if there's only a couple values, maybe okay with it. I'm

values, maybe okay with it. I'm typically always going to recommend

typically always going to recommend stacked area chart. Anyway, for this, we

stacked area chart. Anyway, for this, we want to see what is the trend of data

want to see what is the trend of data jobs. Basically, we're showing what we

jobs. Basically, we're showing what we had previously, but we're breaking it

had previously, but we're breaking it down further in the legend by job title.

down further in the legend by job title. All right, so for this, the easiest

All right, so for this, the easiest thing is let's just actually copyr C and

thing is let's just actually copyr C and Crl + V and build on this previous line

Crl + V and build on this previous line chart that we have. And I'm going to

chart that we have. And I'm going to just convert this into a stacked area

just convert this into a stacked area chart. I'm going to change the title

chart. I'm going to change the title real quick to make sure that we're

real quick to make sure that we're staying on task to what is the trend of

staying on task to what is the trend of data jobs in 2024. Remember, I like the

data jobs in 2024. Remember, I like the title a little bit bigger than this. I'm

title a little bit bigger than this. I'm going put a 20oint font. I'm also going

going put a 20oint font. I'm also going to center it. If I select this previous

to center it. If I select this previous visual, it still has this open so I can

visual, it still has this open so I can thus update the title as well for it.

thus update the title as well for it. Putting it as 20 point font in the

Putting it as 20 point font in the center. Anyway, back to this one.

center. Anyway, back to this one. Entering it into focus mode. Navigating

Entering it into focus mode. Navigating back to build visual. Remember, we want

back to build visual. Remember, we want to break this down by job titles. So,

to break this down by job titles. So, I'm going to drag that job title short

I'm going to drag that job title short into the legend. And then I'm going to

into the legend. And then I'm going to change that legend to job title just to

change that legend to job title just to show that point that we're trying to

show that point that we're trying to make. Right? This is a stacked area

make. Right? This is a stacked area chart. And this is going up to the

chart. And this is going up to the values of almost 55,000 here. If we were

values of almost 55,000 here. If we were to do just an area chart, it causes it

to do just an area chart, it causes it to overlap and then the values go down.

to overlap and then the values go down. This is just very

This is just very I'm just like I'm having a meltdown

I'm just like I'm having a meltdown right now. does not show anything useful

right now. does not show anything useful out of it. Stacked area chart already is

out of it. Stacked area chart already is a little hard to read. That makes it

a little hard to read. That makes it even harder to read. Similarly, with

even harder to read. Similarly, with this one, we can drill down in the

this one, we can drill down in the hierarchy. If I wanted to go to a daily

hierarchy. If I wanted to go to a daily basis, oh my gosh, I'm going need to

basis, oh my gosh, I'm going need to take some aspirin for this. Going back

take some aspirin for this. Going back up, could navigate into the quarters or

up, could navigate into the quarters or fully for the year. We're going to put

fully for the year. We're going to put that back down into monthly. One thing

that back down into monthly. One thing that you may want to add to a line chart

that you may want to add to a line chart or an area chart that has values that

or an area chart that has values that you may want to control is this. So

you may want to control is this. So underneath visuals and format your

underneath visuals and format your visual, they have this zoom slider. I'm

visual, they have this zoom slider. I'm going to go ahead and turn it on. It

going to go ahead and turn it on. It automatically enables it for the X and

automatically enables it for the X and Yaxis. In this case, that allows the

Yaxis. In this case, that allows the user to go in and zoom in on a

user to go in and zoom in on a particular area for what they want to

particular area for what they want to do. So if they wanted to drill down into

do. So if they wanted to drill down into the daily portion, they could and then

the daily portion, they could and then look at, hey, what's going on during

look at, hey, what's going on during this week right here, I can also enable

this week right here, I can also enable depending on what I want, if the x-axis

depending on what I want, if the x-axis or y-axis, and typically I would just do

or y-axis, and typically I would just do it on the x-axis, especially if it has

it on the x-axis, especially if it has date values. Navigate back up to month.

Anytime we have any type of stacked visualization, remember we can do

visualization, remember we can do something like a 100% stacked

something like a 100% stacked visualization. In this case, 100%

visualization. In this case, 100% stacked area chart. This is the same

stacked area chart. This is the same data that we did previously, but just

data that we did previously, but just put into a graph in order to analyze it

put into a graph in order to analyze it in a 100% format. Because of that, I'm

in a 100% format. Because of that, I'm just going to take that top chart up

just going to take that top chart up here, control C it, control + V, move

here, control C it, control + V, move into the bottom quadrant, put it into

into the bottom quadrant, put it into focus mode. As always, start with the

focus mode. As always, start with the title to what are the portion of data

title to what are the portion of data jobs in 2024. And then for the visual

jobs in 2024. And then for the visual itself, I'll change this into a 100%

itself, I'll change this into a 100% stacked area chart. And bam. One thing I

stacked area chart. And bam. One thing I will note, this job count, anytime we

will note, this job count, anytime we did a percentage, I didn't do it in the

did a percentage, I didn't do it in the last lesson, I probably should have, is

last lesson, I probably should have, is I would probably annotate that it is a

I would probably annotate that it is a percentage by updating this value here.

percentage by updating this value here. So now with this one, I feel we can read

So now with this one, I feel we can read it a at least a little bit better than

it a at least a little bit better than just the area chart. We can see towards

just the area chart. We can see towards the end of the year, data analyst

the end of the year, data analyst actually go up in their proportion and

actually go up in their proportion and they have around 30% of postings towards

they have around 30% of postings towards the end of the year. Pretty neat.

All right, last visualization to build and that is a line and column chart.

and that is a line and column chart. This one we're going to be looking at

This one we're going to be looking at yearly and also hourly median salary for

yearly and also hourly median salary for each of the 10 jobs that we have in the

each of the 10 jobs that we have in the job title short column. Personally, I

job title short column. Personally, I feel the yearly salary is more important

feel the yearly salary is more important in this case. So, we're going to make it

in this case. So, we're going to make it the columns because they really stand

the columns because they really stand out to me. And then from there, we'll

out to me. And then from there, we'll make the hourly the line. And

make the hourly the line. And personally, I don't like starting from

personally, I don't like starting from scratch if I don't need to. So, I'm

scratch if I don't need to. So, I'm going to grab this where's the highest

going to grab this where's the highest paying job in data visualization we made

paying job in data visualization we made on the other page. Copy it and then

on the other page. Copy it and then paste it on in here. Drag it down. We'll

paste it on in here. Drag it down. We'll open up up into the focus mode. And the

open up up into the focus mode. And the first thing, as always, I'm going to

first thing, as always, I'm going to change that title to make sure we stay

change that title to make sure we stay on task. And we'll change this to salary

on task. And we'll change this to salary verse hourly pay of data jobs. I don't

verse hourly pay of data jobs. I don't always do questions. I also sometimes do

always do questions. I also sometimes do things like verses. People love verses.

things like verses. People love verses. And so if you do that in this case, it

And so if you do that in this case, it gives them cues them in of what they

gives them cues them in of what they should be looking at in the

should be looking at in the visualization. Now let's actually format

visualization. Now let's actually format this into what we want. We want either a

this into what we want. We want either a line and stack column chart or line and

line and stack column chart or line and stack clustered column chart. Doesn't

stack clustered column chart. Doesn't really matter. We're only going to be

really matter. We're only going to be using one column with this. We have job

using one column with this. We have job title on the x-axis, the column yaxis.

title on the x-axis, the column yaxis. I'm going to update this to median

I'm going to update this to median yearly salary and add USD onto it. And

yearly salary and add USD onto it. And now we have the line yaxis. I'm going to

now we have the line yaxis. I'm going to drag salary hour average into this. And

drag salary hour average into this. And right now it's doing a sum. I don't want

right now it's doing a sum. I don't want it to do a sum. I want it to do a

it to do a sum. I want it to do a median. And if you notice with that,

median. And if you notice with that, whenever I click that, it then because

whenever I click that, it then because the values weren't the same or couldn't

the values weren't the same or couldn't match up with the original yaxis, it

match up with the original yaxis, it created a secondary yaxis. I'm going to

created a secondary yaxis. I'm going to change the title of this to median

change the title of this to median hourly salary USD. Now, one thing I

hourly salary USD. Now, one thing I could do to dress this up is I could add

could do to dress this up is I could add what's down here of data labels. We're

what's down here of data labels. We're going to go ahead and turn this on. This

going to go ahead and turn this on. This is data labels that are applied to all

is data labels that are applied to all series. I could adjust it to only do the

series. I could adjust it to only do the hourly. Anyway, so data labels hasn't

hourly. Anyway, so data labels hasn't been working properly. I'm going to go

been working properly. I'm going to go ahead. Let's actually try. We want to do

ahead. Let's actually try. We want to do it only the hourly salaries. I've

it only the hourly salaries. I've clicked series. I want to apply this to

clicked series. I want to apply this to hourly salaries. Turn on. Okay, it's on.

hourly salaries. Turn on. Okay, it's on. It's working. If I want to do yearly, I

It's working. If I want to do yearly, I can click yearly and then toggle this

can click yearly and then toggle this on. Or if I want to toggle it off. Let's

on. Or if I want to toggle it off. Let's say we want to do just the hourly. We

say we want to do just the hourly. We can display it this way. Personally, I

can display it this way. Personally, I feel like there's too much information

feel like there's too much information on this. I'm not going to apply data

on this. I'm not going to apply data labels to this type of visualization. If

labels to this type of visualization. If we go back to our actual first

we go back to our actual first visualization, this would be more one

visualization, this would be more one that I would apply data labels to. And

that I would apply data labels to. And with this, I could do things like add a

with this, I could do things like add a background to make it call out a little

background to make it call out a little bit better of what's going on with it.

bit better of what's going on with it. And then also going into value, making

And then also going into value, making it something like bold, so it's a little

it something like bold, so it's a little bit easier to actually read. And bam.

bit easier to actually read. And bam. Yeah, that's actually pretty good. Not

Yeah, that's actually pretty good. Not too bad. In this case, if I were to have

too bad. In this case, if I were to have the data labels, I'd most likely turn

the data labels, I'd most likely turn off something like the y-axis, like the

off something like the y-axis, like the values and the title because it already

values and the title because it already has it there. Additionally, under the

has it there. Additionally, under the grid lines, I just think that's too

grid lines, I just think that's too much. I could turn it off there. And now

much. I could turn it off there. And now I have a more simplistic view of what's

I have a more simplistic view of what's going on here with the data. So that is

going on here with the data. So that is line and also area charts or combo

line and also area charts or combo charts if you will. Out of all these,

charts if you will. Out of all these, the line chart is the most common right

the line chart is the most common right after column and bar. So, it pays to

after column and bar. So, it pays to know how to format this bad boy. All

know how to format this bad boy. All right, you now have some practice

right, you now have some practice problems to go through and get more

problems to go through and get more familiar with how to use these different

familiar with how to use these different types of charts. In the next lesson,

types of charts. In the next lesson, we're going to be jumping into other

we're going to be jumping into other common types of charts such as pie,

common types of charts such as pie, donut, tree map, and even scatter plots.

donut, tree map, and even scatter plots. With that, I'll see you there.

In this lesson, we're going to be covering other common types of charts.

covering other common types of charts. We still will be covering some more

We still will be covering some more after this, but I don't feel that

after this, but I don't feel that they're as common as these here and also

they're as common as these here and also those line and column and bar charts.

those line and column and bar charts. So, let's jump in and see what we're

So, let's jump in and see what we're going to be making for this. First up,

going to be making for this. First up, it's a pie chart. And in this, we're

it's a pie chart. And in this, we're analyzing what portion of job postings

analyzing what portion of job postings don't mention a degree. Remember, our

don't mention a degree. Remember, our data sets on job postings. Sometimes

data sets on job postings. Sometimes they mention a degree or they don't.

they mention a degree or they don't. This marks true when it doesn't mention

This marks true when it doesn't mention a degree. Anyway, we'll make a pie chart

a degree. Anyway, we'll make a pie chart out of this. And then similarly, we'll

out of this. And then similarly, we'll make a donut chart, which is basically a

make a donut chart, which is basically a pie chart with a giant hole in the

pie chart with a giant hole in the middle, but instead of doing postings

middle, but instead of doing postings that don't mention a degree, we're going

that don't mention a degree, we're going to do which job postings are marked as

to do which job postings are marked as work from home. Something kind of like

work from home. Something kind of like doing. Next, we'll get into making a

doing. Next, we'll get into making a tree map, which is this bad boy. And in

tree map, which is this bad boy. And in this, we're showing what are the types

this, we're showing what are the types of data jobs. Basically, it could be

of data jobs. Basically, it could be something like full-time, contractor,

something like full-time, contractor, internship, part-time, or even temp

internship, part-time, or even temp work. Tree maps are really good because

work. Tree maps are really good because whenever you have them with other

whenever you have them with other visualizations, people want to click on

visualizations, people want to click on them and you'll be able to filter your

them and you'll be able to filter your data down to what you want to actually

data down to what you want to actually see. Anyway, the last visualization

see. Anyway, the last visualization we're going to be doing is a scatter

we're going to be doing is a scatter plot, which is going to look at the hour

plot, which is going to look at the hour median salary verse the yearly median

median salary verse the yearly median salary for different job titles. And as

salary for different job titles. And as we're going to come to find out, there's

we're going to come to find out, there's definitely a trend between the two.

definitely a trend between the two. So here's the visualization we're going

So here's the visualization we're going to be building and it's looking at what

to be building and it's looking at what portions of postings don't mention

portions of postings don't mention degree. Remember we're using that field

degree. Remember we're using that field of job no degree mention. I think in the

of job no degree mention. I think in the intro I said it dimensions degree had

intro I said it dimensions degree had that backwards. It's what portion of job

that backwards. It's what portion of job postings don't mention degree. So in our

postings don't mention degree. So in our common charts canvas we're going to come

common charts canvas we're going to come in and insert a pie chart. And after

in and insert a pie chart. And after resizing I'm going to enter into focus

resizing I'm going to enter into focus mode. Remember we want to use that job

mode. Remember we want to use that job no degree mentioned column. So I'm going

no degree mentioned column. So I'm going to actually minimize this. Put job note

to actually minimize this. Put job note degree mention into the legend and it's

degree mention into the legend and it's appearing the legend is appearing but

appearing the legend is appearing but nothing's appearing. So we actually have

nothing's appearing. So we actually have to put some values in. Specifically we

to put some values in. Specifically we want the count of that column. First

want the count of that column. First things first I'm going to adjust the

things first I'm going to adjust the title to make sure we stay on task.

title to make sure we stay on task. Change it to what portion of postings

Change it to what portion of postings don't mention degree. So not too bad. I

don't mention degree. So not too bad. I do want to do some cleanup. Mainly I

do want to do some cleanup. Mainly I don't like this legend. I'd rather have

don't like this legend. I'd rather have data labels saying what is true and what

data labels saying what is true and what is false. so they don't have to figure

is false. so they don't have to figure out which one's true and false from

out which one's true and false from this. So under visual, I'll go down to

this. So under visual, I'll go down to detail labels. And for the label

detail labels. And for the label contents, we want to have the category.

contents, we want to have the category. And I don't really care about the count.

And I don't really care about the count. So I'm going to go to category percent

So I'm going to go to category percent of total. Scrolling down to the values,

of total. Scrolling down to the values, we'll make this a little bit bigger. Not

we'll make this a little bit bigger. Not too bad. I don't really like that it has

too bad. I don't really like that it has two decimal places. I just want zero. So

two decimal places. I just want zero. So not too bad. So this is pretty good for

not too bad. So this is pretty good for the detail labels. Because of that, I

the detail labels. Because of that, I don't want the legend. I'm going to go

don't want the legend. I'm going to go ahead and turn that off. Now, anytime

ahead and turn that off. Now, anytime I'm making a pie chart, I typically want

I'm making a pie chart, I typically want to use only two to three values in it.

to use only two to three values in it. That's why we're doing this true or

That's why we're doing this true or false here. And with it, right, the

false here. And with it, right, the question is what portion of postings

question is what portion of postings don't mention degrees. So, immediately I

don't mention degrees. So, immediately I want their eye to go to true. So, I do

want their eye to go to true. So, I do want true to be darker in this. However,

want true to be darker in this. However, I'm not really a fan of the colors. So,

I'm not really a fan of the colors. So, I'm going to go into slices. The true,

I'm going to go into slices. The true, we're going to maintain this dark blue.

we're going to maintain this dark blue. But for this false, I want a much

But for this false, I want a much lighter color. We'll leave it in the

lighter color. We'll leave it in the same palette even. And we'll make it

same palette even. And we'll make it this one. This, in my opinion, is much

this one. This, in my opinion, is much more readable and it draws it into where

more readable and it draws it into where they need to go. So, as we can see from

they need to go. So, as we can see from this, onethirds of job postings in

this, onethirds of job postings in general for all jobs don't mention the

general for all jobs don't mention the degree in the job posting.

Next one to make is a donut chart. This one will be pretty easy in however we're

one will be pretty easy in however we're changing it up. We're going to be

changing it up. We're going to be looking at what portion of job postings

looking at what portion of job postings are work from home. So I'm going to take

are work from home. So I'm going to take our original pie chart, control C it and

our original pie chart, control C it and control V it. Drag it up to the top

control V it. Drag it up to the top right hand corner and then I'm going to

right hand corner and then I'm going to change this into a doughnut chart. Now

change this into a doughnut chart. Now with this one and we're looking at work

with this one and we're looking at work from home and we have a column on that

from home and we have a column on that that's also a true and false value. So

that's also a true and false value. So two values. So perfect for this. So, I'm

two values. So perfect for this. So, I'm going to drag that into the legend

going to drag that into the legend itself and take out job no degree

itself and take out job no degree mention along with putting that jerk

mention along with putting that jerk work from home job work from home in the

work from home job work from home in the values and removing that job no degree

values and removing that job no degree mention. Put this bad boy into focus

mention. Put this bad boy into focus mode. As always, we're going to update

mode. As always, we're going to update that title first to what portion of

that title first to what portion of postings are work from home. I'm not a

postings are work from home. I'm not a fan of the coloring scheme once again.

fan of the coloring scheme once again. So, we'll change that true to that dark

So, we'll change that true to that dark color and we'll change the false to the

color and we'll change the false to the lighter one. All right. Not bad. I am

lighter one. All right. Not bad. I am noticing though the tool tips. Right.

noticing though the tool tips. Right. Um, it is it's very verbose in what it

Um, it is it's very verbose in what it has there. We do need to update that.

has there. We do need to update that. And I changed it to is work from home

And I changed it to is work from home and job count. So, is work from home

and job count. So, is work from home true. Looks like job count. It's like

true. Looks like job count. It's like 63,000 jobs or 13%. Don't forget also

63,000 jobs or 13%. Don't forget also when we probably need to update that pie

when we probably need to update that pie chart as well. Now, when you scroll over

chart as well. Now, when you scroll over it, we can see, hey, is no degree

it, we can see, hey, is no degree mentioned? True. And the count, which

mentioned? True. And the count, which count didn't update. Okay, that's

count didn't update. Okay, that's looking better.

looking better. Next up is tree map. And this is really

Next up is tree map. And this is really good visualization to show a breakdown

good visualization to show a breakdown of what's available. But mainly I like

of what's available. But mainly I like it because if we actually go back to the

it because if we actually go back to the report itself with everything else on it

report itself with everything else on it makes it to where you want to actually

makes it to where you want to actually interact with it. So in the case of the

interact with it. So in the case of the tree map, if I wanted to look at just

tree map, if I wanted to look at just full-time roles, I can click on that and

full-time roles, I can click on that and then all the other data filters for

then all the other data filters for that. So it's a great way to draw users

that. So it's a great way to draw users in and interact with your report. So

in and interact with your report. So inside of here, we're going to click

inside of here, we're going to click here and we're going to add a tree map.

here and we're going to add a tree map. I'm going to drag it to the bottom right

I'm going to drag it to the bottom right hand corner. Go into focus mode. For

hand corner. Go into focus mode. For this, we're using a new column we

this, we're using a new column we haven't used yet. It's this job schedule

haven't used yet. It's this job schedule type. And job schedule type has

type. And job schedule type has different values in it. Actually has

different values in it. Actually has this actually needs a lot of cleanup,

this actually needs a lot of cleanup, but it has things like contractor,

but it has things like contractor, full-time, part-time, PDM, temp work.

full-time, part-time, PDM, temp work. We'll actually get into cleaning this up

We'll actually get into cleaning this up in the next chapter in chapter three uh

in the next chapter in chapter three uh chapter 3 on Power Query. So stand by

chapter 3 on Power Query. So stand by for that. Anyway, we're going to drag

for that. Anyway, we're going to drag this job schedule type into the

this job schedule type into the categories and then also drag it into

categories and then also drag it into the values. First thing I want to do

the values. First thing I want to do like usual is update the title to what

like usual is update the title to what are the type of data jobs. Now there's a

are the type of data jobs. Now there's a few too many values here. You can see

few too many values here. You can see it's even breaking down further in the

it's even breaking down further in the bottom right hand corner. I want to

bottom right hand corner. I want to filter this down to only specific values

filter this down to only specific values for the job schedule type. Specifically,

for the job schedule type. Specifically, if I move over this filter, whenever it

if I move over this filter, whenever it has multiple different ones like

has multiple different ones like contractor and full-time, contractor to

contractor and full-time, contractor to internship, there's only like two, 45,

internship, there's only like two, 45, 40. I don't really want those. I want

40. I don't really want those. I want just the single value. So, in this case,

just the single value. So, in this case, contractor, full-time, internship,

contractor, full-time, internship, part-time, PDM doesn't have a lot, so

part-time, PDM doesn't have a lot, so we're not going to do that. We'll do

we're not going to do that. We'll do temp work and we'll call it good. Close

temp work and we'll call it good. Close out of this filters pane. So, at least a

out of this filters pane. So, at least a little bit more readable in what are the

little bit more readable in what are the different selections and the top values

different selections and the top values uh that we can get. Now, I'm not a big

uh that we can get. Now, I'm not a big fan of all these different colors,

fan of all these different colors, especially if you go back to your

especially if you go back to your report. Like, it just doesn't match the

report. Like, it just doesn't match the palette that we're doing here. And also,

palette that we're doing here. And also, in general, I don't like a lot of

in general, I don't like a lot of different colors. It gets visually

different colors. It gets visually distracting. Your users won't know where

distracting. Your users won't know where to put their eyes. In our case, we do

to put their eyes. In our case, we do want their eyes to go to probably the

want their eyes to go to probably the most the biggest value, if you will. So,

most the biggest value, if you will. So, once again, we want to get this darker.

once again, we want to get this darker. And then the other values, these smaller

And then the other values, these smaller ones, we want a little bit lighter. So

ones, we want a little bit lighter. So what we can do is under format visual

what we can do is under format visual under colors, we could change each one

under colors, we could change each one of these colors individually for what it

of these colors individually for what it is. Like if I want to change this to

is. Like if I want to change this to contractor to pink. I'm not really a fan

contractor to pink. I'm not really a fan of this. Instead, we go into advanced

of this. Instead, we go into advanced controls and use this conditional

controls and use this conditional formatting. Under the format style,

formatting. Under the format style, we're going to change this into a

we're going to change this into a gradient. And let's change this into

gradient. And let's change this into those colors. So for the minimum value,

those colors. So for the minimum value, let's use what we were using previously,

let's use what we were using previously, this light color theme. And then for the

this light color theme. And then for the maximum value, we're going to use the

maximum value, we're going to use the darker one of this. This looks okay.

darker one of this. This looks okay. Click okay. And bam. Now we have

Click okay. And bam. Now we have something that's a lot more manageable

something that's a lot more manageable in the eyesight. And when compared to

in the eyesight. And when compared to all our other visuals, it fits in in the

all our other visuals, it fits in in the visuals on where we want them to draw

visuals on where we want them to draw their attention. Last thing we need to

their attention. Last thing we need to do is just update the tool tips for

do is just update the tool tips for this. We'll call this one schedule type

this. We'll call this one schedule type for the category and then for the

for the category and then for the values, job count. All right, not too

values, job count. All right, not too bad. getting this back into focus mode.

bad. getting this back into focus mode. The other thing that we want to do is we

The other thing that we want to do is we want to put because right now we can see

want to put because right now we can see it, but what is the relative percentage

it, but what is the relative percentage of each of these relative to each other?

of each of these relative to each other? So, what I'm going to do is come in here

So, what I'm going to do is come in here under format visual under the data

under format visual under the data labels. I'm going to go ahead and turn

labels. I'm going to go ahead and turn them on. And if you notice, they're

them on. And if you notice, they're actually a count. I don't really want

actually a count. I don't really want count, but we'll deal with that for the

count, but we'll deal with that for the time being. We will change, however, the

time being. We will change, however, the value decimal places to zero. And I will

value decimal places to zero. And I will make them slightly bigger. Now, like I

make them slightly bigger. Now, like I said, I don't want to count for here. I

said, I don't want to count for here. I can't change this percentage inside of

can't change this percentage inside of here. But what I can do is going into

here. But what I can do is going into values, clicking on this down arrow.

values, clicking on this down arrow. Right now, we have count. But then we

Right now, we have count. But then we have this show value as we could do no

have this show value as we could do no calculation or percent of grand total,

calculation or percent of grand total, which is actually exactly what we want.

which is actually exactly what we want. We see that full-time jobs are 90% of

We see that full-time jobs are 90% of the job postings, contractors are 7%,

the job postings, contractors are 7%, internships are less than 1%, and

internships are less than 1%, and following.

The last visualization to make is a scatter plot. Scatter plots are great at

scatter plot. Scatter plots are great at showing the relationship between two

showing the relationship between two different values. In this case, we can

different values. In this case, we can show the relationship between hourly and

show the relationship between hourly and yearly median salary for these different

yearly median salary for these different job titles. So, let's put this bad boy

job titles. So, let's put this bad boy together. In our canvas, we're going to

together. In our canvas, we're going to throw in this scatter chart. I'm going

throw in this scatter chart. I'm going to go into focus mode. For this one,

to go into focus mode. For this one, we're going to just start off instead of

we're going to just start off instead of doing the values, we're going to start

doing the values, we're going to start with that x-axis, putting the yearly

with that x-axis, putting the yearly salary data there and then the hourly in

salary data there and then the hourly in the y ais. For both of these, we want to

the y ais. For both of these, we want to aggregate by the median. Then we want to

aggregate by the median. Then we want to break it up right by the job title. So,

break it up right by the job title. So, we need to put in the legend that job

we need to put in the legend that job title short. All right, not too bad.

title short. All right, not too bad. Before we get too far, I do want to

Before we get too far, I do want to update the title on this to hourly verse

update the title on this to hourly verse yearly salary of data jobs. People love

yearly salary of data jobs. People love some verses. I'm also going to clean up

some verses. I'm also going to clean up what's in the field well to make these

what's in the field well to make these values more readable. So there's a lot

values more readable. So there's a lot more readable. We can see what's up here

more readable. We can see what's up here in the top right hand corner. Machine

in the top right hand corner. Machine learning engineers have 155,000 for the

learning engineers have 155,000 for the hour for the yearly salary, $60 for the

hour for the yearly salary, $60 for the hourly salary. Might need to consider

hourly salary. Might need to consider changing my job. Anyway, once again, if

changing my job. Anyway, once again, if I go back to the actual report pane,

I go back to the actual report pane, look at this. I mean, the the coloring

look at this. I mean, the the coloring on this just doesn't match the other

on this just doesn't match the other pallets. And two, it's just highly

pallets. And two, it's just highly distracting. If I go back into focus

distracting. If I go back into focus mode, where the heck do you need to look

mode, where the heck do you need to look on this? Well, with a scatter plot in

on this? Well, with a scatter plot in general, I don't want to necessarily

general, I don't want to necessarily draw your attention any specific

draw your attention any specific location in this kind of manner. So, I

location in this kind of manner. So, I want to probably just remove the colors.

want to probably just remove the colors. However, this one's a little bit more

However, this one's a little bit more tricky. In this case, we're going to go

tricky. In this case, we're going to go to format your visual and we go to

to format your visual and we go to markers and expanding it out and

markers and expanding it out and scrolling on down to colors. If I try

scrolling on down to colors. If I try to, it doesn't let me change the color.

to, it doesn't let me change the color. I can, however, change the transparency.

I can, however, change the transparency. We're going to take that transparency to

We're going to take that transparency to 100%. Basically, not make it 100%

100%. Basically, not make it 100% transparent. You can't visual visualize

transparent. You can't visual visualize it. And I'm going to go into border.

it. And I'm going to go into border. With this, I'm going to uncheck the

With this, I'm going to uncheck the match fill color. And now, you see all

match fill color. And now, you see all of them are this dark gray color. What I

of them are this dark gray color. What I can do is I can come in and make it that

can do is I can come in and make it that dark blue color that I like. And I can

dark blue color that I like. And I can make the width of this bigger. That's a

make the width of this bigger. That's a little bit too big. I could also adjust

little bit too big. I could also adjust the transparency on this, but then my

the transparency on this, but then my visuals aren't available. Okay. So, not

visuals aren't available. Okay. So, not bad. But I'd argue it still needs well

bad. But I'd argue it still needs well one this is highly confusing because I

one this is highly confusing because I have this job title legend up here with

have this job title legend up here with colors and none of it correlates. So

colors and none of it correlates. So under format visual under the same thing

under format visual under the same thing I'm going to turn off the legend first

I'm going to turn off the legend first and then under category labels I'm going

and then under category labels I'm going to turn this on. This is allows us to

to turn this on. This is allows us to now format those different uh category

now format those different uh category labels. I made it slightly bigger and we

labels. I made it slightly bigger and we can actually see where everything falls

can actually see where everything falls and we can see something like data

and we can see something like data analyst is down at the bottom left hand

analyst is down at the bottom left hand corner. Oh gosh. All right. So, not too

corner. Oh gosh. All right. So, not too bad. Anytime I have any of these scatter

bad. Anytime I have any of these scatter plots, I would want to I want to see is

plots, I would want to I want to see is there some sort of relationship. I could

there some sort of relationship. I could in this case put in a trend line, make

in this case put in a trend line, make it a lighter color and a little bit

it a lighter color and a little bit transparent so it doesn't like take up

transparent so it doesn't like take up the entire thing. And then bam, we can

the entire thing. And then bam, we can actually see, okay, in queue our end

actually see, okay, in queue our end users in, there is actually a trend. If

users in, there is actually a trend. If there's a higher yearly salary, there's

there's a higher yearly salary, there's probably going to be a higher median

probably going to be a higher median hourly salary. Those that have senior

hourly salary. Those that have senior roles, like senior data engineers,

roles, like senior data engineers, senior data scientists are going to pay

senior data scientists are going to pay more than their counterparts. Well, at

more than their counterparts. Well, at least in some cases, looks like this

least in some cases, looks like this data engineer in hourly gets paid more

data engineer in hourly gets paid more than senior data engineer in their

than senior data engineer in their hourly. There's no

hourly. There's no there's no more visuals to build, but I

there's no more visuals to build, but I want to talk about a feature within

want to talk about a feature within PowerBI dealing with editing

PowerBI dealing with editing interactions. If you remember from

interactions. If you remember from earlier, I talked about with this tree

earlier, I talked about with this tree map. It's great because I can click it

map. It's great because I can click it and then it will filter other data. But

and then it will filter other data. But let's do something like I'm going to

let's do something like I'm going to click contractor, which has less. What

click contractor, which has less. What the heck am I supposed to make out of

the heck am I supposed to make out of this doughut chart? And what the heck am

this doughut chart? And what the heck am I supposed to make out of this pie

I supposed to make out of this pie chart? Like I'm not even like you can

chart? Like I'm not even like you can see they're not even the same size. I'm

see they're not even the same size. I'm supposed to do the math on my head and

supposed to do the math on my head and try to figure out how they are

try to figure out how they are different. Basically, I'm not liking how

different. Basically, I'm not liking how they're crossfiltered. Well, that's

they're crossfiltered. Well, that's where if we go into the format tab, they

where if we go into the format tab, they have this edit interactions. I'm going

have this edit interactions. I'm going to go ahead and click it and it stays

to go ahead and click it and it stays clicked. As you notice this, these icons

clicked. As you notice this, these icons popped up and then when I turn it off,

popped up and then when I turn it off, those icons pop away. So, this enables

those icons pop away. So, this enables you to edit interactions. But there's

you to edit interactions. But there's only one icon here that there's a

only one icon here that there's a there's actually a little bit of a bug

there's actually a little bit of a bug with PowerBI. You can't see some of

with PowerBI. You can't see some of these visualizations and their

these visualizations and their appropriate things for edit interaction.

appropriate things for edit interaction. So I'm going to make these other charts

So I'm going to make these other charts a little bit smaller. Now we can see

a little bit smaller. Now we can see when I'm clicked on the tree map, all

when I'm clicked on the tree map, all these other three around these have

these other three around these have these options to edit the interactions.

these options to edit the interactions. Anyway, the easiest one to understand is

Anyway, the easiest one to understand is for the tree map, right? I can turn off

for the tree map, right? I can turn off to make it none for all of these, right?

to make it none for all of these, right? So they're not getting filtered. So as a

So they're not getting filtered. So as a test, whenever I I just turned off edit

test, whenever I I just turned off edit interactions as a test, I can click

interactions as a test, I can click different things in here and none of the

different things in here and none of the other ones are getting or having

other ones are getting or having affected. However, whenever I go to the

affected. However, whenever I go to the pie chart, it is interacting with

pie chart, it is interacting with others, right? I I did it specifically

others, right? I I did it specifically for this tree map. Now, I typically

for this tree map. Now, I typically don't use this none. Let my turn back on

don't use this none. Let my turn back on edit interactions. I typically don't use

edit interactions. I typically don't use this none to remove the filtering.

this none to remove the filtering. Remember, by default, it was on this

Remember, by default, it was on this highlight for at least for the pie

highlight for at least for the pie charts. For this scatter plot, they only

charts. For this scatter plot, they only have one option, and that's only filter.

have one option, and that's only filter. We're going to leave scatter plot as

We're going to leave scatter plot as filter. Anyway, with the tree map

filter. Anyway, with the tree map selected, let's change these other ones

selected, let's change these other ones now from being highlight to filter. I'm

now from being highlight to filter. I'm going to change it for that. And also

going to change it for that. And also the pie chart. Now, whenever I click the

the pie chart. Now, whenever I click the tree map and filter down, notice what it

tree map and filter down, notice what it does. it doesn't actually do that

does. it doesn't actually do that shrinking down. It actually adjusts the

shrinking down. It actually adjusts the actual pie chart itself and uh donut

actual pie chart itself and uh donut chart. So it makes a lot more readable.

chart. So it makes a lot more readable. However, this is just for the tree map.

However, this is just for the tree map. If I go over to the scatter plot, I'm

If I go over to the scatter plot, I'm going to look at something like data

going to look at something like data engineers and I clicked on this. It's

engineers and I clicked on this. It's still going to do this option. Notice

still going to do this option. Notice now I'm selected on the scatter plot.

now I'm selected on the scatter plot. This for the pie chart is on highlight.

This for the pie chart is on highlight. I'm going to change this to filter. And

I'm going to change this to filter. And for the doughut chart, I'm going to

for the doughut chart, I'm going to change it also to filter. Okay, now just

change it also to filter. Okay, now just testing it. Yep, it's getting adjusted.

testing it. Yep, it's getting adjusted. Two more I want to update for this. So,

Two more I want to update for this. So, same thing. I'm going to select the pie

same thing. I'm going to select the pie chart. Click in here. Don't like how

chart. Click in here. Don't like how it's doing it. I'm going to change this

it's doing it. I'm going to change this one. And then it click into the doughut

one. And then it click into the doughut chart. Going to click this one. Oh,

chart. Going to click this one. Oh, don't like how it's done. Going to

don't like how it's done. Going to change this one to filter. And now

change this one to filter. And now everything is back or at least in

everything is back or at least in configured in a way that I actually like

configured in a way that I actually like it. And so I can make these charts back

it. And so I can make these charts back to the same size they were. Basically

to the same size they were. Basically hid those icons that were being hidden

hid those icons that were being hidden andclick edit interactions. Now just

andclick edit interactions. Now just testing it out. Can click contractor.

testing it out. Can click contractor. Yep, everything filters down. I can

Yep, everything filters down. I can click the true portion of work from

click the true portion of work from home. Yep, everything filters down like

home. Yep, everything filters down like I want. So edit interactions is sort of

I want. So edit interactions is sort of an advanced concept to understand get

an advanced concept to understand get through. So that's why we have some

through. So that's why we have some practice problems to now for you to go

practice problems to now for you to go through not only build those pie and

through not only build those pie and those doughnut charts, but also get some

those doughnut charts, but also get some familiarity with how to use edit

familiarity with how to use edit interactions. With that, I'll see you in

interactions. With that, I'll see you in the next one.

Welcome to this lesson on maps. And although I use these less frequently

although I use these less frequently like thing than things like bar charts,

like thing than things like bar charts, column charts or line charts, these do

column charts or line charts, these do these map visuals do have their place

these map visuals do have their place from time to time. So let's jump in into

from time to time. So let's jump in into understanding what we're actually going

understanding what we're actually going to be doing for this lesson in building

to be doing for this lesson in building three different maps. Now PowerBI by

three different maps. Now PowerBI by default has three different map types.

default has three different map types. The first one is called just map. Really

The first one is called just map. Really special, I know. The second one is

special, I know. The second one is called field map because it looks like a

called field map because it looks like a field map. And then the third one right

field map. And then the third one right here is ArcGIS for PowerBI map.

here is ArcGIS for PowerBI map. Basically, they want you to buy the

Basically, they want you to buy the extra ArcGIS. We'll get into all that in

extra ArcGIS. We'll get into all that in a little bit. Anyway, for the basic

a little bit. Anyway, for the basic first map overview, we're going to be

first map overview, we're going to be looking at which countries don't mention

looking at which countries don't mention degrees in their job postings. This

degrees in their job postings. This displays a dot over our specified

displays a dot over our specified location, in our case, country. And the

location, in our case, country. And the size of it is relative, in our case, to

size of it is relative, in our case, to the job count. We can also break it down

the job count. We can also break it down further into this pie chart inside of

further into this pie chart inside of here for true or false values. In our

here for true or false values. In our case, true that it doesn't mention a

case, true that it doesn't mention a degree in the job posting. Next up is

degree in the job posting. Next up is our filled map. And I'll be honest, out

our filled map. And I'll be honest, out of all the maps here, find this the most

of all the maps here, find this the most useless, but I do want to cover it

useless, but I do want to cover it anyway. It fills in a certain color. In

anyway. It fills in a certain color. In this case, we're just filtering the

this case, we're just filtering the color based on what country is which

color based on what country is which country, which in my mind, I'm like, I

country, which in my mind, I'm like, I don't really care about that. That's why

don't really care about that. That's why I'm not really that big of a fan of this

I'm not really that big of a fan of this type of map, although I do want to go

type of map, although I do want to go through it so you're aware of it. Now,

through it so you're aware of it. Now, the last one I actually like the most of

the last one I actually like the most of RGis for PowerBI. And this one, we're

RGis for PowerBI. And this one, we're looking at what are the highest paying

looking at what are the highest paying jobs globally. Here you can see, well,

jobs globally. Here you can see, well, this country right here, this big one

this country right here, this big one has the highest paying. Kind of fishy.

has the highest paying. Kind of fishy. Anyway, this one's going to come with a

Anyway, this one's going to come with a little bit of a catch. not not the

little bit of a catch. not not the country, the map itself. And so my main

country, the map itself. And so my main recommended one is this first one.

recommended one is this first one. So let's get into building these map

So let's get into building these map visuals. We're going to start by

visuals. We're going to start by creating a new page. And I'm going to

creating a new page. And I'm going to call this map charts. Now, if you didn't

call this map charts. Now, if you didn't actually work through the first chapter

actually work through the first chapter in this, you're going to come to find

in this, you're going to come to find out that the map charts aren't going to

out that the map charts aren't going to work. We have to actually enable them.

work. We have to actually enable them. And this is done by going into file and

And this is done by going into file and then options and settings and then

then options and settings and then options. I'm going to navigate down here

options. I'm going to navigate down here under global to security. And for this,

under global to security. And for this, we want to make sure that custom v uh

we want to make sure that custom v uh visuals are enabled. ArcJS for PowerBI,

visuals are enabled. ArcJS for PowerBI, which is one of the maps, and then the

which is one of the maps, and then the map and field map visuals are also

map and field map visuals are also enabled. These are the main two that you

enabled. These are the main two that you need for this lesson. Now, if you set up

need for this lesson. Now, if you set up a PowerBI Pro license and you've

a PowerBI Pro license and you've connected it to your PowerBI app, you

connected it to your PowerBI app, you may need to take these additional steps

may need to take these additional steps that I did in order to enable it in the

that I did in order to enable it in the PowerBI service so you don't get black

PowerBI service so you don't get black blocked from doing it in the app. If you

blocked from doing it in the app. If you didn't set up a free or pro account with

didn't set up a free or pro account with PowerBI, this portion is not applicable

PowerBI, this portion is not applicable to you. Anyway, I'm going to go to the

to you. Anyway, I'm going to go to the settings icon up here and I'm going to

settings icon up here and I'm going to navigate to the admin portal. In the

navigate to the admin portal. In the search bar over here, I'm going to type

search bar over here, I'm going to type in map. And for this I want to ensure

in map. And for this I want to ensure that use RGIs maps for PowerBI is

that use RGIs maps for PowerBI is enabled and also that the map and field

enabled and also that the map and field map visuals are enabled. Whenever you

map visuals are enabled. Whenever you enable them you need to then go ahead

enable them you need to then go ahead and apply. And like usual this can take

and apply. And like usual this can take up to 15 minutes to work.

up to 15 minutes to work. So while you're waiting for that to

So while you're waiting for that to load, let's jump into building our first

load, let's jump into building our first map visual. And like again this is going

map visual. And like again this is going to be looking at which countries don't

to be looking at which countries don't mention degrees in their job postings.

mention degrees in their job postings. in our blank canvas. I'm going to select

in our blank canvas. I'm going to select map. I'll move it into that top

map. I'll move it into that top quadrant. And then we go into focus

quadrant. And then we go into focus mode. All right. For this one, we're

mode. All right. For this one, we're going to be using we want to aggregate

going to be using we want to aggregate it by the job country location. So, I'm

it by the job country location. So, I'm going to go ahead and put that in here.

going to go ahead and put that in here. Now, notice there's a dot now for each

Now, notice there's a dot now for each of the countries. And just as a

of the countries. And just as a reminder, just so you can check it out,

reminder, just so you can check it out, I could I'm going to X out of this this

I could I'm going to X out of this this job country. And I'm going to drag job

job country. And I'm going to drag job location onto here. If you notice by

location onto here. If you notice by this, there's a lot more dots on here.

this, there's a lot more dots on here. If I scroll over it, these get very more

If I scroll over it, these get very more in deep uh detail. Like this was from

in deep uh detail. Like this was from Crawford'sville, Indiana. If you're

Crawford'sville, Indiana. If you're watching this from there, Indiana, give

watching this from there, Indiana, give a comment in the video below. Anyway,

a comment in the video below. Anyway, we'll use job location at a different

we'll use job location at a different time, but right now, this is just too

time, but right now, this is just too much data on this map visual. I don't

much data on this map visual. I don't like it. I'm going to go ahead and X out

like it. I'm going to go ahead and X out of it. Instead, we're going to drop job

of it. Instead, we're going to drop job country down into location. Now, this

country down into location. Now, this also has the option for using latitude

also has the option for using latitude and longitude and this will be

and longitude and this will be applicable to all the map visuals that

applicable to all the map visuals that we have. This results in even more

we have. This results in even more precise data because sometimes your

precise data because sometimes your location data isn't going to populate in

location data isn't going to populate in this map chart because it's not able to

this map chart because it's not able to figure it out on where it needs to go.

figure it out on where it needs to go. So, if you have latitude and longitude

So, if you have latitude and longitude data, which we don't have in this case,

data, which we don't have in this case, I recommend you use that instead because

I recommend you use that instead because it's more precise. Now, let's make these

it's more precise. Now, let's make these bubbles a different size. We're going to

bubbles a different size. We're going to use this bubble size. I'm going to drag

use this bubble size. I'm going to drag job title short into here and it's now

job title short into here and it's now going to do a count of job title short.

going to do a count of job title short. I'm going to rename this down to job

I'm going to rename this down to job count and then also this location to

count and then also this location to country. So that way whenever I scroll

country. So that way whenever I scroll over something like the United States, I

over something like the United States, I can see that hey the country is United

can see that hey the country is United States and the job count is 140,000

States and the job count is 140,000 jobs. Now remember, we want to see the

jobs. Now remember, we want to see the breakdown of what countries don't

breakdown of what countries don't mention a degree requirement in the job

mention a degree requirement in the job posting. So, I'm going to take job no

posting. So, I'm going to take job no degree mentioned and put it into the

degree mentioned and put it into the legend. I'm going to change this to no

legend. I'm going to change this to no degree mentioned. And now, scrolling

degree mentioned. And now, scrolling back over the United States, we can see

back over the United States, we can see that it's false for 109,000 values and

that it's false for 109,000 values and then it is true for 30,000 values. And

then it is true for 30,000 values. And so, I may I didn't update the title to

so, I may I didn't update the title to keep us on track and update it to which

keep us on track and update it to which countries don't mention degrees in job

countries don't mention degrees in job postings. Going back to that build

postings. Going back to that build visual, there's one other field that's

visual, there's one other field that's very common that's in basically every

very common that's in basically every single visualization and that's this

single visualization and that's this tool tips. We can add extra fields to

tool tips. We can add extra fields to this tool tips to appear. If I wanted

this tool tips to appear. If I wanted to, I could take that salary year

to, I could take that salary year average, drag it into here, make it into

average, drag it into here, make it into something like median, and then change

something like median, and then change this to median yearly salary. And now

this to median yearly salary. And now whenever I scroll over this, I can see

whenever I scroll over this, I can see that when there's no mention of a

that when there's no mention of a degree, the salary is $104,000

degree, the salary is $104,000 on median. But when there is a mention

on median. But when there is a mention of degree, it's 113,000. So it's a

of degree, it's 113,000. So it's a little bit higher whenever we do mention

little bit higher whenever we do mention a degree in there. Pretty interesting

a degree in there. Pretty interesting insight.

Next, let's get into the field map. Like I mentioned in the beginning, I'm not

I mentioned in the beginning, I'm not really a fan of this, so we're going to

really a fan of this, so we're going to kind of rush through it just so you're

kind of rush through it just so you're familiar with it and understand it. But

familiar with it and understand it. But as far as the capabilities, as I can see

as far as the capabilities, as I can see from this, or as you can see from this,

from this, or as you can see from this, I don't get a lot of insights out of it.

I don't get a lot of insights out of it. Back in our canvas, I'm going to go

Back in our canvas, I'm going to go ahead and insert a filled map. I'm going

ahead and insert a filled map. I'm going to start from scratch and not copy and

to start from scratch and not copy and paste just to make sure that you get it

paste just to make sure that you get it down of what we're actually doing here.

down of what we're actually doing here. We're going to use location field. And

We're going to use location field. And for specifically for that, we're going

for specifically for that, we're going to use job country. I'm going to go

to use job country. I'm going to go ahead and call this country. Next, right

ahead and call this country. Next, right underneath it is the legend. And as we

underneath it is the legend. And as we had in that final one, I can go ahead

had in that final one, I can go ahead and put job country into here and

and put job country into here and putting this into focus mode. This is

putting this into focus mode. This is giving me all sorts of colors. Anyway,

giving me all sorts of colors. Anyway, if I scroll over it, I can see things

if I scroll over it, I can see things like the tool tips to providing that it

like the tool tips to providing that it is what the country is. The other field

is what the country is. The other field in here that I wanted to do if tool tips

in here that I wanted to do if tool tips again, I could drag in that salary or

again, I could drag in that salary or average into here. Make that median and

average into here. Make that median and call that median yearly salary USD. Now,

call that median yearly salary USD. Now, when I scroll over to United States, I

when I scroll over to United States, I get that as well. Anyway, what I would

get that as well. Anyway, what I would hope would be more intuitive out of

hope would be more intuitive out of this, like something like the legend.

this, like something like the legend. Instead, let's say I wanted to color it

Instead, let's say I wanted to color it in like a scheme or a gradient using

in like a scheme or a gradient using something like the salary year average

something like the salary year average column. Now, it's going to go ahead and

column. Now, it's going to go ahead and color it, but it basically gives

color it, but it basically gives distinct colors for all the different

distinct colors for all the different values. Doesn't also even do aggregation

values. Doesn't also even do aggregation in the legend. Basically, it's a hot

in the legend. Basically, it's a hot mess. Not a fan of this bad boy. So, I'm

mess. Not a fan of this bad boy. So, I'm going to go ahead and put country back

going to go ahead and put country back in here. update the title to where our

in here. update the title to where our job postings globally. And we're going

job postings globally. And we're going to call it a day with this one.

to call it a day with this one. Now, where field maps fail, this is

Now, where field maps fail, this is where I like RGis. They come in handy

where I like RGis. They come in handy and they actually satisfies what I need.

and they actually satisfies what I need. In this case, we're going to go through

In this case, we're going to go through and actually make based on median salary

and actually make based on median salary what is the different color coding or

what is the different color coding or the color scheme necessary to actually

the color scheme necessary to actually get it to visually indicate what is the

get it to visually indicate what is the highest salary. So, back in our canvas,

highest salary. So, back in our canvas, I'm going to go ahead down here under

I'm going to go ahead down here under ArcJS for PowerBI. I'm going to insert

ArcJS for PowerBI. I'm going to insert it in. We're going to go into focus mode

it in. We're going to go into focus mode so we can actually read this. Now, this

so we can actually read this. Now, this is the main issue we're going to have

is the main issue we're going to have with ARGIS.

with ARGIS. We don't need to create a sign in. We're

We don't need to create a sign in. We're going to be able to go in and actually

going to be able to go in and actually continue as guest. But, as you notice,

continue as guest. But, as you notice, there is a sign in here. So, if we're

there is a sign in here. So, if we're going to go forward with sharing a

going to go forward with sharing a dashboard, specifically in the PowerBI

dashboard, specifically in the PowerBI service with an ARGIS visual, we're not

service with an ARGIS visual, we're not going to be able to do it unless your

going to be able to do it unless your company or you pay the thousands of

company or you pay the thousands of dollars to have an Argis subscription.

dollars to have an Argis subscription. Long story short, if you're just like a

Long story short, if you're just like a student or somebody like me, a solo

student or somebody like me, a solo entrepreneur, you can, yeah, use this to

entrepreneur, you can, yeah, use this to make graphs and visuals for you only,

make graphs and visuals for you only, but as soon as you want to start

but as soon as you want to start distributing to somebody else, you're

distributing to somebody else, you're going to have to pay a heck of a price.

going to have to pay a heck of a price. This is a report that I built that had

This is a report that I built that had an ArcJS for PowerBI in it that I

an ArcJS for PowerBI in it that I uploaded to the PowerBI service.

uploaded to the PowerBI service. Whenever I actually went to it and tried

Whenever I actually went to it and tried to use it with ARG uh ARGIS, it says

to use it with ARG uh ARGIS, it says this map does not meet the requirements

this map does not meet the requirements for publish reports. So, it's proof that

for publish reports. So, it's proof that you can't use it unless you're paying

you can't use it unless you're paying for it. Anyway, very similar. We can use

for it. Anyway, very similar. We can use latitude or longitude or the location.

latitude or longitude or the location. So, I'm going to drag job country into

So, I'm going to drag job country into that location column. And the visual

that location column. And the visual does take a second or two to load, but

does take a second or two to load, but loads nonetheless. and we'll change

loads nonetheless. and we'll change location to country. Then scrolling on

location to country. Then scrolling on down below latitude, longitude, we have

down below latitude, longitude, we have size and also color, time, and then also

size and also color, time, and then also tool tips and join layer. Oh, and find

tool tips and join layer. Oh, and find similar. We're going to just focus

similar. We're going to just focus mainly on size and color. Now, I'm going

mainly on size and color. Now, I'm going to start with the size and put salary

to start with the size and put salary year average into here. And in this

year average into here. And in this case, I'm going to change it to median.

case, I'm going to change it to median. Notice it is doing because I did size,

Notice it is doing because I did size, it's going to do size of the bubble

it's going to do size of the bubble sizes. And this thing, I'll be honest, I

sizes. And this thing, I'll be honest, I can't really read it. We can adjust it

can't really read it. We can adjust it later. But that's why we're actually

later. But that's why we're actually going to shift this one down into color

going to shift this one down into color as this is much more intuitive. Although

as this is much more intuitive. Although I don't like the color scheme of this.

I don't like the color scheme of this. This is much more intuitive of where are

This is much more intuitive of where are the higher salaries compared to

the higher salaries compared to everybody else. I'll change this to

everybody else. I'll change this to median yearly salary USD. Now, whenever

median yearly salary USD. Now, whenever I scroll over it, it's not providing

I scroll over it, it's not providing anything. Probably got to put this into

anything. Probably got to put this into tool tips. And then I'm going to change

tool tips. And then I'm going to change this to median yearly salary USD. Now

this to median yearly salary USD. Now scrolling over this, we don't get a

scrolling over this, we don't get a country anymore, but at least we're

country anymore, but at least we're getting the median value, which isn't

getting the median value, which isn't correct. What's going on here? I still

correct. What's going on here? I still have it on sum. I'm an idiot. I should

have it on sum. I'm an idiot. I should have it on median. Okay, median's out in

have it on median. Okay, median's out in the US. Looking a lot like more like the

the US. Looking a lot like more like the value that we need to have. Job country

value that we need to have. Job country is not appearing on here. So apparently

is not appearing on here. So apparently I got to drag this also into the tool

I got to drag this also into the tool tip. And now I'm getting country and

tip. And now I'm getting country and also median year. Okay, so that's

also median year. Okay, so that's basically all the formatting we're going

basically all the formatting we're going to do outside of here. The other thing

to do outside of here. The other thing we can do now to start formatting,

we can do now to start formatting, especially the color and how it's all

especially the color and how it's all set up is over here on this lefth hand

set up is over here on this lefth hand menu. This expand and collapses. The

menu. This expand and collapses. The next one gets into the layers. We can

next one gets into the layers. We can see a breakdown of how they're actually

see a breakdown of how they're actually breaking up the colors. And then we can

breaking up the colors. And then we can also go into symbology to change if we

also go into symbology to change if we want how we're doing the coloring by job

want how we're doing the coloring by job country. We don't want to mess with

country. We don't want to mess with that. We want actually go into the style

that. We want actually go into the style options. We're not going to change the

options. We're not going to change the shape cuz we're not doing shape, right?

shape cuz we're not doing shape, right? We're doing color. We're going to change

We're doing color. We're going to change this. And personally, I like the blues

this. And personally, I like the blues and I like this blue three to where

and I like this blue three to where higher values are darker in color.

higher values are darker in color. Closing this out just to inspect from

Closing this out just to inspect from it. Okay, this is getting what I want.

it. Okay, this is getting what I want. Other things to note from this menu

Other things to note from this menu besides just this layers aspect, I can

besides just this layers aspect, I can also go and change the different base

also go and change the different base maps that I have. I could change it into

maps that I have. I could change it into a dark gray canvas instead. And I really

a dark gray canvas instead. And I really liking that. We'll leave it light gray.

liking that. We'll leave it light gray. Then they have other things like a

Then they have other things like a selection tool, a search tool, and then

selection tool, a search tool, and then also do with further analysis. We're not

also do with further analysis. We're not going to go any further into that. I

going to go any further into that. I feel like this is as good as we're going

feel like this is as good as we're going to get with this, and this is all we

to get with this, and this is all we need to know for this type of visual.

need to know for this type of visual. Also, I forgot a title. Where are the

Also, I forgot a title. Where are the highest paying jobs? So, don't forget to

highest paying jobs? So, don't forget to put that in. So, that is an intro into

put that in. So, that is an intro into map visuals. you now have some practice

map visuals. you now have some practice problems to go through and get more

problems to go through and get more familiar with these different maps. In

familiar with these different maps. In our next lesson, we're going to be

our next lesson, we're going to be covering our final portion on different

covering our final portion on different charts you can build in PowerBI. It'll

charts you can build in PowerBI. It'll be focused on uncommon charts. So, it's

be focused on uncommon charts. So, it's going to be rapid pace, basically just

going to be rapid pace, basically just getting you an intro into what maps are

getting you an intro into what maps are also available inside the PowerBI

also available inside the PowerBI service before we move on to other

service before we move on to other greater things like tables, slicers, and

greater things like tables, slicers, and whatnot. With that, I'll see you there.

Welcome to this lesson on uncommon charts. Basically, there's a fitting

charts. Basically, there's a fitting title because we covered common charts.

title because we covered common charts. Now, we're going to cover some uncommon

Now, we're going to cover some uncommon ones. Now, there's actually like a dozen

ones. Now, there's actually like a dozen other charts that we haven't covered

other charts that we haven't covered yet. And actually, I'm going to just

yet. And actually, I'm going to just cover them briefly here at the beginning

cover them briefly here at the beginning so that way you're aware of them, but

so that way you're aware of them, but for this, we're going to be focusing on

for this, we're going to be focusing on three main charts. And well, for that,

three main charts. And well, for that, let's jump into the PowerBI.

let's jump into the PowerBI. Specifically, we're going to be building

Specifically, we're going to be building a ribbon chart and then what's known as

a ribbon chart and then what's known as a waterfall chart. And then finally,

a waterfall chart. And then finally, this bad boy down here is a funnel

this bad boy down here is a funnel chart. These are all pretty easy to

chart. These are all pretty easy to build, so we should be pretty quick to

build, so we should be pretty quick to cover them. Now, going into our

cover them. Now, going into our notebook, let's look at some other uncom

notebook, let's look at some other uncom uncommon types that we're not going to

uncommon types that we're not going to even get into in this video or even for

even get into in this video or even for the remainder of this course. If I go to

the remainder of this course. If I go to the insert tab, I have this section on

the insert tab, I have this section on AI visuals. They have Q&A, key

AI visuals. They have Q&A, key influencers, decomposition tree, and

influencers, decomposition tree, and narrative. You can also access them down

narrative. You can also access them down here in the actual visualization pane.

here in the actual visualization pane. Anyway, these AI features or AI features

Anyway, these AI features or AI features I should put in quotes are really bad

I should put in quotes are really bad and I don't find any use out of them.

and I don't find any use out of them. Here I just click Q&A and let's just

Here I just click Q&A and let's just type in a question that I think it maybe

type in a question that I think it maybe another way on the answer. But it says,

another way on the answer. But it says, "What is the median salary?" Oh, I

"What is the median salary?" Oh, I didn't spell right of data analyst. I'll

didn't spell right of data analyst. I'll go ahead and type that. And yeah, um

go ahead and type that. And yeah, um yeah, it doesn't it doesn't even give us

yeah, it doesn't it doesn't even give us a visual. I cannot stand this Q&A

a visual. I cannot stand this Q&A feature or really any of the AI map

feature or really any of the AI map visuals and so I'm not going to go over

visuals and so I'm not going to go over them and I'm not going to recommend

them and I'm not going to recommend them. Next on this insert tab is power

them. Next on this insert tab is power platforms and this you can create uh

platforms and this you can create uh pageionated reports basically breakdown

pageionated reports basically breakdown of reports that you want to send to

of reports that you want to send to somebody else. You can integrate Power

somebody else. You can integrate Power Apps or even Power Automate. Both Power

Apps or even Power Automate. Both Power Apps and Power Automate are great tools

Apps and Power Automate are great tools to dive into further after this, but

to dive into further after this, but frankly for the basics of PowerBI, it's

frankly for the basics of PowerBI, it's beyond the scope of this. And so we're

beyond the scope of this. And so we're not going to be going into any of this

not going to be going into any of this for this course. And on that note,

for this course. And on that note, related looking down here in the

related looking down here in the visualization pane, they also have

visualization pane, they also have something like the R script visual and

something like the R script visual and the Python script visual. Anyway, if I

the Python script visual. Anyway, if I drag something or I create something

drag something or I create something like a Python visual, yeah, we can put

like a Python visual, yeah, we can put some values in here, but overall, as you

some values in here, but overall, as you can see, you have to be able to write in

can see, you have to be able to write in for Python, you have to be able to write

for Python, you have to be able to write in Python. For R, you have to be able to

in Python. For R, you have to be able to write in R. I'm assuming most of you

write in R. I'm assuming most of you don't have the capabilities to write in

don't have the capabilities to write in Python or R. And I honestly don't use

Python or R. And I honestly don't use Python or R unless I really need to into

Python or R unless I really need to into a PowerBI report. So, not going to

a PowerBI report. So, not going to recommend this either.

And with that, let's get into our first uncommon chart, which is a ribbon chart.

uncommon chart, which is a ribbon chart. Ribbon charts are pretty cool because

Ribbon charts are pretty cool because they show over specifically I like to

they show over specifically I like to use it on a time basis. This is quarter

use it on a time basis. This is quarter down here. Remember, we can use our

down here. Remember, we can use our drill down. I'll drill down one more

drill down. I'll drill down one more into month. You'll be able to see what

into month. You'll be able to see what is the top value over time. That's how

is the top value over time. That's how it's actually sorting it. The lowest

it's actually sorting it. The lowest value is at bottom. I'll be honest, this

value is at bottom. I'll be honest, this one is a little bit of a hot mess. This

one is a little bit of a hot mess. This is a little bit too many values on here.

is a little bit too many values on here. I could make this a little bit more

I could make this a little bit more readable by only having something like

readable by only having something like data scientist, engineer, and scientist

data scientist, engineer, and scientist on here to see what are the top salaries

on here to see what are the top salaries throughout the year. So, let's get into

throughout the year. So, let's get into creating this bad boy. I already started

creating this bad boy. I already started a new page called uncommon charts. You

a new page called uncommon charts. You need to go ahead and do the same. And

need to go ahead and do the same. And inside of here, I'm going to insert in a

inside of here, I'm going to insert in a ribbon chart. Put into that quadrant.

ribbon chart. Put into that quadrant. And I'm going to go into focus mode. For

And I'm going to go into focus mode. For the x-axis, I'm going to go ahead and

the x-axis, I'm going to go ahead and use a time series data. So, we're going

use a time series data. So, we're going to use that job posted date. Right now,

to use that job posted date. Right now, it's filtered to year. So remember,

it's filtered to year. So remember, we're going to use all the way to the

we're going to use all the way to the right these this drop down arrow right

right these this drop down arrow right here. And I'm going to go into quarter

here. And I'm going to go into quarter for the time being. Now, as far as the

for the time being. Now, as far as the y-axis, we're going to once again use

y-axis, we're going to once again use that median salary like we've been

that median salary like we've been doing. Change this from sum to median

doing. Change this from sum to median and rename this to median yearly salary

and rename this to median yearly salary USD. So now we can see even with just

USD. So now we can see even with just this, we can see that yeah, over the

this, we can see that yeah, over the year, oh, median salary has gone up

year, oh, median salary has gone up slightly. But now we want to break it

slightly. But now we want to break it down based on the job titles themselves.

down based on the job titles themselves. So, we'll take that job title short,

So, we'll take that job title short, throw it into the legend, change that to

throw it into the legend, change that to job title, and then bam, we can see over

job title, and then bam, we can see over the year what has happened here. It

the year what has happened here. It looks like senior data scientist came

looks like senior data scientist came out of top the end of the year with

out of top the end of the year with $152,000.

$152,000. Now, like I mentioned before, I'm not

Now, like I mentioned before, I'm not really a fan of this. I would probably

really a fan of this. I would probably more likely recommend something like a

more likely recommend something like a line chart for this. Although, even with

line chart for this. Although, even with this, there's just way too many lines on

this, there's just way too many lines on here. So, I'd probably want to filter it

here. So, I'd probably want to filter it down with the number of jobs that it has

down with the number of jobs that it has in here. But line chart, it's going to

in here. But line chart, it's going to be my preference over something like a

be my preference over something like a ribbon chart.

Next up is a waterfall chart. And it gets its name because it kind of looks

gets its name because it kind of looks like it goes step to step to step kind

like it goes step to step to step kind of like a waterfall does. But the point

of like a waterfall does. But the point of this type of visualization is to show

of this type of visualization is to show how portions of a final value are made

how portions of a final value are made up of things that can add and subtract

up of things that can add and subtract from it. In this case, we're showing

from it. In this case, we're showing what is based on the total salary for a

what is based on the total salary for a data analyst salary breakdown. This

data analyst salary breakdown. This darker blue bar is what they're getting

darker blue bar is what they're getting at the end. So, $80,000. And then as it

at the end. So, $80,000. And then as it goes through these lighter blue are the

goes through these lighter blue are the different things that add to the salary

different things that add to the salary and this uh gray is things that subtract

and this uh gray is things that subtract from the salary giving us our final

from the salary giving us our final total. This type of visualization is

total. This type of visualization is very common in financial roles. Now if

very common in financial roles. Now if you recall from our data we don't have

you recall from our data we don't have it in a manner actually we just need to

it in a manner actually we just need to go to table view. We don't have any data

go to table view. We don't have any data especially numerical data in a in a

especially numerical data in a in a format that's necessary to actually make

format that's necessary to actually make this type of chart. So we need to insert

this type of chart. So we need to insert some data in to create a chart like

some data in to create a chart like this. Back in the home tab I'm going to

this. Back in the home tab I'm going to go in and we're going to enter data.

go in and we're going to enter data. We're going to ahead and insert some

We're going to ahead and insert some values into here. Specifically for base

values into here. Specifically for base salary we'll say it starts at 75,000.

salary we'll say it starts at 75,000. Bonus is at 5,000. Stock options are at

Bonus is at 5,000. Stock options are at 7,000. Not too shabby. Benefit values is

7,000. Not too shabby. Benefit values is at 8,000. Taxes induction at -15,000.

at 8,000. Taxes induction at -15,000. I'm going to change these column names

I'm going to change these column names by double clicking on it. This will be

by double clicking on it. This will be the column of comp. This will be of

the column of comp. This will be of amount. I'm using this for our waterfall

amount. I'm using this for our waterfall chart. So, I'll just call this waterfall

chart. So, I'll just call this waterfall data. We'll go ahead and load this in.

data. We'll go ahead and load this in. Inside the data pane, I can see that

Inside the data pane, I can see that it's appearing now with a mountain comp.

it's appearing now with a mountain comp. I can also go into the table view.

I can also go into the table view. Selecting that table, we can see that

Selecting that table, we can see that it's in there. So, let's go ahead and

it's in there. So, let's go ahead and insert in that waterfall chart, going to

insert in that waterfall chart, going to put in that top quadrant. For the

put in that top quadrant. For the category, I'm going to go ahead and drag

category, I'm going to go ahead and drag in comp. And then we're not going to use

in comp. And then we're not going to use breakdown because I want to break down

breakdown because I want to break down any further. We're going to use the

any further. We're going to use the yaxis specifically. We'll drag in that

yaxis specifically. We'll drag in that for this. Right now, we'll just put it a

for this. Right now, we'll just put it a sum. These individual values could be

sum. These individual values could be broken down more, maybe with another

broken down more, maybe with another column, but that's beyond the scope of

column, but that's beyond the scope of this. We're not going to do it. We're

this. We're not going to do it. We're going to keep this simple. I'm going to

going to keep this simple. I'm going to open this up into focus mode. And the

open this up into focus mode. And the main thing that stands out to me is the

main thing that stands out to me is the coloring. Once again, I'm not a big fan

coloring. Once again, I'm not a big fan of different colorings. I do like that

of different colorings. I do like that the aspect of they're doing green for

the aspect of they're doing green for positive values, red for negative, and

positive values, red for negative, and blue for the final, but still that's

blue for the final, but still that's just too much in my opinion. So, I'm

just too much in my opinion. So, I'm going to go to format your visual and

going to go to format your visual and underneath here. I'm trying to find out

underneath here. I'm trying to find out where I can change the color. A little

where I can change the color. A little shortcut is I'm going to use the search

shortcut is I'm going to use the search bar up here and just type color. I'm

bar up here and just type color. I'm going scroll down until I find the

going scroll down until I find the colors that I wanted. Oh, there it is.

colors that I wanted. Oh, there it is. That's what I want to change right here.

That's what I want to change right here. For increase, I'm going to make it a

For increase, I'm going to make it a light blue. For decrease, I'm going to

light blue. For decrease, I'm going to make it a dark gray. And then for the

make it a dark gray. And then for the total, I'm just going to change it to

total, I'm just going to change it to this dark blue. All right. So, bam,

this dark blue. All right. So, bam, that's a waterfall chart. And like I

that's a waterfall chart. And like I mentioned, this is has a lot of

mentioned, this is has a lot of negatives to it in the fact that you

negatives to it in the fact that you usually don't get data that's in this

usually don't get data that's in this format. So, you need to use something

format. So, you need to use something like Power Query, which is going to be

like Power Query, which is going to be in the next chapter, in order to clean

in the next chapter, in order to clean data up and get it into this type of

data up and get it into this type of format. Makes an entire hassle.

Last one to cover is a funnel chart. And similar to the last chart, you have to

similar to the last chart, you have to have the data in a certain format in

have the data in a certain format in order to feed it in and actually be able

order to feed it in and actually be able to make or reconcile what's going on

to make or reconcile what's going on here. Anyway, the point of this this

here. Anyway, the point of this this funnel is to show how people or whatever

funnel is to show how people or whatever it may be goes through a funnel process.

it may be goes through a funnel process. So, in this case, this is job applicants

So, in this case, this is job applicants per stage. They those that view the

per stage. They those that view the posting is around 5,000. Those that

posting is around 5,000. Those that clicked apply, it was around 4K. started

clicked apply, it was around 4K. started the application around 3K, submitted 2K,

the application around 3K, submitted 2K, interviewed 1K, and hired 0K, which

interviewed 1K, and hired 0K, which going over the toolkit, looks like it's

going over the toolkit, looks like it's around 120. So, only 2.4% out of this

around 120. So, only 2.4% out of this 100% got into getting a job. That's what

100% got into getting a job. That's what funnel charts are great at showing. So,

funnel charts are great at showing. So, let's create this real quick. We're

let's create this real quick. We're going to throw in a funnel chart. Put

going to throw in a funnel chart. Put into focus mode. And we don't have any

into focus mode. And we don't have any data. We just need to enter that data.

data. We just need to enter that data. We'll put in first the value of view job

We'll put in first the value of view job postings at 5,000. clicked apply at

postings at 5,000. clicked apply at 3,800, started application at 2,900,

3,800, started application at 2,900, submitted at 2,200, interviewed at 500,

submitted at 2,200, interviewed at 500, and then hired at 120. We'll label the

and then hired at 120. We'll label the column names as stage, and then

column names as stage, and then applicants. Last thing we'll do is just

applicants. Last thing we'll do is just need to make sure we update the uh the

need to make sure we update the uh the table name, and then change it to funnel

table name, and then change it to funnel data. We'll go ahead and click load.

data. We'll go ahead and click load. Now, with our funnel data, I'm going to

Now, with our funnel data, I'm going to drag applicants or sorry, I'm going to

drag applicants or sorry, I'm going to drag stage into the category and then

drag stage into the category and then applicants into the values. Now, one

applicants into the values. Now, one thing I didn't like from last time is

thing I didn't like from last time is how it goes 54321 and then 0. Okay, that

how it goes 54321 and then 0. Okay, that I don't feel like that's descriptive

I don't feel like that's descriptive enough. So, under format your visual,

enough. So, under format your visual, I'm not even going to search for it. I'm

I'm not even going to search for it. I'm just going to search and I'm going to

just going to search and I'm going to type decimal. And we have value decimal

type decimal. And we have value decimal places. I'm going to put this as zero or

places. I'm going to put this as zero or sorry, not zero. We're going to put as

sorry, not zero. We're going to put as one. So, now this shows more of a

one. So, now this shows more of a breakdown of what's going on here. so we

breakdown of what's going on here. so we can actually see what's going on. And

can actually see what's going on. And that's the one thing I'm going to change

that's the one thing I'm going to change with this.

All right, one bonus section that I do want to add into this and that is on get

want to add into this and that is on get more visuals. What do I mean by that?

more visuals. What do I mean by that? Well, if you notice, we have these

Well, if you notice, we have these ellipses down here at the bottom of the

ellipses down here at the bottom of the visualization pane and it says, hey, get

visualization pane and it says, hey, get more visuals. I'm going to go ahead and

more visuals. I'm going to go ahead and click it and I can say I can import it

click it and I can say I can import it from file, remove visual, what not. Here

from file, remove visual, what not. Here I want to get more visuals. Now, this is

I want to get more visuals. Now, this is going to pop up and show us some

going to pop up and show us some different ways we can get other visuals.

different ways we can get other visuals. I'm going to go back real quick and call

I'm going to go back real quick and call out something. You may try this. If

out something. You may try this. If you're not logged in, if you don't have

you're not logged in, if you don't have an account, you don't you can't you're

an account, you don't you can't you're not signed in, you're not going to be

not signed in, you're not going to be able to go through and get more visuals

able to go through and get more visuals without actually signing in. If you're

without actually signing in. If you're not able to do that, don't worry about

not able to do that, don't worry about it. Not a big deal. We have some

it. Not a big deal. We have some practice problems using these, but I've

practice problems using these, but I've made them optional, so you don't need to

made them optional, so you don't need to necessarily do them. But I'm going to

necessarily do them. But I'm going to just go through and showcase what is

just go through and showcase what is capable what you're able to do with

capable what you're able to do with this. Anyway, we're going to go ahead

this. Anyway, we're going to go ahead and search for something specifically

and search for something specifically box and whisker chart. And it's good

box and whisker chart. And it's good whenever you're looking at any of these

whenever you're looking at any of these that you look at the ratings and that at

that you look at the ratings and that at least does have some ratings. This one

least does have some ratings. This one doesn't have any ratings. Makes me

doesn't have any ratings. Makes me question it. I've tried this one from

question it. I've tried this one from data scenarios. So, we're going to go

data scenarios. So, we're going to go with that. Now, for this, I have the

with that. Now, for this, I have the ability to now add this. I do want to

ability to now add this. I do want to call something out real quick. If I go

call something out real quick. If I go back, if I go into this other one, Box

back, if I go into this other one, Box and Whiskers Pro, they actually have

and Whiskers Pro, they actually have underneath it plans and pricings. And

underneath it plans and pricings. And you can get into some where depending on

you can get into some where depending on the users and where you want to publish

the users and where you want to publish it, but they are going to charge you for

it, but they are going to charge you for this. So, be careful of which ones you

this. So, be careful of which ones you choose if you want to get it charged or

choose if you want to get it charged or not. You'd have to enter in your credit

not. You'd have to enter in your credit card to do all that kind of stuff. So,

card to do all that kind of stuff. So, you're not going to get charged without

you're not going to get charged without knowing it, but it may come at a cost.

knowing it, but it may come at a cost. They may not display it unless you're

They may not display it unless you're paying. Anyway, I want this one. I'm

paying. Anyway, I want this one. I'm going to go ahead and click add. Now we

going to go ahead and click add. Now we notice uh underneath the divider here we

notice uh underneath the divider here we now have this box and whiskers chart

now have this box and whiskers chart which is a new one that we have

which is a new one that we have available. I put job title short into

available. I put job title short into the category and then put salary year

the category and then put salary year average into the sampling we want for

average into the sampling we want for this and salary average into the values

this and salary average into the values specifically. I don't want sum, I want

specifically. I don't want sum, I want the median. And then open this bad boy

the median. And then open this bad boy up so we can see it. Bam. Now I have a

up so we can see it. Bam. Now I have a visualization that was not previously

visualization that was not previously accessible to me via the standard ones

accessible to me via the standard ones in here and I can actually visualize

in here and I can actually visualize something for free. Pretty neat. Now I

something for free. Pretty neat. Now I have noticed with a lot of these the

have noticed with a lot of these the customization that you can actually do

customization that you can actually do with this. Yeah, there is a lot of

with this. Yeah, there is a lot of options. I'm not going to go into

options. I'm not going to go into customizing this one. The customization

customizing this one. The customization can get kind of limited and they can

can get kind of limited and they can sometimes be buggy because they're not

sometimes be buggy because they're not officially PowerBI approved all the

officially PowerBI approved all the time. So that is something to think in

time. So that is something to think in mind whenever you decide to go out and

mind whenever you decide to go out and get a visualization within the get more

get a visualization within the get more visuals. All right, it's now your turn

visuals. All right, it's now your turn to go through and build some of these

to go through and build some of these uncommon charts. We actually have some

uncommon charts. We actually have some practice problems as well that include

practice problems as well that include price problems on get more visuals if

price problems on get more visuals if you want to get experience with that.

you want to get experience with that. Once again, those portions of the

Once again, those portions of the practice problems will be optional

practice problems will be optional because you do have to have an account

because you do have to have an account login to sign in. So don't worry if you

can't accomplish it. In the next lesson, we're going to be jumping into tables

we're going to be jumping into tables and matrices. With that, I'll see you

and matrices. With that, I'll see you there.

In this video, we're going to be covering tables and also matrices. And

covering tables and also matrices. And especially if you have end users like

especially if you have end users like your boss, maybe that's more familiar

your boss, maybe that's more familiar with something like Excel. They're going

with something like Excel. They're going to from time to time request that you

to from time to time request that you put stuff into tables. Anyway, jumping

put stuff into tables. Anyway, jumping into the PowerBI file to show what we're

into the PowerBI file to show what we're going to be doing in this lesson. As we

going to be doing in this lesson. As we can see from that homepage, we've

can see from that homepage, we've covered everything we're going to cover

covered everything we're going to cover for charts. We're now on to other

for charts. We're now on to other visuals. for right now we're covering

visuals. for right now we're covering tables. Then we'll cover cards and next

tables. Then we'll cover cards and next slicers. Anyway, let's go into tables.

slicers. Anyway, let's go into tables. We're going to be building this table

We're going to be building this table which aggregates all the different key

which aggregates all the different key information from job postings that

information from job postings that contain a yearly salary. Tables are

contain a yearly salary. Tables are pretty nice cuz I can click on the

pretty nice cuz I can click on the different columns and I can sort them

different columns and I can sort them depending on what value is where. And we

depending on what value is where. And we can see something like this. We're data

can see something like this. We're data scientists at Netflix. We're getting

scientists at Netflix. We're getting $920,000.

$920,000. Gosh, full-time job. Anyway, we're also

Gosh, full-time job. Anyway, we're also going to be going through not just this

going to be going through not just this uh how to make the table itself, but

uh how to make the table itself, but also conditional formatting. That's what

also conditional formatting. That's what these blue data bars are right here and

these blue data bars are right here and then these icons right here. And we're

then these icons right here. And we're going to be using some quick measures to

going to be using some quick measures to make these stars. Now, after we cover

make these stars. Now, after we cover tables, we're going to go into matrices.

tables, we're going to go into matrices. If you're familiar with pivot tables in

If you're familiar with pivot tables in Excel, that's basically what this is.

Excel, that's basically what this is. This is allows us to do aggregation

This is allows us to do aggregation on certain columns. In this case, we're

on certain columns. In this case, we're going to do aggregation on the job title

going to do aggregation on the job title short column to see things like salary

short column to see things like salary and count. We can also have hierarchies

and count. We can also have hierarchies within it. In this case, I can go dive

within it. In this case, I can go dive into business analyst and I can look at

into business analyst and I can look at it in the different quarters as it

it in the different quarters as it progresses along. I use conditional

progresses along. I use conditional formatting for the bars. And then over

formatting for the bars. And then over here on the right hand side, we have job

here on the right hand side, we have job trends, which is using spark lines,

trends, which is using spark lines, which is the last thing we're actually

which is the last thing we're actually going to cover for this.

going to cover for this. So, let's start with creating this

So, let's start with creating this table. I'm going to come into create a

table. I'm going to come into create a blank page. I'm going to rename it.

blank page. I'm going to rename it. Actually, call it something creative

Actually, call it something creative like tables. And inside of here, I'm

like tables. And inside of here, I'm going to come in here and select the

going to come in here and select the table icon. We're going to extend it all

table icon. We're going to extend it all the way over and make it in the top

the way over and make it in the top half. Now, there's only one field well

half. Now, there's only one field well here, and that's the add data fields

here, and that's the add data fields right here for columns. So, we can come

right here for columns. So, we can come in here and start dragging things in.

in here and start dragging things in. We're going to start with just the job

We're going to start with just the job title short column, which shows those 10

title short column, which shows those 10 values. And then I actually want to see

values. And then I actually want to see what is the full job title. So, I'm

what is the full job title. So, I'm going to drag that next into here. After

going to drag that next into here. After this, I want the company information.

this, I want the company information. So, I'm put in company name. I want the

So, I'm put in company name. I want the salary information. So, we'll drag in

salary information. So, we'll drag in salary year average. Right now, it's

salary year average. Right now, it's doing aggregation. We'll fix that in a

doing aggregation. We'll fix that in a second. And then finally, the last

second. And then finally, the last column that we're going to drag in for

column that we're going to drag in for the time being is that job schedule

the time being is that job schedule type. Okay, getting back to that sum of

type. Okay, getting back to that sum of salary. We don't want to necessarily do

salary. We don't want to necessarily do a sum. There are some cases like this.

a sum. There are some cases like this. Oh my gosh, I can't even see this. I'm

Oh my gosh, I can't even see this. I'm going to open up the focus mode. There

going to open up the focus mode. There are this case where it is doing a sum

are this case where it is doing a sum and that's because there's multiple jobs

and that's because there's multiple jobs that fit this job title and company

that fit this job title and company name. So it sums up all the sellers. I

name. So it sums up all the sellers. I don't want to do an aggregation. So I'm

don't want to do an aggregation. So I'm going to click this down arrow right

going to click this down arrow right here and I'm going to say don't

here and I'm going to say don't summarize it. Now I'm going to go back

summarize it. Now I'm going to go back into the report so we can actually see

into the report so we can actually see it. All right, not too bad. I do have a

it. All right, not too bad. I do have a bunch of blank values for salary or

bunch of blank values for salary or average. I just want to see the postings

average. I just want to see the postings that have a yearly salary associated

that have a yearly salary associated with it. So, going into the filters pane

with it. So, going into the filters pane under salary year average, I'm going to

under salary year average, I'm going to say show items when the value is not

say show items when the value is not blank. And let's see if this works. Bam.

blank. And let's see if this works. Bam. Does work. Now, we're getting a bunch of

Does work. Now, we're getting a bunch of salary values in there. Okay, let's go

salary values in there. Okay, let's go back into focus mode and do some just

back into focus mode and do some just final cleanup. I'm going to adjust this

final cleanup. I'm going to adjust this column right here so we can see all

column right here so we can see all them. We need to adjust all these

them. We need to adjust all these different column titles and update to

different column titles and update to make it look a little more readable. Job

make it look a little more readable. Job title, job title, full, company, yearly

title, job title, full, company, yearly salary, and job type. Not too bad. Now,

salary, and job type. Not too bad. Now, with something like this, it's important

with something like this, it's important to understand if I put this in to a

to understand if I put this in to a visual and somebody wanted access to

visual and somebody wanted access to this data, remember, they can go to

this data, remember, they can go to those more options and then export the

those more options and then export the data and then they wanted it, they could

data and then they wanted it, they could have it in something like Excel and if

have it in something like Excel and if they're more of a master of it, they can

they're more of a master of it, they can use it there.

use it there. All right, let's get back to our table.

All right, let's get back to our table. There's one thing I want to do to this

There's one thing I want to do to this to spice it up. not necessarily

to spice it up. not necessarily conditional formatting just yet, but

conditional formatting just yet, but instead looking at what our final table

instead looking at what our final table is, I want to add these stars in here

is, I want to add these stars in here and be able to showcase based on a

and be able to showcase based on a certain salary, if it meets my salary,

certain salary, if it meets my salary, it being five stars, and then down to

it being five stars, and then down to zero stars. Now, in order to do this,

zero stars. Now, in order to do this, back in our table, I'm going to go to

back in our table, I'm going to go to that modeling tab, and what we're going

that modeling tab, and what we're going to use is a quick measure. Now, measures

to use is a quick measure. Now, measures in general, remember, we're going to go

in general, remember, we're going to go really in detail in it in chapter 4, but

really in detail in it in chapter 4, but this we're going to give a sneak peek

this we're going to give a sneak peek and see in the capabilities of something

and see in the capabilities of something like quick measures, which honestly

like quick measures, which honestly we're going to come to find out it's

we're going to come to find out it's quite limited. These quick measures

quite limited. These quick measures generate DAX in order to satisfy our

generate DAX in order to satisfy our need. So, we could do things like

need. So, we could do things like aggregation, filtering, time

aggregation, filtering, time intelligence, totals, but I'm want we're

intelligence, totals, but I'm want we're going to go down to this one here of

going to go down to this one here of text and specifically star ratings. The

text and specifically star ratings. The first thing we need to do, it's pretty

first thing we need to do, it's pretty self-intuitive. We need a base value,

self-intuitive. We need a base value, and this is the value want to convert

and this is the value want to convert into a star rating. Remember, we're

into a star rating. Remember, we're going to be using this from that salary

going to be using this from that salary year average column. We want to base it

year average column. We want to base it off of that. So, I'm going to drag it

off of that. So, I'm going to drag it into add data. We don't want this to be

into add data. We don't want this to be sum, right? We want this to be median.

sum, right? We want this to be median. So, I'm going to go ahead and change

So, I'm going to go ahead and change this underneath here. And now we can do

this underneath here. And now we can do there. It's going to provide what are

there. It's going to provide what are the number of stars we want and we want

the number of stars we want and we want what is the value for the lowest star

what is the value for the lowest star rating. Well, I'm going to say, hey, my

rating. Well, I'm going to say, hey, my lowest point that I want to give a star

lowest point that I want to give a star for any star for, I'm going to say let's

for any star for, I'm going to say let's say 75,000. And then something that

say 75,000. And then something that would exceed my expectations in salary,

would exceed my expectations in salary, I'm going to give that of 150,000. So,

I'm going to give that of 150,000. So, I'm going go ahead and click add. And as

I'm going go ahead and click add. And as we can see, I'm going to uh hide this

we can see, I'm going to uh hide this off to the side. It generated all the

off to the side. It generated all the different DAXs necessary. This is up in

different DAXs necessary. This is up in the formula bar up at the top and

the formula bar up at the top and conveniently it ended up putting it into

conveniently it ended up putting it into or unconveniently it ended up putting it

or unconveniently it ended up putting it into our funnel data table. Now this is

into our funnel data table. Now this is actually we don't I don't want in the

actually we don't I don't want in the funnel data table and also need to

funnel data table and also need to change its name. So we'll do both of

change its name. So we'll do both of those things. First is I want to change

those things. First is I want to change the home table to job posting flat and

the home table to job posting flat and then the name up here to salary star

then the name up here to salary star rating. If you notice it updated in the

rating. If you notice it updated in the formula. All right. All right, I'm going

formula. All right. All right, I'm going to go ahead and close out of that

to go ahead and close out of that formula. Click into here. Now, for this

formula. Click into here. Now, for this table, we want to add that in. So, I'm

table, we want to add that in. So, I'm going to take this salary star rating as

going to take this salary star rating as designated by this measures icon next to

designated by this measures icon next to it. We're going to drag it and put it

it. We're going to drag it and put it right in that first column. Opening up

right in that first column. Opening up focus mode so we can actually see what's

focus mode so we can actually see what's going on here. And adjusting the column

going on here. And adjusting the column so we can actually read it. Okay, this

so we can actually read it. Okay, this is looking like it's working. So,

is looking like it's working. So, something like 32,000 doesn't get any

something like 32,000 doesn't get any stars. whereas something like 163,000

stars. whereas something like 163,000 does get five stars because it's above

does get five stars because it's above that 150,000. Anyway, I really like the

that 150,000. Anyway, I really like the star rating, especially that quick

star rating, especially that quick measure. It makes it quick and easy to

measure. It makes it quick and easy to create a measure like this.

create a measure like this. Unfortunately, going back to quick

Unfortunately, going back to quick measures and looking at it, honestly, I

measures and looking at it, honestly, I don't get a lot of value out of any of

don't get a lot of value out of any of these other ones. I've tried to use it

these other ones. I've tried to use it before with the exception of this. And

before with the exception of this. And really, I get more value out of writing

really, I get more value out of writing my own DAX to create measures, which

my own DAX to create measures, which like I said, we're going to go into it

like I said, we're going to go into it more in chapter 4. So, just stay tuned

more in chapter 4. So, just stay tuned for that.

for that. Next, let's get into building our

Next, let's get into building our matrix. And with this, very similar in

matrix. And with this, very similar in format in that we want to do aggregation

format in that we want to do aggregation of the job titles, but we're going to do

of the job titles, but we're going to do finding that of count and then also the

finding that of count and then also the different hourly and median salaries.

different hourly and median salaries. We're not going to be inserting in the

We're not going to be inserting in the trend lines just yet or the conditional

trend lines just yet or the conditional formatting. We'll be doing that after we

formatting. We'll be doing that after we set up the um matrix. So, inside of our

set up the um matrix. So, inside of our canvas, I'm going to go ahead and insert

canvas, I'm going to go ahead and insert in a matrix. I'm going put the job title

in a matrix. I'm going put the job title short into the rows. And then they have

short into the rows. And then they have columns, but really we're going to want

columns, but really we're going to want to do values. But let me just show you

to do values. But let me just show you what's going on with this uh for I could

what's going on with this uh for I could put something like columns, the job

put something like columns, the job countries in the columns. And then let's

countries in the columns. And then let's say I wanted like the count, which we

say I wanted like the count, which we are going to keep. So, I'm going to drag

are going to keep. So, I'm going to drag job tile short to the values. And we're

job tile short to the values. And we're going to do count here. Now we have

going to do count here. Now we have based on this right columns are along or

based on this right columns are along or the countries rows are the job tile

the countries rows are the job tile short. And then inside of here we have

short. And then inside of here we have all the different counts depending on

all the different counts depending on the country with something as much as

the country with something as much as country and how wide this table is. Now

country and how wide this table is. Now not finding much use out of that. So I'm

not finding much use out of that. So I'm actually going to just remove job

actually going to just remove job country. And we still have that job

country. And we still have that job count in there. Other values we want to

count in there. Other values we want to drag into there are that yearly salary

drag into there are that yearly salary and hourly salary. adjusting them both

and hourly salary. adjusting them both to be those median values. Then I'm

to be those median values. Then I'm going to go ahead and put this into into

going to go ahead and put this into into focus mode and then also cleaned up all

focus mode and then also cleaned up all the columns to job title, job count,

the columns to job title, job count, yearly salary, and hourly salary. Now

yearly salary, and hourly salary. Now remember, right, this is a matrix. So we

remember, right, this is a matrix. So we can create a hierarchy within here. And

can create a hierarchy within here. And as we did previously, I use the, as I

as we did previously, I use the, as I showed previously, I've used the

showed previously, I've used the quarter. So I'm actually going to grab

quarter. So I'm actually going to grab quarter within underneath job posted

quarter within underneath job posted date. I'm going drag it into the rows.

date. I'm going drag it into the rows. Now, for this expand icon right here, I

Now, for this expand icon right here, I can see based on a quarter how it breaks

can see based on a quarter how it breaks down. Some quick formatting things to

down. Some quick formatting things to note on this matrix. What we can also do

note on this matrix. What we can also do going into format visual and then under

going into format visual and then under visual, you can change things like the

visual, you can change things like the subtotals. In this case, it's only

subtotals. In this case, it's only giving us column sub totals or sorry,

giving us column sub totals or sorry, row subtotals. So, I can toggle it on

row subtotals. So, I can toggle it on and off what I want it. This case, it's

and off what I want it. This case, it's not going to give us those column sub

not going to give us those column sub totals. The other thing is what happens

totals. The other thing is what happens if we want to format these numbers

if we want to format these numbers further. Let's take specifically the job

further. Let's take specifically the job count. So I can go down here to specific

count. So I can go down here to specific column have job count selected. And then

column have job count selected. And then underneath values we can actually change

underneath values we can actually change the display units that we want cuz right

the display units that we want cuz right now 12,000 or 128,994 that's kind of

now 12,000 or 128,994 that's kind of unreadable. So what I can do is I can

unreadable. So what I can do is I can change that to thousands and then for

change that to thousands and then for the decimal place put in a zero.

the decimal place put in a zero. Similarly, I can do the same for

Similarly, I can do the same for something like the yearly salary as

something like the yearly salary as well. I could change that to thousands.

well. I could change that to thousands. And then that one's also more readable.

And then that one's also more readable. For hourly salary, I'm not getting much

For hourly salary, I'm not getting much value out of those two decimal places.

value out of those two decimal places. So, we're just going to change that to

So, we're just going to change that to zero. Bam. Much more readable the data

zero. Bam. Much more readable the data that's coming out of this. Now, with

that's coming out of this. Now, with these matrices, right, especially since

these matrices, right, especially since we did that hierarchy of the quarter

we did that hierarchy of the quarter underneath here, you also have these

underneath here, you also have these drill downs up here for you to drill

drill downs up here for you to drill down into. And if you wanted to, you can

down into. And if you wanted to, you can expand all the way down by using the

expand all the way down by using the navigation up there in the top right

navigation up there in the top right hand corner. Overall, I like to usually

hand corner. Overall, I like to usually just dive into one myself and then dive

just dive into one myself and then dive further as necessary.

Next, let's get into some conditional formatting to spice these visuals up and

formatting to spice these visuals up and draw people's attention to where we want

draw people's attention to where we want them to actually look for this. We're

them to actually look for this. We're going to start with our matrix first,

going to start with our matrix first, and I want to do job count. What we're

and I want to do job count. What we're going to do is click this do uh down

going to do is click this do uh down arrow. And you notice right here we have

arrow. And you notice right here we have conditional formatting. There's a few

conditional formatting. There's a few options. Background color, font color,

options. Background color, font color, data bars, icons, and web URL. Let's

data bars, icons, and web URL. Let's start with background color first. Now,

start with background color first. Now, we can get it to do based on a rule. I'm

we can get it to do based on a rule. I'm typically not a fan actually rules of

typically not a fan actually rules of like, hey, set it at with this value be

like, hey, set it at with this value be a certain color. Instead, I'm going to

a certain color. Instead, I'm going to go to this of gradient, and it

go to this of gradient, and it automatically picks up that we're doing

automatically picks up that we're doing a count of the job title. And it's

a count of the job title. And it's setting the lowest value at this light

setting the lowest value at this light blue and this other one the maximum at a

blue and this other one the maximum at a darker blue. Kind of like it. We'll go

darker blue. Kind of like it. We'll go with it. And bam. Key things to think

with it. And bam. Key things to think about though when you use these blue

about though when you use these blue type colors. I'll be honest, it's a

type colors. I'll be honest, it's a little bit harder to read the numbers

little bit harder to read the numbers that's going on right here. So if I

that's going on right here. So if I want, I can go back into conditional

want, I can go back into conditional formatting for that background color and

formatting for that background color and change this to be slightly less dark.

change this to be slightly less dark. And I think the numbers are a little

And I think the numbers are a little more readable in that matter. All right.

more readable in that matter. All right. Next one to notice for the conditional

Next one to notice for the conditional formatting. We'll go to yearly salatary

formatting. We'll go to yearly salatary conditional formatting. We're going to

conditional formatting. We're going to be doing we'll skip font colors for now.

be doing we'll skip font colors for now. We're going to go to data bars. In this

We're going to go to data bars. In this the format style is data bars. And so

the format style is data bars. And so the only option available available.

the only option available available. We're already doing the median yearly

We're already doing the median yearly salary for this. And in it they have we

salary for this. And in it they have we can specify basically the color. So

can specify basically the color. So positive bars are this blue. Negative

positive bars are this blue. Negative bars you can make if you want it to be

bars you can make if you want it to be red and the axis black. What I'm going

red and the axis black. What I'm going to do, it's all our values are positive.

to do, it's all our values are positive. So, I'm going to go ahead and just

So, I'm going to go ahead and just adjust this to a lighter blue color so

adjust this to a lighter blue color so that way we can still read those numbers

that way we can still read those numbers behind it. Click okay. And bam, we get

behind it. Click okay. And bam, we get this. I think it's still a little too

this. I think it's still a little too dark. Unfortunately, we have to go back

dark. Unfortunately, we have to go back in all the way to data bars and then

in all the way to data bars and then select the color I want. I'm going to do

select the color I want. I'm going to do this light blue one. Click okay. All

this light blue one. Click okay. All right. That's a lot more readable. I'm

right. That's a lot more readable. I'm also going to go through and do this

also going to go through and do this real quick for the hourly column as

real quick for the hourly column as well. Making it at that light blue. then

well. Making it at that light blue. then applying it. Bam. Looks good. This is

applying it. Bam. Looks good. This is all we can do for this matrix. So, let's

all we can do for this matrix. So, let's switch on over to our table. I went to

switch on over to our table. I went to focus mode. So, let's start with that

focus mode. So, let's start with that year yearly salary. We're going to

year yearly salary. We're going to eventually put data bars, but I just

eventually put data bars, but I just want to show conditional formatting. We

want to show conditional formatting. We could do something like a font color

could do something like a font color where it does a gradient from where it

where it does a gradient from where it needs to go and where it needs to be.

needs to go and where it needs to be. Overall font color. I know the coloring

Overall font color. I know the coloring is probably pretty bad on this. In

is probably pretty bad on this. In general, I don't I don't get a lot of

general, I don't I don't get a lot of value at font color, so I don't use that

value at font color, so I don't use that very often. So, what I'll do is I'll

very often. So, what I'll do is I'll open it up. go to remove conditional

open it up. go to remove conditional formatting and we'll select remove font

formatting and we'll select remove font coloring and go back in to add that

coloring and go back in to add that conditional formatting for data bars

conditional formatting for data bars specifying that that positive bar we

specifying that that positive bar we want that that light blue color. All

want that that light blue color. All right, the next thing with this let's

right, the next thing with this let's give it some icons specifically

give it some icons specifically depending on a job type that I'm trying

depending on a job type that I'm trying to get to or that I want maybe I want to

to get to or that I want maybe I want to be able to signify via an icon to cue my

be able to signify via an icon to cue my eyes into it. So I'll go to job type go

eyes into it. So I'll go to job type go into conditional formatting and for this

into conditional formatting and for this one select icons. Right now the format

one select icons. Right now the format that style that's using is rules and so

that style that's using is rules and so that's why it's using the is whatever

that's why it's using the is whatever text and then assigning it a certain

text and then assigning it a certain icon. We can specify we can specify

icon. We can specify we can specify where we want the icon left of data icon

where we want the icon left of data icon only or right of data. Right now we'll

only or right of data. Right now we'll just leave it left of data. And you can

just leave it left of data. And you can even change the type. They have a few

even change the type. They have a few different options you can choose from.

different options you can choose from. We're going to end up changing it to

We're going to end up changing it to we're going to change it to this one

we're going to change it to this one right here with these circles and these

right here with these circles and these up and downs. Basically, I want it to be

up and downs. Basically, I want it to be green whenever if value is. In our case,

green whenever if value is. In our case, I want it to be uh when it's full-time.

I want it to be uh when it's full-time. Part-time will be a yellow. I'm not

Part-time will be a yellow. I'm not really a fan of it. And then red will be

really a fan of it. And then red will be I don't want to I don't even want to

I don't want to I don't even want to look at it. That will be for jobs that

look at it. That will be for jobs that are contractors. Go ahead and click

are contractors. Go ahead and click okay. And now we got visual indications

okay. And now we got visual indications of the different types of jobs along

of the different types of jobs along with this. Now, there's one other type

with this. Now, there's one other type of conditional format we can do, and

of conditional format we can do, and that's with web URLs. And we're actually

that's with web URLs. And we're actually going to get to that in our practice

going to get to that in our practice problems. So for those that supported

problems. So for those that supported the course, you're going to get to that

the course, you're going to get to that eventually.

All right, the last thing we want to do is add a spark line. And that's what

is add a spark line. And that's what this is at the end of this that shows

this is at the end of this that shows it's called job trends, but it's the

it's called job trends, but it's the count of jobs over time. We can see very

count of jobs over time. We can see very quickly that there's a trend over time

quickly that there's a trend over time where we have this dip down in October,

where we have this dip down in October, November area. Anyway, I like using this

November area. Anyway, I like using this especially in these it's only about 10

especially in these it's only about 10 columns or sorry 10 rows here. Great way

columns or sorry 10 rows here. Great way to use it. But as far as our table, not

to use it. But as far as our table, not really a big fan of using it inside of

really a big fan of using it inside of all these different values. It's sort of

all these different values. It's sort of cumbersome at that point. So inside of

cumbersome at that point. So inside of what we have built some forward, I'm

what we have built some forward, I'm going to go back into focus mode and

going to go back into focus mode and since the matrix is selected, I'm going

since the matrix is selected, I'm going to go to insert. Add a spark line is not

to go to insert. Add a spark line is not grayed out. We can actually add a spark

grayed out. We can actually add a spark line. For this, we're going to do the

line. For this, we're going to do the counts of jobs. Like usual, we're going

counts of jobs. Like usual, we're going to use that job title short column in

to use that job title short column in order to calculate that. So I'll select

order to calculate that. So I'll select job title short count. And then finally,

job title short count. And then finally, we'll also do for that X-axis. We want

we'll also do for that X-axis. We want to do job title short. We'll click

to do job title short. We'll click create. Now I had a brain fart. And I

create. Now I had a brain fart. And I completely set that up wrong. But how

completely set that up wrong. But how can we actually fix that? Well, if we go

can we actually fix that? Well, if we go back down into our fields, specifically

back down into our fields, specifically our value fields, click this down arrow.

our value fields, click this down arrow. I'm going to go into edit spark line for

I'm going to go into edit spark line for the x-axis. Right? We're not using job

the x-axis. Right? We're not using job title short. We want to use that date,

title short. We want to use that date, right? Because we want to see it over

right? Because we want to see it over time. Specifically, I don't want all the

time. Specifically, I don't want all the different days. So, I'm going to go into

different days. So, I'm going to go into date hierarchy and I'm going to just

date hierarchy and I'm going to just select month. Click okay. Bam. That's

select month. Click okay. Bam. That's actually what we wanted with this. And

actually what we wanted with this. And I'm going to rename this to job trends.

I'm going to rename this to job trends. If you notice the spark line, it has

If you notice the spark line, it has this sort of trailing arrow similar to

this sort of trailing arrow similar to that spark line up there to signify that

that spark line up there to signify that it is a spark line that we created. Now,

it is a spark line that we created. Now, let's say we wanted to format this spark

let's say we wanted to format this spark line further. I would go into underneath

line further. I would go into underneath format your visual under visual

format your visual under visual scrolling on down all the way to the

scrolling on down all the way to the bottom we have spark lines but the most

bottom we have spark lines but the most important thing is all the way at the

important thing is all the way at the bottom anyway the spark line selected is

bottom anyway the spark line selected is job trends we could change the data

job trends we could change the data color if we want we could also adjust

color if we want we could also adjust the width to be bigger or smaller but

the width to be bigger or smaller but the thing I want to draw attention to is

the thing I want to draw attention to is this next section on markers you could

this next section on markers you could in here mark things like the highest and

in here mark things like the highest and then also the lowest unfortunately they

then also the lowest unfortunately they only have one color option So, if I

only have one color option So, if I wanted to, I could call it the red, but

wanted to, I could call it the red, but then I'm like, which one's highest?

then I'm like, which one's highest? Which one's lowest? Um, because of this,

Which one's lowest? Um, because of this, I'm going to say I'm not going to

I'm going to say I'm not going to actually use it. Fortunately, with

actually use it. Fortunately, with Excel, they give you more fine-tuning

Excel, they give you more fine-tuning capability to give certain colors to

capability to give certain colors to certain values. So, I could find that

certain values. So, I could find that more useful, but in this case, n I'm

more useful, but in this case, n I'm going to leave it off. All right, so not

going to leave it off. All right, so not so bad. We just went through tables and

so bad. We just went through tables and matrices. You have some practice

matrices. You have some practice problems to go through and get familiar

problems to go through and get familiar with how to use these. All right, with

with how to use these. All right, with that, see you in the next one.

In this lesson, we're going to be covering the five different types of

covering the five different types of cards in PowerBI that we can take

cards in PowerBI that we can take advantage of. And these are really

advantage of. And these are really important at displaying key

important at displaying key characteristics about our data. Well,

characteristics about our data. Well, instead of me telling you about it, let

instead of me telling you about it, let me actually show you what I mean. This

me actually show you what I mean. This is our first project that we're going to

is our first project that we're going to be building at the end of this chapter.

be building at the end of this chapter. We don't need to go too much into it,

We don't need to go too much into it, but the key thing here is these are

but the key thing here is these are cards up at the top of the dashboard.

cards up at the top of the dashboard. That's where cards typically located

That's where cards typically located are. These are the key information that

are. These are the key information that we want users to see first. And they can

we want users to see first. And they can display key information like job count,

display key information like job count, median salary, and even that previous

median salary, and even that previous five-star ranking that we did

five-star ranking that we did previously. Can show it here. So, let's

previously. Can show it here. So, let's get into building some cards.

So, in our canvas, I've created this new page. I'm going to go ahead and call it

page. I'm going to go ahead and call it something like cards, real original. And

something like cards, real original. And I'm going to insert our first one that

I'm going to insert our first one that we're going to go over, and that's card.

we're going to go over, and that's card. All right, with this we have a field

All right, with this we have a field attribute that we can drag into. We're

attribute that we can drag into. We're going to start simple by just showing

going to start simple by just showing the median yearly salary. So I'm going

the median yearly salary. So I'm going to drag salary year average in there. It

to drag salary year average in there. It does some aggregation. We're going to

does some aggregation. We're going to change this to median. All right. So

change this to median. All right. So this shows the value and then underneath

this shows the value and then underneath it says median year average. I'm

it says median year average. I'm actually going to change that to median

actually going to change that to median yearly salary USD. Now we can do some

yearly salary USD. Now we can do some formatting to clean this up.

formatting to clean this up. Specifically going under format value. I

Specifically going under format value. I can go into the callout value. I can

can go into the callout value. I can make that a little bit bigger if I

make that a little bit bigger if I wanted to. I can then also go into the

wanted to. I can then also go into the category label. make this one a little

category label. make this one a little bit bigger, maybe even bold it. But

bit bigger, maybe even bold it. But overall, that's about it. All the

overall, that's about it. All the specializations we can do with that card

specializations we can do with that card visual. But I actually have another

visual. But I actually have another visual that I would recommend instead of

visual that I would recommend instead of this one.

this one. And that brings us to our next visual,

And that brings us to our next visual, which instead of this card right here,

which instead of this card right here, we're going to use this card parenthesis

we're going to use this card parenthesis new. So this one actually has multiple

new. So this one actually has multiple different fields within it. We're just

different fields within it. We're just going to focus on the data field right

going to focus on the data field right now to compare what these two look like.

now to compare what these two look like. I'm going to drag in that salary year

I'm going to drag in that salary year average into there. Make it into the

average into there. Make it into the median value and then call it median

median value and then call it median yearly salary. Now, I prefer this

yearly salary. Now, I prefer this visual, this card new over the other one

visual, this card new over the other one because the title is up at the top and

because the title is up at the top and in my mind you're it's more intuitive to

in my mind you're it's more intuitive to the end user for them to actually view.

the end user for them to actually view. Now, I don't like that it centers it to

Now, I don't like that it centers it to the left. So, under format your visual

the left. So, under format your visual for this card, I can go into callout

for this card, I can go into callout value, go down to the values, I can

value, go down to the values, I can change this to center, and then I can

change this to center, and then I can even bump up the size if I wanted to to

even bump up the size if I wanted to to match 50. And then for the label itself,

match 50. And then for the label itself, I could bump this one up too. Make it

I could bump this one up too. Make it bold to make it a little bit more

bold to make it a little bit more readable. Now, I'm also not necessarily

readable. Now, I'm also not necessarily a fan of how it's drawn a border around

a fan of how it's drawn a border around this one and not this one. If I wanted

this one and not this one. If I wanted to, once again, if I need to search for

to, once again, if I need to search for something, I go into search, type in

something, I go into search, type in borders, make it a lot easier. or let's

borders, make it a lot easier. or let's just try border. Scroll on down. I can

just try border. Scroll on down. I can say here for header or sorry for the

say here for header or sorry for the card section I can turn off that border.

card section I can turn off that border. Now you be the judge for it. Which one

Now you be the judge for it. Which one do you prefer of these? Now building

do you prefer of these? Now building single cards is not the only capability

single cards is not the only capability of this. We can actually do multiple

of this. We can actually do multiple cards with this card new. I'm actually

cards with this card new. I'm actually going to slide this up here and we're

going to slide this up here and we're going to insert in a new card visual

going to insert in a new card visual underneath it to demonstrate this. So

underneath it to demonstrate this. So inside the data field of this the field

inside the data field of this the field well I can drag in something like salary

well I can drag in something like salary year average put in like we did median

year average put in like we did median but we can stick multiple values in

but we can stick multiple values in here. So I can also stick that of hourly

here. So I can also stick that of hourly in here aggregating by median and it's

in here aggregating by median and it's in one single card. I'm not a fan how

in one single card. I'm not a fan how it's centering it. So I'm going to go in

it's centering it. So I'm going to go in here into call out values and change it

here into call out values and change it to center. Okay. But that's not the only

to center. Okay. But that's not the only thing with this. Right. We got this data

thing with this. Right. We got this data field. I also can do categories with

field. I also can do categories with this. Specifically, I can drag job title

this. Specifically, I can drag job title short into here. And it's showing me for

short into here. And it's showing me for each of the different job titles. I can

each of the different job titles. I can scroll down to see more. I can see this

scroll down to see more. I can see this all. So, if I wanted to, I could sorry,

all. So, if I wanted to, I could sorry, I could put this over here. Expand this

I could put this over here. Expand this all the way up. Oh, only three are

all the way up. Oh, only three are showing. Why is that? If I want to

showing. Why is that? If I want to adjust this, I would go into format

adjust this, I would go into format visual visual under layouts, I find that

visual visual under layouts, I find that under in the layout, if I change this to

under in the layout, if I change this to something like grid, I can get a little

something like grid, I can get a little bit more. We're adjusting this to a max

bit more. We're adjusting this to a max R of two and column shown of one, but

R of two and column shown of one, but still kind of unreadable. It's too much

still kind of unreadable. It's too much data. We're just actually going to go

data. We're just actually going to go back to what it was previously. And I'm

back to what it was previously. And I'm going to go stick in that corner down

going to go stick in that corner down there. Anyway, this is my preferred

there. Anyway, this is my preferred visual out of every visual we're going

visual out of every visual we're going to show here today. Mainly because of

to show here today. Mainly because of what we demonstrated above to get that

what we demonstrated above to get that title above what the callout value is.

Next up is this gauge card and it can be used in certain situations specifically

used in certain situations specifically for us for this yearly salary. We can

for us for this yearly salary. We can use it in a manner to show what is the

use it in a manner to show what is the median salary but also what's the

median salary but also what's the minimum what's the maximum that 920,000

minimum what's the maximum that 920,000 and then what is this average or target

and then what is this average or target value. So inside of our canvas I'm going

value. So inside of our canvas I'm going to insert this gauge card. I'm going to

to insert this gauge card. I'm going to put in this top quadrant right here.

put in this top quadrant right here. Now, for the value itself, I'm going to

Now, for the value itself, I'm going to go ahead and expand this into focus

go ahead and expand this into focus mode. For the value itself, we're going

mode. For the value itself, we're going to put in that salary year average. And

to put in that salary year average. And remember, we want that to be a median

remember, we want that to be a median value. Automatically with this gauge

value. Automatically with this gauge card, whenever they put in this median

card, whenever they put in this median value, it automatically puts the minimum

value, it automatically puts the minimum or the the minimum as zero and the

or the the minimum as zero and the maximum as double that. So, where the

maximum as double that. So, where the gauge goes in the middle. Anyway, we can

gauge goes in the middle. Anyway, we can set the minimum and maximum values by

set the minimum and maximum values by dragging these over into here,

dragging these over into here, specifying, hey, we want the minimum for

specifying, hey, we want the minimum for this. And now we can see, hey, the

this. And now we can see, hey, the minimum is 15,000. Similarly, I can drag

minimum is 15,000. Similarly, I can drag this into the maximum value. And we can

this into the maximum value. And we can make that well the maximum. And then

make that well the maximum. And then they also have this target value. We

they also have this target value. We don't really have a target per se, but

don't really have a target per se, but you could put in our case, I'm going to

you could put in our case, I'm going to drag the salary average in there. And

drag the salary average in there. And we'll just put in the average in there.

we'll just put in the average in there. Now, this does have a tool tip come up.

Now, this does have a tool tip come up. So, it's behoo of you to go through and

So, it's behoo of you to go through and actually put in what is the actual names

actually put in what is the actual names for these. And so, we can see that the

for these. And so, we can see that the median salary is 113,000. Average salary

median salary is 113,000. Average salary is 120,000. Let's go back to our report,

is 120,000. Let's go back to our report, see how I've viewed from this. Not too

see how I've viewed from this. Not too bad. Like usual, let's actually update

bad. Like usual, let's actually update that title. So, we're going to change

that title. So, we're going to change that to median yearly salary. I'm also

that to median yearly salary. I'm also going to bump the font up and center it.

going to bump the font up and center it. We can also change the font of other

We can also change the font of other things like these data labels which are

things like these data labels which are the min and max values. I could bump it

the min and max values. I could bump it up to something like 20. For that

up to something like 20. For that average value, I could go to that target

average value, I could go to that target label, make this also 20. Also take off

label, make this also 20. Also take off the decimal places off that. I don't

the decimal places off that. I don't want that on there. And then finally for

want that on there. And then finally for the callout value itself, I can adjust

the callout value itself, I can adjust things like the font if I wanted to make

things like the font if I wanted to make it bold. And that's about it. That will

it bold. And that's about it. That will move with it. Fortunately, you can't, or

move with it. Fortunately, you can't, or at least underneath here, I can't

at least underneath here, I can't control the size of this callout value.

Next up is a multi-row card. And like the name implies, it has multiple rows.

the name implies, it has multiple rows. To make things easier on ourselves, I'm

To make things easier on ourselves, I'm going to take that card new, do a

going to take that card new, do a control crl +v, and then with this

control crl +v, and then with this selected, I'm going to then change this

selected, I'm going to then change this into this one of this multi-row card.

into this one of this multi-row card. All right. In our case where we had

All right. In our case where we had those 10 different job titles and using

those 10 different job titles and using in that card new I think this multi-ro

in that card new I think this multi-ro card going into this focus mode it's a

card going into this focus mode it's a lot more readable and a lot more

lot more readable and a lot more userfriendly and intuitive. I could even

userfriendly and intuitive. I could even drag in if I wanted to something like

drag in if I wanted to something like job title short and we can get an

job title short and we can get an aggregation of it specifically of count.

aggregation of it specifically of count. As usual you want to go through and

As usual you want to go through and clean up all the field names to make it

clean up all the field names to make it a lot more presentable. But overall

a lot more presentable. But overall pretty happy with this.

The last card to talk about is a KPI card. There's a lot going on here, but

card. There's a lot going on here, but the main purpose of this is to show a

the main purpose of this is to show a callout value, if you will, and then

callout value, if you will, and then from there, some sort of trend that's

from there, some sort of trend that's going on in the background. If you're at

going on in the background. If you're at or above your goal for whatever you're

or above your goal for whatever you're doing, whether it's sales or returns or

doing, whether it's sales or returns or whatever it may be, it's going to be

whatever it may be, it's going to be green. If it's below, it's going to be

green. If it's below, it's going to be red. Hopefully, it goes without saying.

red. Hopefully, it goes without saying. You could change the colors if you want

You could change the colors if you want to. Anyway, let's build this bad boy.

to. Anyway, let's build this bad boy. I'm gonna take this visual right here.

I'm gonna take this visual right here. Here, I'm going to copy it and then

Here, I'm going to copy it and then paste it. We're running out of room on

paste it. We're running out of room on here, so I'm going to just slide some

here, so I'm going to just slide some stuff around. With this new card

stuff around. With this new card selected and down at the bottom, I'm

selected and down at the bottom, I'm going to now change this into a KPI

going to now change this into a KPI card. And it's not going to show

card. And it's not going to show anything. It says, hey, fields for both

anything. It says, hey, fields for both value and trend axis are needed. So, for

value and trend axis are needed. So, for the trend axis, what we want to look at

the trend axis, what we want to look at over time, I'm going to say we want to

over time, I'm going to say we want to look at that monthly job posted date.

look at that monthly job posted date. Okay, taking this into focus mode to

Okay, taking this into focus mode to look a little bit more into it. So

look a little bit more into it. So what's going in the background is it's

what's going in the background is it's showing how that median salary is

showing how that median salary is changing over time. Right now we don't

changing over time. Right now we don't have a target. Unfortunately I don't

have a target. Unfortunately I don't have any data to if you will show a

have any data to if you will show a target for this. Like there's not

target for this. Like there's not another column for target salary based

another column for target salary based in that time frame or something like

in that time frame or something like that. So all I can do is just throw in

that. So all I can do is just throw in something like the yearly salary into

something like the yearly salary into there. Right now, it's doing an

there. Right now, it's doing an aggregation based on the sum, which

aggregation based on the sum, which apparently that's less than what the

apparently that's less than what the median is. I don't know how that's

median is. I don't know how that's possible, but if I set it to the median

possible, but if I set it to the median itself to basically set it equal to

itself to basically set it equal to itself, it's 0%, it equals itself. It

itself, it's 0%, it equals itself. It maintains green. Once again, this is for

maintains green. Once again, this is for data that maybe has you have your some

data that maybe has you have your some sales data and then some target sales

sales data and then some target sales data. And then you could combine the two

data. And then you could combine the two to combine whether you want to have this

to combine whether you want to have this green or red value show for these

green or red value show for these values. All right, it's your turn now to

values. All right, it's your turn now to go through with those practice problems

go through with those practice problems to get more in depth and familiar with

to get more in depth and familiar with how to use these different cards. In the

how to use these different cards. In the next lesson, we're going to be jumping

next lesson, we're going to be jumping into slicers and we're almost well, two

into slicers and we're almost well, two more lessons left and we'll be done with

more lessons left and we'll be done with this chapter. With that, I'll see you

this chapter. With that, I'll see you there.

All right, two more lessons and we're going to be getting into our project. In

going to be getting into our project. In this lesson, we're going to be covering

this lesson, we're going to be covering slicers, and it's a great way to prompt

slicers, and it's a great way to prompt your users to interact with their data

your users to interact with their data in order to make selections and dive in

in order to make selections and dive in deeper to find insights. All right, here

deeper to find insights. All right, here I am inside of our final solutions file.

I am inside of our final solutions file. In it, we're going to be going through

In it, we're going to be going through all the different slicers. In no way do

all the different slicers. In no way do I ever recommend you actually make a

I ever recommend you actually make a report with this many slicers inside of

report with this many slicers inside of a report, but this is mainly for demo so

a report, but this is mainly for demo so we can see all the different types that

we can see all the different types that are available. We'll have a little bonus

are available. We'll have a little bonus section at the end that whenever we

section at the end that whenever we filter down to whatever data we want,

filter down to whatever data we want, we're going to add this button to where

we're going to add this button to where if we want to clear all slicers, we can

if we want to clear all slicers, we can just click that and it'll clear it. So,

just click that and it'll clear it. So, little bonus.

So, let's get into the three major types of slicers. I'm going to create a new

of slicers. I'm going to create a new page right here and call it slicers.

page right here and call it slicers. Inside of our canvas, I'm going to go

Inside of our canvas, I'm going to go ahead and insert in a slicer. Let's go

ahead and insert in a slicer. Let's go into focus mode. And for this, we're

into focus mode. And for this, we're going to keep it simple. We're going to

going to keep it simple. We're going to just go into the job title short

just go into the job title short portion. All right. So, this is our

portion. All right. So, this is our slicer. Right now, we can select

slicer. Right now, we can select basically one uh value at a time. Now

basically one uh value at a time. Now the three major types of slicers going

the three major types of slicers going under format your visual under slicer

under format your visual under slicer settings are controlled in here right

settings are controlled in here right now. The style they have vertical list

now. The style they have vertical list which is what we're seeing tile. So I

which is what we're seeing tile. So I could select different options like this

could select different options like this and then the other option which I uh

and then the other option which I uh also really like are is the dropdown to

also really like are is the dropdown to where the user has to go in select the

where the user has to go in select the dropdown. They do may have to scroll,

dropdown. They do may have to scroll, but they can select what they want

but they can select what they want inside of here and then still see that

inside of here and then still see that it's selected. Now, let's actually see

it's selected. Now, let's actually see this interact with the report. I'm going

this interact with the report. I'm going to go to our column and bar chart

to go to our column and bar chart example that we put together or the page

example that we put together or the page we put together. I'm going to copy this

we put together. I'm going to copy this report and then paste it right here

report and then paste it right here underneath here. And then you can see as

underneath here. And then you can see as I paste it in here, it already filter

I paste it in here, it already filter down to what I want, which actually

down to what I want, which actually brings up our next point. What happens

brings up our next point. What happens if we want to quickly clear it? like

if we want to quickly clear it? like yeah I can come in here and uncheck that

yeah I can come in here and uncheck that but that's a little little burdensome.

but that's a little little burdensome. Um so instead if we have something

Um so instead if we have something selected like data analyst in this case

selected like data analyst in this case if you notice whenever I'm on top of the

if you notice whenever I'm on top of the slicer I can come up here to the top and

slicer I can come up here to the top and select clear selection and then it

select clear selection and then it updates with the selection clear. Now

updates with the selection clear. Now this always in is isn't intuitive to end

this always in is isn't intuitive to end users. So at the end of this lesson

users. So at the end of this lesson we're going over how we can create that

we're going over how we can create that button to clear all slicers if we want

button to clear all slicers if we want to. I'm going to do some formatting

to. I'm going to do some formatting changes just to make it a little bit

changes just to make it a little bit more visual as we go through some of

more visual as we go through some of these demos. I'm make this graph all the

these demos. I'm make this graph all the way big and make this take up this. I'm

way big and make this take up this. I'm also going to change the font size. You

also going to change the font size. You don't need to do this unless you want

don't need to do this unless you want to, but I'm going to make it a lot

to, but I'm going to make it a lot bigger so we can actually see it. All

bigger so we can actually see it. All right, let's go over some functionality

right, let's go over some functionality more on this. I'm going to transfer this

more on this. I'm going to transfer this back into a vertical list. Okay, the

back into a vertical list. Okay, the first thing is this. What happens if I

first thing is this. What happens if I want like in this case there's only 10

want like in this case there's only 10 values, but imagine if there's a 100

values, but imagine if there's a 100 values and I need to search through it.

values and I need to search through it. How am I going to be able to do that?

How am I going to be able to do that? Well, I can click the ellipses up here

Well, I can click the ellipses up here and select search. And this now enables

and select search. And this now enables search within here. So, I could search

search within here. So, I could search for something like, hey, which ones

for something like, hey, which ones contain the word data? And all of them

contain the word data? And all of them pop up. The next thing is what if I want

pop up. The next thing is what if I want to select multiple values? So, in this

to select multiple values? So, in this case, I have data analyst, b business

case, I have data analyst, b business anal like I can't select multiple

anal like I can't select multiple values. What am I supposed to do here?

values. What am I supposed to do here? Well, if we go under format your visual

Well, if we go under format your visual visuals and then slicer settings under

visuals and then slicer settings under selection, we can see that by default

selection, we can see that by default multi- select with control or command is

multi- select with control or command is enabled right now. So if the user wants

enabled right now. So if the user wants to input multiple values in this, they

to input multiple values in this, they have to hold control and then select all

have to hold control and then select all the multiple different values. Kind of

the multiple different values. Kind of annoying if you ask me. Now let's say

annoying if you ask me. Now let's say you only want them to select one value

you only want them to select one value at a time. Well, you can turn this

at a time. Well, you can turn this single select on and then it turns into

single select on and then it turns into radio buttons which prompts them that

radio buttons which prompts them that hey, you can only do one value at a

hey, you can only do one value at a time. In this case with job titles,

time. In this case with job titles, that's not necessarily applicable. So,

that's not necessarily applicable. So, I'm going to turn that off. And then the

I'm going to turn that off. And then the other one I am going to enable that's

other one I am going to enable that's not enabled by default that you should

not enabled by default that you should turn on typically is show select all.

turn on typically is show select all. And this is a fast way for them besides

And this is a fast way for them besides using that clear slicer to just hey,

using that clear slicer to just hey, select all to get back to where they

select all to get back to where they need to get to. For the time being, I'm

need to get to. For the time being, I'm going to switch this up and we're going

going to switch this up and we're going to just change this into tile. And it

to just change this into tile. And it works in the same way. I can select

works in the same way. I can select multiple values by holding control and

multiple values by holding control and then going through and clicking on the

then going through and clicking on the buttons.

Now, for categories, these are the three major types, vertical list, tile, and

major types, vertical list, tile, and drop down. But there's actually other

drop down. But there's actually other types of slicers. Let's find out how we

types of slicers. Let's find out how we can make those. So, I'm going to come in

can make those. So, I'm going to come in and create a new slicer. And I could

and create a new slicer. And I could drag in a number column or in this case

drag in a number column or in this case I'm going to drag in a date column. Now

I'm going to drag in a date column. Now for this one it's using what we call a

for this one it's using what we call a between slicer. I know this I can go

between slicer. I know this I can go into format your visual slicer settings.

into format your visual slicer settings. And now when I do this drop down there's

And now when I do this drop down there's a lot more different options in here.

a lot more different options in here. You can do between um between meaning I

You can do between um between meaning I can slide this and move it anywhere

can slide this and move it anywhere between. We can see that the data did

between. We can see that the data did update during that time. I could do

update during that time. I could do before where only on one side it's

before where only on one side it's allowed me to move. after on the other

allowed me to move. after on the other side allowed me to move drop down as we

side allowed me to move drop down as we saw before not no change here for that

saw before not no change here for that they do have this one on relative date

they do have this one on relative date or relative time this one I can't really

or relative time this one I can't really even see this this allows you to filter

even see this this allows you to filter based on something like oh the last one

based on something like oh the last one if we wanted to last one years and then

if we wanted to last one years and then we can't see anything actually let's go

we can't see anything actually let's go back to the report oh it's not working

back to the report oh it's not working this is actually known issue let me show

this is actually known issue let me show you what I mean I'm going to actually

you what I mean I'm going to actually switch this back to between. I'm going

switch this back to between. I'm going to go and clear the selection so that

to go and clear the selection so that way this and I actually had to drag this

way this and I actually had to drag this end date down to get it to work after

end date down to get it to work after switching it to between. Anyway, this is

switching it to between. Anyway, this is a known issue with PowerBI. As you see,

a known issue with PowerBI. As you see, whenever I drag that start date back and

whenever I drag that start date back and forward, it's causing the filters or

forward, it's causing the filters or causing the other visuals to end up

causing the other visuals to end up breaking. And so, what I have to do is I

breaking. And so, what I have to do is I have to basically clear selection and

have to basically clear selection and then it gets back to working. Anyway,

then it gets back to working. Anyway, this is a bug. Somebody on Reddit

this is a bug. Somebody on Reddit reported this a month ago, filming this

reported this a month ago, filming this in May of 2025. So maybe by the time

in May of 2025. So maybe by the time you're filming this or sorry, you're

you're filming this or sorry, you're actually going through this, you won't

actually going through this, you won't have this error coming up. But just know

have this error coming up. But just know that Microsoft is aware of this issue

that Microsoft is aware of this issue and they're trying to troubleshoot to

and they're trying to troubleshoot to fix this. So anytime you enter this

fix this. So anytime you enter this error error by just like filtering by

error error by just like filtering by start date, you can just clear the

start date, you can just clear the selection. Right now, I'm going to

selection. Right now, I'm going to recommend we don't use any between

recommend we don't use any between filters because of this. Specifically

filters because of this. Specifically for any dashboards that we're trying to

for any dashboards that we're trying to build. Hey, we can also do one other

build. Hey, we can also do one other thing with this specifically. We're not

thing with this specifically. We're not limited to just dates. If I wanted to, I

limited to just dates. If I wanted to, I could take something like the salary

could take something like the salary data and stick it into the field. I'm

data and stick it into the field. I'm going to get rid of this and then change

going to get rid of this and then change this visual or the slicer setting back

this visual or the slicer setting back to between. But now I could adjust what

to between. But now I could adjust what salary range I want to look at for this.

salary range I want to look at for this. Now, for all of these slicers, I do

Now, for all of these slicers, I do recommend updating the title to make it

recommend updating the title to make it more intuitive of what's going on here.

more intuitive of what's going on here. You can adjust it underneath the slicer

You can adjust it underneath the slicer header, but I'm going to recommend just

header, but I'm going to recommend just going into the field and changing it to

going into the field and changing it to an intuitive title like this one's of

an intuitive title like this one's of filter salary range. Our top title one

filter salary range. Our top title one now is select job title. And then update

now is select job title. And then update this one to something like filter

this one to something like filter posting date.

posting date. So, what happens if we want to use

So, what happens if we want to use slicers on multiple different pages?

slicers on multiple different pages? Well, we do have an an option to sync

Well, we do have an an option to sync slicer. So, in this case, I'm going to

slicer. So, in this case, I'm going to take this tile slicer that we have right

take this tile slicer that we have right here and copy it. And then we're going

here and copy it. And then we're going to go to the column and bar page. I'm

to go to the column and bar page. I'm going to paste this bad boy into here.

going to paste this bad boy into here. And it's going to say this. Hey, do you

And it's going to say this. Hey, do you want to sync slicers or sync visuals?

want to sync slicers or sync visuals? One or more of these copy visuals can

One or more of these copy visuals can stay in sync with the visual is copied

stay in sync with the visual is copied from. Do you want to keep them in sync?

from. Do you want to keep them in sync? In most all cases, I do want to do this.

In most all cases, I do want to do this. Now, if you remember back to our column

Now, if you remember back to our column and bar lesson, we did a filter on this

and bar lesson, we did a filter on this page for job title short for all job

page for job title short for all job titles that contain the word data. So,

titles that contain the word data. So, in this case, there's only six values

in this case, there's only six values here. Seven if you include se select

here. Seven if you include se select all. Anyway, if I make a selection like

all. Anyway, if I make a selection like in this case, I'm going hold control and

in this case, I'm going hold control and do data analyst, data engineer, and data

do data analyst, data engineer, and data scientist. All three selected. And then

scientist. All three selected. And then when I go back to the slicers page,

when I go back to the slicers page, these are also selected here. Now, what

these are also selected here. Now, what if I want to unsync it or customize the

if I want to unsync it or customize the syncing behavior more? Well, in the

syncing behavior more? Well, in the ribbon, if we navigate to the view tab

ribbon, if we navigate to the view tab and we go to sync slicers, I'm going to

and we go to sync slicers, I'm going to close out of data. And also in

close out of data. And also in visualizations, we have a sync slicers

visualizations, we have a sync slicers popup. I'm going to select the slicer

popup. I'm going to select the slicer that I want to investigate further. And

that I want to investigate further. And it provides a list of all the different

it provides a list of all the different pages we have. So column and bar all the

pages we have. So column and bar all the way to slicers. This first column right

way to slicers. This first column right here indicates whether the slicer is

here indicates whether the slicer is synced between the other page. And then

synced between the other page. And then this other one indicates whether it's

this other one indicates whether it's visible or not. So on this slicer page I

visible or not. So on this slicer page I could make this unvisible or invisible

could make this unvisible or invisible and uh take it away. Typically I would

and uh take it away. Typically I would just delete it. Anyway, you could we

just delete it. Anyway, you could we could also unsync it. So if I wanted to

could also unsync it. So if I wanted to unsync these slicers I could unsink

unsync these slicers I could unsink unsync it here. And now on this page,

unsync it here. And now on this page, I'm going to select senior roles and

I'm going to select senior roles and remove these. However, when I navigate

remove these. However, when I navigate back to column and bar, the data is

back to column and bar, the data is still filtered for data engineer, data

still filtered for data engineer, data scientist, data analyst. However, it's

scientist, data analyst. However, it's filtered down and our slicer is missing.

filtered down and our slicer is missing. This is actually sort of weird behavior,

This is actually sort of weird behavior, but if we go into view and I go into

but if we go into view and I go into selection to show this up, we can see

selection to show this up, we can see all the different visuals in here,

all the different visuals in here, right? You know, you can do the hide if

right? You know, you can do the hide if you want to or not. The slicer became

you want to or not. The slicer became hidden because we basically unhid it.

hidden because we basically unhid it. Anyway, we have those three values right

Anyway, we have those three values right here. I'm going to go ahead. I don't

here. I'm going to go ahead. I don't want this slicer on this page anyway, so

want this slicer on this page anyway, so we're going to go ahead and just delete

we're going to go ahead and just delete it anyway. But now we understand how we

it anyway. But now we understand how we can sync slicers between different

can sync slicers between different pages. In almost all situations, like I

pages. In almost all situations, like I said, I'm going to sync slice between

said, I'm going to sync slice between pages to make it that much easier for

pages to make it that much easier for the enduser to navigate between pages

the enduser to navigate between pages and have their current selections

and have their current selections maintained as they go throughout.

Last thing to talk about is the clear all slicers button. Like I mentioned

all slicers button. Like I mentioned previously, this isn't necessarily of

previously, this isn't necessarily of clear selections of the top of the drop

clear selections of the top of the drop down. It's not necessarily super

down. It's not necessarily super intuitive for users to click to clear

intuitive for users to click to clear their slicers. So, open the ribbon under

their slicers. So, open the ribbon under insert. I'm going to go to buttons and

insert. I'm going to go to buttons and we're going to insert a button of clear

we're going to insert a button of clear all slicers. Let's make room for this up

all slicers. Let's make room for this up at the top. Going to drag that right

at the top. Going to drag that right into the center right here. So now, so

into the center right here. So now, so let's say I go through and make some

let's say I go through and make some different selections and filter also for

different selections and filter also for a certain date. Whenever I want to clear

a certain date. Whenever I want to clear this, I can just come up to clear all

this, I can just come up to clear all slicers, press control, and click it. If

slicers, press control, and click it. If I want to dress this button up a little

I want to dress this button up a little bit and go under the format buttons

bit and go under the format buttons under style, make the font a little bit

under style, make the font a little bit bigger, make it bold, go down to fill,

bigger, make it bold, go down to fill, turn it on, change it to this blue

turn it on, change it to this blue color, and then maybe put something like

color, and then maybe put something like a shadow underneath it to make it stand

a shadow underneath it to make it stand out a little bit more. Also, not really

out a little bit more. Also, not really a fan of rectangles. So, underneath

a fan of rectangles. So, underneath shape in here, I can come under this and

shape in here, I can come under this and I can just change this to round

I can just change this to round rectangle. All right, good enough. Now,

rectangle. All right, good enough. Now, there's one other button I want to call

there's one other button I want to call your attention to that you can add to

your attention to that you can add to this. I'm going to go under the optimize

this. I'm going to go under the optimize tab on the ribbon, and it's an apply all

tab on the ribbon, and it's an apply all slicers button. And I'm going to drag

slicers button. And I'm going to drag that over here for the time being and

that over here for the time being and drag this over. Let's first demo it, and

drag this over. Let's first demo it, and then I'll explain it. I'm going to make

then I'll explain it. I'm going to make multiple different selections. So, data

multiple different selections. So, data analyst, also data engineers, and then

analyst, also data engineers, and then data scientist. You notice the button

data scientist. You notice the button went from that light gray to a dark

went from that light gray to a dark gray. I'll also change the date also

gray. I'll also change the date also during this which I didn't call out none

during this which I didn't call out none of the data updated. So now I can click

of the data updated. So now I can click controllclick this and now the data will

controllclick this and now the data will update. Now why would you want to do

update. Now why would you want to do this? So I cleared all slicers. If I go

this? So I cleared all slicers. If I go through and just actually select all the

through and just actually select all the items as it's filtering. Actually I have

items as it's filtering. Actually I have to get rid of this button to actually

to get rid of this button to actually demo that. I'm going to go ahead and

demo that. I'm going to go ahead and remove it. Yeah, this will change the uh

remove it. Yeah, this will change the uh slicer behavior, but mainly I want to

slicer behavior, but mainly I want to just demonstrate whenever I'm clicking

just demonstrate whenever I'm clicking this, the data is updating pretty dang

this, the data is updating pretty dang quickly. So something like that of

quickly. So something like that of underneath the optimize tab of this

underneath the optimize tab of this apply all slicers button in this case

apply all slicers button in this case based on how small the data set is, not

based on how small the data set is, not really useful and probably going to

really useful and probably going to cause more confusion for the end user.

cause more confusion for the end user. This type of button would be used in

This type of button would be used in cases where there's a very large data

cases where there's a very large data set and it's taking a long time for the

set and it's taking a long time for the data to refresh. And so instead, I'd

data to refresh. And so instead, I'd allow them to select all what they

allow them to select all what they wanted to do and then apply the update

wanted to do and then apply the update to all the visuals. So if you have this

to all the visuals. So if you have this on there of this apply all slicers, go

on there of this apply all slicers, go ahead and remove it. We're not going to

ahead and remove it. We're not going to use it and going to go ahead and clear

use it and going to go ahead and clear all slicers as well. All right. So you

all slicers as well. All right. So you have some practice problems and now go

have some practice problems and now go through and get more familiar with using

through and get more familiar with using all these different slicers and also

all these different slicers and also some practice with buttons. In the next

some practice with buttons. In the next lesson, which conveniently we just had a

lesson, which conveniently we just had a intro to buttons, we go further in

intro to buttons, we go further in detail on buttons and also bookmarks. So

detail on buttons and also bookmarks. So with that, I'll see you there.

Welcome to this last lesson this chapter on buttons and bookmarks. And I may be a

on buttons and bookmarks. And I may be a little biased, but this is by far my

little biased, but this is by far my favorite section because we're going to

favorite section because we're going to have a lot more fun with PowerBI. Now,

have a lot more fun with PowerBI. Now, in our solutions notebook, let me show

in our solutions notebook, let me show you what I mean. So, in the first half,

you what I mean. So, in the first half, we're going to be covering buttons.

we're going to be covering buttons. Buttons aren't really that difficult,

Buttons aren't really that difficult, and you've made some already. In this

and you've made some already. In this case, I want to go to a certain lesson

case, I want to go to a certain lesson in here. I can navigate with the witch

in here. I can navigate with the witch chart, and it navigates me to that page,

chart, and it navigates me to that page, and then I can navigate back. All this

and then I can navigate back. All this was configured with buttons. We're going

was configured with buttons. We're going to build something similar to this for

to build something similar to this for the first half of this lesson. Now, for

the first half of this lesson. Now, for the second half, we're going to move on

the second half, we're going to move on to a more complex topic of bookmarks.

to a more complex topic of bookmarks. Anyway, let me show you what bookmarks

Anyway, let me show you what bookmarks can do. Here we are on our page of

can do. Here we are on our page of column and bar charts. And I have this

column and bar charts. And I have this button up at the top for a bookmark. And

button up at the top for a bookmark. And whenever I click controllclick onto the

whenever I click controllclick onto the button, I have appearing on top of our

button, I have appearing on top of our visuals a slicer that allows us to

visuals a slicer that allows us to navigate down to whatever data I want.

navigate down to whatever data I want. So I'll make a few selections. And then

So I'll make a few selections. And then once I'm done making those selections, I

once I'm done making those selections, I can go ahead and click this button to

can go ahead and click this button to close it off. And it's filtered down.

close it off. And it's filtered down. Anyway, revealing the magic behind the

Anyway, revealing the magic behind the scenes. Going to the view tab under

scenes. Going to the view tab under bookmarks. I can see that this is

bookmarks. I can see that this is controlled via bookmarks, specifically

controlled via bookmarks, specifically this bookmark here. and this bookmark

this bookmark here. and this bookmark here, which we're going to be building

here, which we're going to be building during this lesson. And these bookmarks

during this lesson. And these bookmarks are just placed inside of the action for

are just placed inside of the action for the button themselves. Okay. Anyway, I'm

the button themselves. Okay. Anyway, I'm getting ahead of myself. Let's actually

getting ahead of myself. Let's actually get into buttons.

get into buttons. Like I mentioned, we're going to be

Like I mentioned, we're going to be building a page like this. So, inside of

building a page like this. So, inside of our PowerBI report that we're on, we

our PowerBI report that we're on, we need to first create a new page for this

need to first create a new page for this homepage. I'm going to go ahead and name

homepage. I'm going to go ahead and name it home. And then I'm going to

it home. And then I'm going to rightclick it. Instead of trying to drag

rightclick it. Instead of trying to drag it over, I'm going go to move to and I'm

it over, I'm going go to move to and I'm going to move to front. Inside of this

going to move to front. Inside of this blank pan canvas, I'm going to insert a

blank pan canvas, I'm going to insert a title. We can do this by going to the

title. We can do this by going to the insert tab and just inserting a text

insert tab and just inserting a text box. And then in my case, I'm just going

box. And then in my case, I'm just going to format say chapter 2 visualizations

to format say chapter 2 visualizations real original. All right, let's build

real original. All right, let's build those buttons for the different pages.

those buttons for the different pages. This one's actually really simple.

This one's actually really simple. Underneath buttons, I'm going to go to

Underneath buttons, I'm going to go to navigator and then page navigator. These

navigator and then page navigator. These buttons then pop up. I'm going to move

buttons then pop up. I'm going to move them and recenter it. We're going to

them and recenter it. We're going to format it here in a little bit, but just

format it here in a little bit, but just want to demonstrate how it is working

want to demonstrate how it is working right now. Right, home is we're on home,

right now. Right, home is we're on home, so it's automatically black. And then

so it's automatically black. And then column bar. If I want to go to that, I'm

column bar. If I want to go to that, I'm going to click control. And then I'm

going to click control. And then I'm going to be able to navigate to it. So,

going to be able to navigate to it. So, let's format it by ensuring we're

let's format it by ensuring we're selected on it. Go into the format

selected on it. Go into the format navigator and under shape. You know, I

navigator and under shape. You know, I don't like rectangles. I like rounded

don't like rectangles. I like rounded rectangles. Next, I'm going to go into

rectangles. Next, I'm going to go into the grid layout to adjust how it's done.

the grid layout to adjust how it's done. And we're going to change this from

And we're going to change this from horizontal to grid. And from this we can

horizontal to grid. And from this we can specify how many rows and how many

specify how many rows and how many columns. For this we're going to say

columns. For this we're going to say three rows and three columns. Now

three rows and three columns. Now navigating underneath style I'm going to

navigating underneath style I'm going to change the font size to a little bit

change the font size to a little bit bigger. Make it bold. And if you notice

bigger. Make it bold. And if you notice it only changed these buttons. It didn't

it only changed these buttons. It didn't change our home one. And that's because

change our home one. And that's because that's the selected one. So we can also

that's the selected one. So we can also change this one to 20. Anyway, going

change this one to 20. Anyway, going back to those that are default. We can

back to those that are default. We can then go into under fill. And you know I

then go into under fill. And you know I love me some light blue. So, we'll stick

love me some light blue. So, we'll stick with that. And then we'll also enable

with that. And then we'll also enable the shadow to make it look a little more

the shadow to make it look a little more poppy to entice people to press the

poppy to entice people to press the buttons. All right, this is good enough.

buttons. All right, this is good enough. Now, as you remember, if we clicked on

Now, as you remember, if we clicked on something like press control and go to

something like press control and go to column bar. Yeah, we're on column bar,

column bar. Yeah, we're on column bar, but how the heck do we get back to the

but how the heck do we get back to the homepage? Well, we can add a button for

homepage? Well, we can add a button for this. Now, inside of the projects folder

this. Now, inside of the projects folder under resources and then under images,

under resources and then under images, we actually have an image in there of a

we actually have an image in there of a home icon emoji. We're going to use this

home icon emoji. We're going to use this for our button. So, underneath the

for our button. So, underneath the insert ribbon, I'm going to go to image.

insert ribbon, I'm going to go to image. We're going to navigate to that projects

We're going to navigate to that projects folder, resources, images, and then

folder, resources, images, and then select the home emoji. From there, I'm

select the home emoji. From there, I'm going to slide it up into the right hand

going to slide it up into the right hand corner to make sure it's not blocking

corner to make sure it's not blocking anything. All right. Right now, this is

anything. All right. Right now, this is just an image. There's nothing happening

just an image. There's nothing happening with it. It doesn't do any actions when

with it. It doesn't do any actions when I uh click control. We notice we have

I uh click control. We notice we have format image popup and we have action.

format image popup and we have action. And right now, it's off. So, obviously,

And right now, it's off. So, obviously, we want the action to be on. Now we can

we want the action to be on. Now we can assign different actions. We could

assign different actions. We could assign book a bookmark which we'll be

assign book a bookmark which we'll be doing later. Could assign page

doing later. Could assign page navigation which probably what we want

navigation which probably what we want to do but they also have other features

to do but they also have other features to do as well. We're going to just stick

to do as well. We're going to just stick with page navigation. For this we need

with page navigation. For this we need to specify the destination and we want

to specify the destination and we want to go home. I always want to go home. So

to go home. I always want to go home. So now with this if I go ahead and click

now with this if I go ahead and click control and then on the home button it

control and then on the home button it navigates me here and I can navigate

navigates me here and I can navigate back here. All right. Easy way. Let's go

back here. All right. Easy way. Let's go ahead and just copy this. Press Ctrl + C

ahead and just copy this. Press Ctrl + C and then I'm going to just navigate into

and then I'm going to just navigate into every single page and paste it into

every single page and paste it into there pressing commandV or controlV. All

there pressing commandV or controlV. All right, I navigated to the last page.

right, I navigated to the last page. Let's go back home. And with this,

Let's go back home. And with this, remember we have to press control plus

remember we have to press control plus the click. Some of our users that are

the click. Some of our users that are new to this may not know they need to

new to this may not know they need to press control. So, we could use what's

press control. So, we could use what's called a Q&A feature or Q&A button to

called a Q&A feature or Q&A button to prompt them to do this. So, under the

prompt them to do this. So, under the insert tab, I'm going to go to buttons

insert tab, I'm going to go to buttons and we're going to go to this one on

and we're going to go to this one on help. I'm going to stick it up in the

help. I'm going to stick it up in the right hand corner. And this is pretty

right hand corner. And this is pretty generic and understanding that users

generic and understanding that users probably need to go here if they have

probably need to go here if they have help. Now, clicking this button, I'm

help. Now, clicking this button, I'm going to go into actions itself. I want

going to go into actions itself. I want to turn on these actions. And the type

to turn on these actions. And the type of action I want to do for this is a

of action I want to do for this is a Q&A.

Q&A. Then I'm going to navigate here to under

Then I'm going to navigate here to under tool tip. the tool tip is on. And for

tool tip. the tool tip is on. And for the tool tip or the answer to the

the tool tip or the answer to the question, if you will, I'll say press

question, if you will, I'll say press control while clicking a button to

control while clicking a button to select. So now whenever I'm on this

select. So now whenever I'm on this page, if I just scroll over this, this

page, if I just scroll over this, this popup comes up says press control while

popup comes up says press control while clicking a button to select. And they

clicking a button to select. And they don't even have to actually select the

don't even have to actually select the button. Sorry, got a little bit

button. Sorry, got a little bit confused. Whenever I click control and

confused. Whenever I click control and actually click this button, it pops up

actually click this button, it pops up Q&A is not what I want. I don't want

Q&A is not what I want. I don't want Remember, we went over Q&A feature. The

Remember, we went over Q&A feature. The thing's horrendous. Instead, what we

thing's horrendous. Instead, what we want for the action for this button is

want for the action for this button is we want it to just bookmark and the

we want it to just bookmark and the bookmark to be none. So, if they were to

bookmark to be none. So, if they were to actually control-click this, nothing's

actually control-click this, nothing's actually going to happen, but they have

actually going to happen, but they have the tool tip pop up. Got a little

the tool tip pop up. Got a little confused there. And that's a great segue

confused there. And that's a great segue into our next section on bookmarks

into our next section on bookmarks because now we can see that we could set

because now we can see that we could set a bookmark as an action to a button.

So, let's get into making our first bookmark. What we need to do is go to

bookmark. What we need to do is go to view tab on the ribbon, select bookmarks

view tab on the ribbon, select bookmarks to pop it up. In it, the instructions

to pop it up. In it, the instructions are pretty simple. It says filter data

are pretty simple. It says filter data to get to the state you want to capture

to get to the state you want to capture and then click add. So, let's first just

and then click add. So, let's first just capture this state right here that we're

capture this state right here that we're in to demonstrate what's going on here.

in to demonstrate what's going on here. So, I'll click add. And nothing special

So, I'll click add. And nothing special just pops up here. Now, let's say we

just pops up here. Now, let's say we want a filtered state. Say I wanted to

want a filtered state. Say I wanted to select in this case data engineer, data

select in this case data engineer, data scientist and data analyst. By the way,

scientist and data analyst. By the way, I'm holding control for all those. And I

I'm holding control for all those. And I want to bookmark this view. Well, then I

want to bookmark this view. Well, then I would go ahead and click add. Okay,

would go ahead and click add. Okay, remember previously bookmark one is the

remember previously bookmark one is the unfiltered one. So if I were to click

unfiltered one. So if I were to click it, it's going to go to that. And if I

it, it's going to go to that. And if I go to this one, it's going to go to

go to this one, it's going to go to that. Now these names are generic. So

that. Now these names are generic. So we're going to name them real quick to

we're going to name them real quick to column and bar unfiltered and then

column and bar unfiltered and then column bar filtered. Like I said before,

column bar filtered. Like I said before, we could go in, insert a button. In this

we could go in, insert a button. In this case, I'll just insert, we'll say a

case, I'll just insert, we'll say a blank button and make it nice and big,

blank button and make it nice and big, and of course, change the fill to a

and of course, change the fill to a light blue color. We're going to end up

light blue color. We're going to end up deleting this button, so you don't need

deleting this button, so you don't need to necessarily use this. Anyway, in the

to necessarily use this. Anyway, in the action, I can turn it on. Go into here,

action, I can turn it on. Go into here, turn on bookmark, and set this bookmark

turn on bookmark, and set this bookmark to something like filtered. So now,

to something like filtered. So now, whenever I click this, pressing

whenever I click this, pressing controllclick, it's going to filter my

controllclick, it's going to filter my data. Um, but if I could click it again,

data. Um, but if I could click it again, it's not going to unfilter it. I'd have

it's not going to unfilter it. I'd have to create another button for unfiltered.

to create another button for unfiltered. Anyway, that's for demos only. We're

Anyway, that's for demos only. We're going to go ahead and just delete this

going to go ahead and just delete this button. Now, diving into this bookmark a

button. Now, diving into this bookmark a little bit further. Going to the filter,

little bit further. Going to the filter, going to the three dots associated with

going to the three dots associated with this. We can see for the popup up at the

this. We can see for the popup up at the top, they have options like update,

top, they have options like update, rename, delete. You could also g group

rename, delete. You could also g group bookmarks if you want. We're not going

bookmarks if you want. We're not going to do that. The key things down here are

to do that. The key things down here are we have these attributes selected. These

we have these attributes selected. These are the bookmark properties on whether

are the bookmark properties on whether it's going to save things related to the

it's going to save things related to the data display or if it was related to the

data display or if it was related to the current page. So let's demo why this is

current page. So let's demo why this is important. Going back and selecting this

important. Going back and selecting this for column and bar charts filtered. If I

for column and bar charts filtered. If I were to select this one and then

were to select this one and then unselect data. So it's no longer

unselect data. So it's no longer selected. And then from there click

selected. And then from there click update because it's updating this

update because it's updating this bookmark. Now when I go to unfiltered

bookmark. Now when I go to unfiltered it's going to unfilter. And then when I

it's going to unfilter. And then when I go to filtered, it's not going to

go to filtered, it's not going to actually filter the data anymore because

actually filter the data anymore because we have data unchecked. So let's

we have data unchecked. So let's actually put that back where it needs to

actually put that back where it needs to be. We'll select data engineer, data

be. We'll select data engineer, data scientist, data analyst. We'll change

scientist, data analyst. We'll change this to data and then also click update.

this to data and then also click update. Now navigating between the two, they

Now navigating between the two, they work just fine. All right. Next up, what

work just fine. All right. Next up, what do we mean by display? Well, display

do we mean by display? Well, display deals with what visuals are shown or not

deals with what visuals are shown or not shown. Let's unfilter this data. Let's

shown. Let's unfilter this data. Let's say I wanted to insert in a slicer. So,

say I wanted to insert in a slicer. So, I'm going to put this bad boy right

I'm going to put this bad boy right here. I'm going to drag in the job title

here. I'm going to drag in the job title short column for it. Right now, we're

short column for it. Right now, we're going to change the settings and we're

going to change the settings and we're going to make it into tile. I'm going

going to make it into tile. I'm going make it a little bit bigger. Also, let's

make it a little bit bigger. Also, let's close out of this data pane.

close out of this data pane. Everything's getting sort of small.

Everything's getting sort of small. Anyway, as expected, right, I could use

Anyway, as expected, right, I could use this and holding control, I could select

this and holding control, I could select multiple different options that I want.

multiple different options that I want. But what happens if I wanted to use a

But what happens if I wanted to use a bookmark to control whether something

bookmark to control whether something like this visual uh is visible or not?

like this visual uh is visible or not? So let's first record a bookmark with

So let's first record a bookmark with this slicer available. I'll go ahead and

this slicer available. I'll go ahead and click add and then call this column and

click add and then call this column and bar slicer. Okay, so now this one's

bar slicer. Okay, so now this one's activated. And now I want to bookmark

activated. And now I want to bookmark where it's not visible. Well, I'm going

where it's not visible. Well, I'm going to go into the view ribbon and go to

to go into the view ribbon and go to selection. Also going to close down on

selection. Also going to close down on visualizations. Got too many panes over

visualizations. Got too many panes over here. Anyway, if you remember, we went

here. Anyway, if you remember, we went over selection pane previously. We can

over selection pane previously. We can hide or display certain options. So,

hide or display certain options. So, what I'm going to do is I'm going to go

what I'm going to do is I'm going to go ahead and hide it. And now, let's add

ahead and hide it. And now, let's add another bookmark and call this column

another bookmark and call this column and bar no slicer. So, now with the

and bar no slicer. So, now with the bookmark, I can toggle between being a

bookmark, I can toggle between being a slicer and no slicer. Now, visually,

slicer and no slicer. Now, visually, this is actually I don't really like

this is actually I don't really like this how this is done visually. So, I'm

this how this is done visually. So, I'm going to just show a quick little trick

going to just show a quick little trick how we can fix this. Going to insert.

how we can fix this. Going to insert. I'm going to insert in a shape,

I'm going to insert in a shape, specifically a rectangle. Make this bad

specifically a rectangle. Make this bad boy take up the entire view. Put this

boy take up the entire view. Put this underneath the slicer. So, the slicer is

underneath the slicer. So, the slicer is on top of it. And then for format shape,

on top of it. And then for format shape, we're going to go into the style, change

we're going to go into the style, change this to the color of black, and then

this to the color of black, and then change the transparency to something

change the transparency to something like 75%. Okay. So, it looks like, hey,

like 75%. Okay. So, it looks like, hey, it's popping out in front. I can insert

it's popping out in front. I can insert another shape behind it. Putting it

another shape behind it. Putting it behind the slicer itself. And for this

behind the slicer itself. And for this one, I'm going to change the color to

one, I'm going to change the color to just white. This one will leave

just white. This one will leave transparency at zero. All right. So now

transparency at zero. All right. So now for our bookmark for the slicer, I want

for our bookmark for the slicer, I want to update what it looks like. So I'm

to update what it looks like. So I'm going to click it and click update. Now

going to click it and click update. Now when I navigate between no slicer and

when I navigate between no slicer and slicer, that didn't work.

slicer, that didn't work. That didn't work because for no slicer,

That didn't work because for no slicer, I actually need to update this one as

I actually need to update this one as well to remove this shape and this

well to remove this shape and this shape. So, we'll update this one as

shape. So, we'll update this one as well. Now, slicer, no slicer. Now, let's

well. Now, slicer, no slicer. Now, let's add buttons to activate this. So, under

add buttons to activate this. So, under insert tab, under buttons, I'm just

insert tab, under buttons, I'm just going to use this one here on bookmarks.

going to use this one here on bookmarks. I made it slightly bigger. And then in

I made it slightly bigger. And then in it, we're going to navigate to the

it, we're going to navigate to the format button. And under action, I'm

format button. And under action, I'm going to change this bookmark to

going to change this bookmark to activate the slicer. So now whenever I

activate the slicer. So now whenever I click this, it activates the slicer, but

click this, it activates the slicer, but the button's still there. I actually

the button's still there. I actually want the button to disappear because we

want the button to disappear because we need to stick another button in that

need to stick another button in that place to make the slicer disappear.

place to make the slicer disappear. So with this, I'm going to hide this

So with this, I'm going to hide this button. And once again, I need to update

button. And once again, I need to update this slicer view. So I click three dots,

this slicer view. So I click three dots, clicked update. Now, navigating back

clicked update. Now, navigating back between the two, testing it out. Click

between the two, testing it out. Click the button. The button disappears. Okay.

the button. The button disappears. Okay. So, now let's add a button on here. For

So, now let's add a button on here. For this, we're just going to keep it

this, we're just going to keep it simple. We're going to add this back

simple. We're going to add this back arrow. And we'll go into formatting that

arrow. And we'll go into formatting that button. The action specifically, I want

button. The action specifically, I want it to go to a bookmark. And we want to

it to go to a bookmark. And we want to go to column and bar. No slicer. Now,

go to column and bar. No slicer. Now, testing this button out. I'm going to

testing this button out. I'm going to click it. And both buttons are

click it. And both buttons are appearing. Uh that means we need to on

appearing. Uh that means we need to on this visual hide the back arrow button

this visual hide the back arrow button and thus update this no slicer to make

and thus update this no slicer to make sure that includes it. Okay, this should

sure that includes it. Okay, this should be the final tweak for this. Yep, now I

be the final tweak for this. Yep, now I can use these buttons to navigate back

can use these buttons to navigate back and forth and I don't have to go to this

and forth and I don't have to go to this bookmarks pane. Now one major thing you

bookmarks pane. Now one major thing you may have noticed with this, let's say I

may have noticed with this, let's say I clicked open this and then I selected

clicked open this and then I selected data analyst, data engineers, data

data analyst, data engineers, data scientist and then clicked back. My data

scientist and then clicked back. My data resets. What the heck is going on here?

resets. What the heck is going on here? Well, as we demonstrated in the

Well, as we demonstrated in the unfiltered and then also the filtered

unfiltered and then also the filtered example, which apparently I need to

example, which apparently I need to update these as well, visuals. We'll get

update these as well, visuals. We'll get to that. We need to adjust what is going

to that. We need to adjust what is going on with the data uh metadata that's

on with the data uh metadata that's going on and being saved here.

going on and being saved here. Specifically, we don't want to keep

Specifically, we don't want to keep exactly this data in here. So, just to

exactly this data in here. So, just to make sure I'm clear, I have no slicer

make sure I'm clear, I have no slicer selected. From there, I'm going to

selected. From there, I'm going to uncheck data and then I'm going to click

uncheck data and then I'm going to click update. I'm going to do the same thing

update. I'm going to do the same thing now with the slicer. I'm going to

now with the slicer. I'm going to uncheck data and then with that click

uncheck data and then with that click update. So now whenever I'm working with

update. So now whenever I'm working with this and I select data analyst, data

this and I select data analyst, data engineer, data scientist and then close

engineer, data scientist and then close out of this, it stays because that no

out of this, it stays because that no slicer bookmark is not preserving the

slicer bookmark is not preserving the data state. I'm going to update the

data state. I'm going to update the filtered and unfiltered real quick. For

filtered and unfiltered real quick. For the filtered, I want to hide basically

the filtered, I want to hide basically all these different things that we had

all these different things that we had on here and then click update. For

on here and then click update. For unfiltered, I want to do the same thing

unfiltered, I want to do the same thing and then click update. Okay, this is a

and then click update. Okay, this is a great way. Now we can just cycle between

great way. Now we can just cycle between all these, make sure that it's working,

all these, make sure that it's working, everything's working fine. Looks like

everything's working fine. Looks like I'm sort of a silly. I didn't maintain

I'm sort of a silly. I didn't maintain the buttons as they needed to be.

the buttons as they needed to be. Specifically, that bookmark filter. I'm

Specifically, that bookmark filter. I'm going to just update that real quick on

going to just update that real quick on both of these. So, as you can see by

both of these. So, as you can see by that, this can get real finicky to make

that, this can get real finicky to make sure that you want everything to work

sure that you want everything to work properly and everything set up just

properly and everything set up just fine. So, that way there's nothing that

fine. So, that way there's nothing that is interfering with the other thing. So,

is interfering with the other thing. So, bookmarks can get really technical.

bookmarks can get really technical. Because of that, we got some practice

Because of that, we got some practice problems for you to now go through and

problems for you to now go through and test your capabilities with creating

test your capabilities with creating buttons and also with bookmarks. With

buttons and also with bookmarks. With that, we now have all the skills

that, we now have all the skills necessary to dive into our first

necessary to dive into our first project. And for that, we'll be building

project. And for that, we'll be building a data science dashboard with this data

a data science dashboard with this data set we've been working with and a lot of

set we've been working with and a lot of the visuals we've already built. All

the visuals we've already built. All right, with that, I'll see you in the

right, with that, I'll see you in the next one.

Welcome to this first of three lessons in building our first project with

in building our first project with PowerBI. In this lesson and the next

PowerBI. In this lesson and the next lesson, we'll be building the first and

lesson, we'll be building the first and second page of our dashboard. And then

second page of our dashboard. And then in the final lesson, we'll be going

in the final lesson, we'll be going through how we can share it via

through how we can share it via something like PowerBI service or even

something like PowerBI service or even something like GitHub.

So before we get into actually building this dashboard, we need to understand

this dashboard, we need to understand what are some basic or best practices to

what are some basic or best practices to implement to create a dashboard that

implement to create a dashboard that people are actually going to use. I can

people are actually going to use. I can tell you from personal experience that a

tell you from personal experience that a lot of dashboards that I built,

lot of dashboards that I built, especially my younger days, that I

especially my younger days, that I didn't have these type of principles in

didn't have these type of principles in mind, I guarantee you they're not in use

mind, I guarantee you they're not in use today because of that. And specifically,

today because of that. And specifically, it starts and also ends with two main

it starts and also ends with two main questions that you should always be

questions that you should always be asking yourself. What problem are we

asking yourself. What problem are we trying to solve with this dashboard? And

trying to solve with this dashboard? And who are we designing this dashboard for?

who are we designing this dashboard for? You may think that you have the newest

You may think that you have the newest and greatest dashboard built, but if

and greatest dashboard built, but if your end consumer, your stakeholder

your end consumer, your stakeholder doesn't have the same concerns or same

doesn't have the same concerns or same problems that they think are being had,

problems that they think are being had, they're not going to be using the

they're not going to be using the dashboard. This is a great example, or

dashboard. This is a great example, or actually I should say a bad example

actually I should say a bad example specifically how somebody built a

specifically how somebody built a dashboard and didn't think of those two

dashboard and didn't think of those two questions. I mean, just looking at it,

questions. I mean, just looking at it, who is it? Who do you think this is even

who is it? Who do you think this is even intended for and what problem are they

intended for and what problem are they trying to solve? This is a dashboard I

trying to solve? This is a dashboard I found online and it's a dashboard that

found online and it's a dashboard that obviously deals with something around

obviously deals with something around sales for a company. But even as a user

sales for a company. But even as a user myself, where should I be looking and

myself, where should I be looking and what should I be drawing my attention

what should I be drawing my attention to? From a design perspective, there's

to? From a design perspective, there's entirely too many colors. And for that

entirely too many colors. And for that pie chart up there or that donut chart,

pie chart up there or that donut chart, once again, you should never have that

once again, you should never have that many values inside of there. All right,

many values inside of there. All right, this next example, they're going to get

this next example, they're going to get better by the way as we go along. This

better by the way as we go along. This example, not too bad. This dashboard is

example, not too bad. This dashboard is obviously being used for some

obviously being used for some stakeholders within supply chain and

stakeholders within supply chain and sales that want to monitor the

sales that want to monitor the performance over time and they have a

performance over time and they have a specific attributes that they're looking

specific attributes that they're looking at. From a design perspective though,

at. From a design perspective though, I'm going to say this has once again a

I'm going to say this has once again a little bit too many colors. Mostly I'm

little bit too many colors. Mostly I'm getting drawn to those portions that are

getting drawn to those portions that are red, such as that profit, then that

red, such as that profit, then that ranking overview at the bottom. But red

ranking overview at the bottom. But red doesn't mean that it's bad. It's just

doesn't mean that it's bad. It's just how they colored it. Not really a fan of

how they colored it. Not really a fan of it. I do however like that it is dark

it. I do however like that it is dark mode. Next up is this one on call center

mode. Next up is this one on call center dashboard. And easily I can see from

dashboard. And easily I can see from this this is monitoring how active a

this this is monitoring how active a call center is and specifically what

call center is and specifically what areas are most active. So this is

areas are most active. So this is probably made for some sort of manager

probably made for some sort of manager within a call center to monitor

within a call center to monitor performance and see if there's any

performance and see if there's any irregularities. I'm liking the design

irregularities. I'm liking the design aspect from this. It's very simple. They

aspect from this. It's very simple. They kept simple color palettes to draw your

kept simple color palettes to draw your eye and attention into darker colors.

eye and attention into darker colors. Although I would argue that some of the

Although I would argue that some of the lighter colors are a little too

lighter colors are a little too distracting, but overall nice little

distracting, but overall nice little view. Oh, and it has a little dark and

view. Oh, and it has a little dark and light mode that you can switch between.

light mode that you can switch between. All right, last one's probably my most

All right, last one's probably my most favorite. In this one, we can see that

favorite. In this one, we can see that clearly we're monitoring some sort of

clearly we're monitoring some sort of web traffic. And we have the cards up at

web traffic. And we have the cards up at the top in order to draw our attention

the top in order to draw our attention into the most key metrics. and then

into the most key metrics. and then visuals underneath this in order to

visuals underneath this in order to reinforce what's going above. I really

reinforce what's going above. I really like this type of design and I'm going

like this type of design and I'm going to recommend it. I'm really liking these

to recommend it. I'm really liking these sessions and page views cuz we can see

sessions and page views cuz we can see we do have some anomalies here in some

we do have some anomalies here in some portions. So, that would queue me in as

portions. So, that would queue me in as a manager of this that I'd want to maybe

a manager of this that I'd want to maybe go and investigate those areas. And why

go and investigate those areas. And why we have it's probably at the same time

we have it's probably at the same time why we have these bounce rates and page

why we have these bounce rates and page exits during that and probably we have

exits during that and probably we have this large spike right here. Anyway, the

this large spike right here. Anyway, the simpler the better. Really love the

simpler the better. Really love the simplicity of this.

So, getting into the planning of our dashboard, we want to first start out by

dashboard, we want to first start out by looking at and answering those two

looking at and answering those two questions that I previously was

questions that I previously was scrutinizing. First is who are we

scrutinizing. First is who are we designing this for? Specifically, we're

designing this for? Specifically, we're going to design this for job seekers,

going to design this for job seekers, job transitioners, or swappers.

job transitioners, or swappers. Basically, somebody looking for a

Basically, somebody looking for a promotion within a company. and

promotion within a company. and specifically those that are working in

specifically those that are working in data science because I got data on data

data science because I got data on data science jobs. And what problem are we

science jobs. And what problem are we trying to solve? Well, those look for

trying to solve? Well, those look for roles often struggle because information

roles often struggle because information about the job market scattered

about the job market scattered everywhere. There's no single location

everywhere. There's no single location to get an overall trend of the market,

to get an overall trend of the market, typical compensation levels, and even

typical compensation levels, and even job quality. So that's what our

job quality. So that's what our dashboard is aiming to solve because we

dashboard is aiming to solve because we could go to something like LinkedIn,

could go to something like LinkedIn, search for a job like data analyst and

search for a job like data analyst and yeah it provides an overview of

yeah it provides an overview of different jobs available and an amount

different jobs available and an amount of results but there's nothing related

of results but there's nothing related to trends expected pay and whatnot. So

to trends expected pay and whatnot. So we're going to be aiming to solve this.

we're going to be aiming to solve this. So, anytime you're starting out, I

So, anytime you're starting out, I recommend actually going and if you

recommend actually going and if you will, drawing out or just giving a rough

will, drawing out or just giving a rough sketch of what you want to accomplish

sketch of what you want to accomplish with your dashboard. And if you have

with your dashboard. And if you have stakeholders available, you can show

stakeholders available, you can show them what you're thinking and g then get

them what you're thinking and g then get direct feedback. So, make sure you're

direct feedback. So, make sure you're not going down a wrong avenue. for this

not going down a wrong avenue. for this dashboard. I did this beforehand and put

dashboard. I did this beforehand and put together a rough sketch of some things

together a rough sketch of some things that I'd like to have available on a

that I'd like to have available on a dashboard for me to have access to. I've

dashboard for me to have access to. I've been in a position of job searching. So,

been in a position of job searching. So, I really put on that lens to try to

I really put on that lens to try to analyze this and dissect if this is how

analyze this and dissect if this is how I wanted it. Key things with this is I

I wanted it. Key things with this is I like to keep it symmetrical, right? So,

like to keep it symmetrical, right? So, I have all of my cards up at the top. I

I have all of my cards up at the top. I have the graphs equally spaced and

have the graphs equally spaced and equally made. I want to keep the visuals

equally made. I want to keep the visuals as simple as possible. So that's why I

as simple as possible. So that's why I have the line and the bar charts on the

have the line and the bar charts on the left. We'll have a table and scatter

left. We'll have a table and scatter plot on the right hand side, which I

plot on the right hand side, which I feel is less likely for them to look

feel is less likely for them to look towards, but also we'll have that key

towards, but also we'll have that key information, especially in that table if

information, especially in that table if they want to export it into Excel. So

they want to export it into Excel. So let's get into building this bad boy.

let's get into building this bad boy. We're going to be doing a rough draft

We're going to be doing a rough draft first. We're going to be starting from

first. We're going to be starting from the top and then building down.

So, in your current workbook, we're going to stay in here because we're

going to stay in here because we're going to copy a lot of different

going to copy a lot of different visualizations from it and also use that

visualizations from it and also use that same data set that we cleaned up. And

same data set that we cleaned up. And I'm going to create a new page and we're

I'm going to create a new page and we're going to call it data jobs dashboard.

going to call it data jobs dashboard. We'll leave the page all the way at the

We'll leave the page all the way at the end. When we're done building this

end. When we're done building this dashboard, we're going to go ahead and

dashboard, we're going to go ahead and delete all these pages and save it as it

delete all these pages and save it as it no own PowerBI file. But for now, we'll

no own PowerBI file. But for now, we'll just keep it all together. Anyway, first

just keep it all together. Anyway, first thing I'm going to do is put a title in

thing I'm going to do is put a title in here. So, I'm going to insert in a text

here. So, I'm going to insert in a text box. And inside of it, I'm going to put

box. And inside of it, I'm going to put data jobs dashboard. And I just

data jobs dashboard. And I just formatted it to be a little bit bigger.

formatted it to be a little bit bigger. Next up, I'm going to insert a slicer.

Next up, I'm going to insert a slicer. So, I can go to our slicers pane and I'm

So, I can go to our slicers pane and I'm going to select this one here on the job

going to select this one here on the job title. Going to insert it in. In this

title. Going to insert it in. In this case, I want to keep them separate

case, I want to keep them separate because they're their own dashboard. So,

because they're their own dashboard. So, we're not going to sync. And I'm going

we're not going to sync. And I'm going to minimize some of these panes over

to minimize some of these panes over here. So, I can come into here anyway. I

here. So, I can come into here anyway. I want to change the format of this slicer

want to change the format of this slicer specifically. I just want it to be a

specifically. I just want it to be a drop down. I don't want it to be too

drop down. I don't want it to be too crazy. All right, so that's good. Now,

crazy. All right, so that's good. Now, let's move into putting the cards up.

let's move into putting the cards up. Remember, we're going to be doing job

Remember, we're going to be doing job count, job rating, yearly salary, and

count, job rating, yearly salary, and hourly salary. I also have it in this

hourly salary. I also have it in this format because job count, these graphs

format because job count, these graphs underneath it are going to correlate to

underneath it are going to correlate to the count. And then whereas the salary,

the count. And then whereas the salary, everything underneath it, the scatter

everything underneath it, the scatter plot that deals with salary and the

plot that deals with salary and the tables are going to deal with salary.

tables are going to deal with salary. So, I leave that aside. So, it makes it

So, I leave that aside. So, it makes it symmetrical but also intuitive. On the

symmetrical but also intuitive. On the cards page, I'm going to copy this one

cards page, I'm going to copy this one that we have on median year salary. Ctrl

that we have on median year salary. Ctrl + C. And then paste it in over here. I'm

+ C. And then paste it in over here. I'm going to align it towards the center

going to align it towards the center because I know I want it over here. And

because I know I want it over here. And we'll put it right here. Okay. I'm going

we'll put it right here. Okay. I'm going to copy this one and then adjust the

to copy this one and then adjust the spacing. I also adjust the width a

spacing. I also adjust the width a little bit. And then I'm going to go

little bit. And then I'm going to go ahead and copy this. Paste another one

ahead and copy this. Paste another one right here. And then another one right

right here. And then another one right here. First one. Remember, we want job

here. First one. Remember, we want job count. So I'm going take that job tile

count. So I'm going take that job tile short. throw it into here. Change

short. throw it into here. Change aggregation account and then this to job

aggregation account and then this to job count. Next thing I want that star

count. Next thing I want that star rating for this. So I'm going to take

rating for this. So I'm going to take the salary star rating, drag it into

the salary star rating, drag it into here. And this one looks like it's

here. And this one looks like it's formatted good. And the only other one

formatted good. And the only other one we need to change this one from median

we need to change this one from median yearly salary to median hourly salary.

yearly salary to median hourly salary. Not too bad. Not liking how this number

Not too bad. Not liking how this number is formatted here. So under format or

is formatted here. So under format or visual for this one, going into the

visual for this one, going into the values, changing the settings to just

values, changing the settings to just job count itself. We'll set the value uh

job count itself. We'll set the value uh the value decimal places to zero. Now

the value decimal places to zero. Now let's put some visuals in here. We'll

let's put some visuals in here. We'll start with top left getting these job

start with top left getting these job count over time on our line and area

count over time on our line and area page. This one's good enough. We'll take

page. This one's good enough. We'll take a control C of this and I'll paste it

a control C of this and I'll paste it right in. Next up, I want our job counts

right in. Next up, I want our job counts per job title. This is the closest one I

per job title. This is the closest one I can find on our column and bar chart

can find on our column and bar chart page. So, I'm going to copy this. We'll

page. So, I'm going to copy this. We'll just have to alter it. And I'll paste

just have to alter it. And I'll paste this in down here, squeezing it in. And

this in down here, squeezing it in. And then we'll change this from using that

then we'll change this from using that median yearly salary to instead using

median yearly salary to instead using the count of jobs. Next up is that

the count of jobs. Next up is that scatter plot in the top rightand corner.

scatter plot in the top rightand corner. If you navigate to that common charts

If you navigate to that common charts page, we're going to be using this one.

page, we're going to be using this one. I'm going to copy it, then paste it

I'm going to copy it, then paste it right into here. Format where it needs

right into here. Format where it needs to go. All right, the final one is our

to go. All right, the final one is our table or more specifically our matrix

table or more specifically our matrix that we made. So I'm going to go ahead

that we made. So I'm going to go ahead and copy this and then we're going to

and copy this and then we're going to put it down at the bottom right hand

put it down at the bottom right hand corner. All right. So, not so bad for a

corner. All right. So, not so bad for a rough draft. We have everything that we

rough draft. We have everything that we want inside of here. If I wanted to, I

want inside of here. If I wanted to, I could filter down for something like

could filter down for something like business analyst. And it shows us

business analyst. And it shows us everything we need for this.

Now, let's get this bad boy cleaned up now. And here's a look at where we're

now. And here's a look at where we're going to finally get to. Specifically, I

going to finally get to. Specifically, I like adding backgrounds and boxes to

like adding backgrounds and boxes to sort of box things off to draw people's

sort of box things off to draw people's attention in to where they need to go to

attention in to where they need to go to and look. In this case, once again, I'm

and look. In this case, once again, I'm keeping it very symmetrical. But like I

keeping it very symmetrical. But like I said, like with this job count, this

said, like with this job count, this area deals with counts and this side

area deals with counts and this side really deals with salary. So that's why

really deals with salary. So that's why I've designed it in this manner. Anyway,

I've designed it in this manner. Anyway, all this is doing is inserting shapes

all this is doing is inserting shapes into here. And specifically, we're

into here. And specifically, we're inserting it behind the visual. So going

inserting it behind the visual. So going into insert into shapes, we're going to

into insert into shapes, we're going to insert in a rounded rectangle. I'm going

insert in a rounded rectangle. I'm going to put it over on top of the area that

to put it over on top of the area that we want. We'll adjust the formatting

we want. We'll adjust the formatting here in a little bit, but first with

here in a little bit, but first with under format shapes under the style, you

under format shapes under the style, you know, I like that light blue. So, we're

know, I like that light blue. So, we're going to start with light blue. Also, I

going to start with light blue. Also, I like shadows. So, we're going to add a

like shadows. So, we're going to add a shadow to this. And it also makes it a

shadow to this. And it also makes it a little bit smaller and not touching the

little bit smaller and not touching the edges. Now, with this selected, I've

edges. Now, with this selected, I've formatted enough. I'm going to control C

formatted enough. I'm going to control C it and then Ctrl +V. I'm going go

it and then Ctrl +V. I'm going go through and just put this over all the

through and just put this over all the different visuals in here. We'll then

different visuals in here. We'll then adjust the order after this. Not too

adjust the order after this. Not too bad. going into view and then selection.

bad. going into view and then selection. First of all, I'm going to select all of

First of all, I'm going to select all of them together because I want to group

them together because I want to group them just to make it easier. I'm

them just to make it easier. I'm pressing control while I do this. And

pressing control while I do this. And then I'm going to click the three dots

then I'm going to click the three dots and click group. Okay, so now it's all

and click group. Okay, so now it's all one group. I'll then name this to

one group. I'll then name this to background. And then I can take the

background. And then I can take the background all the way to the bottom of

background all the way to the bottom of the selection. So that way it puts it

the selection. So that way it puts it behind. And now you're like, Luke, what

behind. And now you're like, Luke, what happened? It disappeared. Well, as you

happened? It disappeared. Well, as you can see through the cracks, it is there.

can see through the cracks, it is there. Um, but we have to actually remove the

Um, but we have to actually remove the backgrounds of all these other things.

backgrounds of all these other things. What do we mean by that? Okay, let's

What do we mean by that? Okay, let's select a card. I'm going to minimize

select a card. I'm going to minimize this and go into format visual. So,

this and go into format visual. So, inside of here, I'm going to just

inside of here, I'm going to just actually search and I'm going to search

actually search and I'm going to search for background. Underneath effects, I'm

for background. Underneath effects, I'm going to turn off this background. And

going to turn off this background. And then also on the cards itself, I'm gonna

then also on the cards itself, I'm gonna turn off that background. And we got to

turn off that background. And we got to go through and do this for all of them

go through and do this for all of them as well. Turning off the effects

as well. Turning off the effects background and then the cards

background and then the cards background. For the graphs and visuals,

background. For the graphs and visuals, it should be only the effects background

it should be only the effects background that you need to turn off. But for the

that you need to turn off. But for the matrix, we need to not only do the

matrix, we need to not only do the effects background, but also you can see

effects background, but also you can see there's other white behind it. And for

there's other white behind it. And for it, we need to go to layout and style

it, we need to go to layout and style presets. The style is on default right

presets. The style is on default right now. We're going to change it to none.

now. We're going to change it to none. Now, what I'm going to do is just go

Now, what I'm going to do is just go through and adjust the size of all this

through and adjust the size of all this to make sure they fit within their

to make sure they fit within their appropriate squares. All right. So,

appropriate squares. All right. So, looking good. Not too bad. If you wanted

looking good. Not too bad. If you wanted to dive into one of these, such as we

to dive into one of these, such as we did before, we can enter obviously focus

did before, we can enter obviously focus mode and we can still see it everything

mode and we can still see it everything visually. But the only thing that I'm

visually. But the only thing that I'm seeing left is how these icons have this

seeing left is how these icons have this white value and it can get sort of

white value and it can get sort of distracting from what's going on there.

distracting from what's going on there. These are controlled under general and

These are controlled under general and header icon. You can't just toggle them

header icon. You can't just toggle them on or off, unfortunately. You have to

on or off, unfortunately. You have to actually change the transparency to 100%

actually change the transparency to 100% to make it sort of hide a little bit

to make it sort of hide a little bit better. So, I'm going to go through and

better. So, I'm going to go through and just hide all these different ones. All

just hide all these different ones. All right, not too bad. Going to go ahead

right, not too bad. Going to go ahead and save this.

and save this. We're going to stop right there for this

We're going to stop right there for this page. In the next lesson, if you will,

page. In the next lesson, if you will, we'll be building the second page for

we'll be building the second page for this. and we're going to be using a new

this. and we're going to be using a new feature that we haven't discussed yet

feature that we haven't discussed yet and that's drill through. There's no

and that's drill through. There's no practice problems for this lesson or any

practice problems for this lesson or any lessons in the project. And with that,

lessons in the project. And with that, see you in the next one.

All right, welcome to the second of three videos in this project section.

three videos in this project section. We're now going to get into building our

We're now going to get into building our drill through page. And you're probably

drill through page. And you're probably like, what the heck is a drill through?

like, what the heck is a drill through? So, let's actually demonstrate it in

So, let's actually demonstrate it in action. Here I am in our final dashboard

action. Here I am in our final dashboard right here. And right now users can go

right here. And right now users can go through and see different things. And

through and see different things. And typically they're going to want to look

typically they're going to want to look in or dive in deeper to something. Let's

in or dive in deeper to something. Let's say I'm a data engineer and I come in

say I'm a data engineer and I come in here and I select that engineer to find

here and I select that engineer to find out different values about it. Well, I

out different values about it. Well, I want to learn more. Well, if you notice

want to learn more. Well, if you notice this button up at the top became well,

this button up at the top became well, it moved from a uh grade out to actually

it moved from a uh grade out to actually ungrade out, if you will, or a visible.

ungrade out, if you will, or a visible. And so now it says drill through to job

And so now it says drill through to job title. And what I can do is press

title. And what I can do is press controlclick to it. And now we're

controlclick to it. And now we're directed to our drill through page. And

directed to our drill through page. And this is what we're going to be building

this is what we're going to be building in this course specifically for this

in this course specifically for this lesson. Anyway, with it, this has

lesson. Anyway, with it, this has specific metrics that I feel are more

specific metrics that I feel are more applicable at a job title level. And

applicable at a job title level. And remember, we selected data engineer. We

remember, we selected data engineer. We have that at the top. Then we have

have that at the top. Then we have things like the hourly and yearly

things like the hourly and yearly salary, the different percentages for

salary, the different percentages for all the different attributes, and then

all the different attributes, and then some different visualizations as well.

some different visualizations as well. Anyway, if we want to navigate back to

Anyway, if we want to navigate back to home, we can click this icon to go back,

home, we can click this icon to go back, and bam, we're back at the data jobs

and bam, we're back at the data jobs dashboard, and everything's cleared. So,

dashboard, and everything's cleared. So, let's actually get into building this

let's actually get into building this drill through.

drill through. So, in our PowerBI file, I'm going to

So, in our PowerBI file, I'm going to create a new page, and I'm going to call

create a new page, and I'm going to call it job title drill through. First thing

it job title drill through. First thing I'm going to stick in here is a card.

I'm going to stick in here is a card. similar to the last thing up at the top

similar to the last thing up at the top that displays what is the job title

that displays what is the job title we're drilling through to. So, I'm just

we're drilling through to. So, I'm just going to come in here and we're going to

going to come in here and we're going to steal it from our first dashboard, at

steal it from our first dashboard, at least the formatting for it, and paste

least the formatting for it, and paste it up here in the top. Now, for this,

it up here in the top. Now, for this, right, I want the job title that it's

right, I want the job title that it's filtered to to displaying in this card.

filtered to to displaying in this card. So, I'm going to drag this job title

So, I'm going to drag this job title short column over into the data to

short column over into the data to replace it. And I'm going to call this

replace it. And I'm going to call this job title Joe through since that's the

job title Joe through since that's the name of our page. Anyway, right now I'm

name of our page. Anyway, right now I'm aggregating to the first value that

aggregating to the first value that appears and right now it's business

appears and right now it's business analyst. So let's actually experiment

analyst. So let's actually experiment with or actually implement our drill

with or actually implement our drill through. So you may have noticed before

through. So you may have noticed before on the visualizations pane if I actually

on the visualizations pane if I actually scroll all the way down they have this

scroll all the way down they have this section here on drill through and this

section here on drill through and this says hey you can add drill through

says hey you can add drill through fields here. For example, we want to

fields here. For example, we want to drill through based on well what this

drill through based on well what this card has too, but that job title short

card has too, but that job title short column. And inside of it, it allows you

column. And inside of it, it allows you to filter the data for what you want.

to filter the data for what you want. We're going to leave everything as is.

We're going to leave everything as is. So overall, you can see nothing really

So overall, you can see nothing really changed on this page. Well, something

changed on this page. Well, something that did change. We got this back arrow

that did change. We got this back arrow up at the top lefthand corner. But this

up at the top lefthand corner. But this now is where the magic happens. I can go

now is where the magic happens. I can go to data jobs dashboard and something

to data jobs dashboard and something like data engineer. I can go ahead and

like data engineer. I can go ahead and select it. Right, we don't have a button

select it. Right, we don't have a button just yet. But what I can do is I can

just yet. But what I can do is I can rightclick it and inside of this popup

rightclick it and inside of this popup it says drill through. And then it says

it says drill through. And then it says we can drill through to the page of job

we can drill through to the page of job title drill through. And I'm now taken

title drill through. And I'm now taken to that page. And that page is filtered

to that page. And that page is filtered for data engineer. One note, you can

for data engineer. One note, you can selecting this card itself and then

selecting this card itself and then scrolling on down to drill through.

scrolling on down to drill through. Right now we have keep all filters on

Right now we have keep all filters on for the drill through. So basically the

for the drill through. So basically the filter on the other page that cross

filter on the other page that cross filters applied to here. And any other

filters applied to here. And any other filters that we may have on that page

filters that we may have on that page are applied to this page. I like to keep

are applied to this page. I like to keep it on. Keep it on. Oh, and then to demo

it on. Keep it on. Oh, and then to demo we still have that they we have that

we still have that they we have that arrow up there. The arrow then takes us

arrow up there. The arrow then takes us back to the previous page in the report

back to the previous page in the report that we came from. Now, this isn't

that we came from. Now, this isn't specific. We didn't add anything to the

specific. We didn't add anything to the data jobs dashboard. Just a demo. If I

data jobs dashboard. Just a demo. If I went to that column and bar section, we

went to that column and bar section, we could do the same thing inside of here.

could do the same thing inside of here. Drill through to that job title. Drill

Drill through to that job title. Drill through. And then we did it for senior

through. And then we did it for senior data scientist. So, I could navigate

data scientist. So, I could navigate back to that as well. All right. So,

back to that as well. All right. So, let's start building out this page.

let's start building out this page. We're going to start at the top building

We're going to start at the top building out these visuals and then working our

out these visuals and then working our way down into the map, bar chart, and

way down into the map, bar chart, and also the tree map. I'm going to take our

also the tree map. I'm going to take our yearly salary gauge and copy it from our

yearly salary gauge and copy it from our cards page. Put it all in. Format it

cards page. Put it all in. Format it down. Duplicate this and then change

down. Duplicate this and then change everything so that way it's hourly

everything so that way it's hourly salary. Don't forget also to change the

salary. Don't forget also to change the title. Next up are those fancy dancy

title. Next up are those fancy dancy doughnut charts we made in the common

doughnut charts we made in the common charts lesson. I'm going to go ahead and

charts lesson. I'm going to go ahead and copy this and then from there duplicate

copy this and then from there duplicate it three times. I had to move things

it three times. I had to move things around. It's not going to be as

around. It's not going to be as symmetrical as I want it. I'm also going

symmetrical as I want it. I'm also going to change these titles now. I don't want

to change these titles now. I don't want them all work from home. So change it to

them all work from home. So change it to no degree mentioned health insurance.

no degree mentioned health insurance. Now, we need to actually adjust the

Now, we need to actually adjust the values in here to actually use what

values in here to actually use what we're supposed to be using. All right,

we're supposed to be using. All right, not too bad. I messed up the coloring

not too bad. I messed up the coloring here. I need to just clean it up while

here. I need to just clean it up while we're in this. Specifically, I'm going

we're in this. Specifically, I'm going to change all the true values to this

to change all the true values to this blue color and the false values to like

blue color and the false values to like a lighter gray. That way, these all have

a lighter gray. That way, these all have a similar type format. All right, three

a similar type format. All right, three more visuals left. I'm going to go to

more visuals left. I'm going to go to the map chart. I'm going to go in and

the map chart. I'm going to go in and steal this one here for where we were

steal this one here for where we were looking at the mentioning of the job

looking at the mentioning of the job postings. Mention degree. We're actually

postings. Mention degree. We're actually going to remove that from the legend. Go

going to remove that from the legend. Go ahead and put that in there and then

ahead and put that in there and then remove that from the legend so it's only

remove that from the legend so it's only showing job counting and give it the

showing job counting and give it the title where are jobs globally. Next up

title where are jobs globally. Next up from the common charts lecture I'm going

from the common charts lecture I'm going to steal this one are what are the type

to steal this one are what are the type of data jobs and put that one in right

of data jobs and put that one in right here into the center bottom right. Last

here into the center bottom right. Last one that needed this one we don't have

one that needed this one we don't have already. I'm going to insert in a stack

already. I'm going to insert in a stack bar chart. Make it fill in the remaining

bar chart. Make it fill in the remaining value. For this one, I want to look at

value. For this one, I want to look at that job via column, specifically the

that job via column, specifically the count, like where are the job postings.

count, like where are the job postings. As we can see, LinkedIn is the top one.

As we can see, LinkedIn is the top one. And then I update this to what platform

And then I update this to what platform has most jobs along with changing this

has most jobs along with changing this one to what are the types of jobs. So,

one to what are the types of jobs. So, this has most what we want. Let's

this has most what we want. Let's actually test it out. We're going to go

actually test it out. We're going to go back to the data jobs dashboard. And we

back to the data jobs dashboard. And we can now click on this. And if we want to

can now click on this. And if we want to rightclick, drill through to the job

rightclick, drill through to the job title uh through page. Now, we're

title uh through page. Now, we're looking at everything for data

looking at everything for data engineers. can see where they're

engineers. can see where they're located, what platform we go to, and

located, what platform we go to, and what type. Not too bad. I wouldn't mind

what type. Not too bad. I wouldn't mind now going back here. We do want a button

now going back here. We do want a button up here because most users are not going

up here because most users are not going to be intuitive enough to think that,

to be intuitive enough to think that, hey, I can right click and go to the

hey, I can right click and go to the drill through. So, under the insert tab,

drill through. So, under the insert tab, I'm going to go to buttons. And for

I'm going to go to buttons. And for this, we're going to insert a blank

this, we're going to insert a blank button that's going to be put up here.

button that's going to be put up here. We'll then go ahead and now format it.

We'll then go ahead and now format it. Now, we'll go into format button. We're

Now, we'll go into format button. We're going to turn on the action itself.

going to turn on the action itself. Specifically, we want it to drill

Specifically, we want it to drill through. So, we'll change this from back

through. So, we'll change this from back to drill through. And for the

to drill through. And for the destination, we need to make sure we

destination, we need to make sure we select job title drill through. Okay.

select job title drill through. Okay. So, now just testing this out. I click

So, now just testing this out. I click data engineer. It's no longer grayed

data engineer. It's no longer grayed out. Clicking control. It navigates me

out. Clicking control. It navigates me to this page. And I can navigate back.

to this page. And I can navigate back. But this button, we don't need to just

But this button, we don't need to just leave grayed out. Let's actually format

leave grayed out. Let's actually format it by making it into a round rectangle.

it by making it into a round rectangle. Turning on the fill and making it

Turning on the fill and making it obviously to a light blue. And then

obviously to a light blue. And then turning on the shadow. Oh, we obviously

turning on the shadow. Oh, we obviously want the text on in there. And I'm going

want the text on in there. And I'm going crank up the text size that says drill

crank up the text size that says drill through to job title. I'll even make it

through to job title. I'll even make it bold. Okay. So now, whenever we're

bold. Okay. So now, whenever we're inside of here, if I click on something

inside of here, if I click on something like that, engineer, boom, it's popping

like that, engineer, boom, it's popping up. Press control, drill through to job

up. Press control, drill through to job title, and I can navigate back if I want

title, and I can navigate back if I want to.

to. All right. So, similar to that last page

All right. So, similar to that last page we did, we need to now clean this up. I

we did, we need to now clean this up. I want to put some like we did in this

want to put some like we did in this one. Put the borders and the background

one. Put the borders and the background behind it. So, I'm going to go ahead and

behind it. So, I'm going to go ahead and copy this. Press Ctrl + C and then paste

copy this. Press Ctrl + C and then paste that into here. We're going to have to

that into here. We're going to have to reformat all the different sizes in

reformat all the different sizes in here. First things first, I'm going to

here. First things first, I'm going to go into the view tab and open up the

go into the view tab and open up the selections pane. We're also going to

selections pane. We're also going to close these on downs to make a bigger

close these on downs to make a bigger view. Anyway, remember we named it

view. Anyway, remember we named it background for this. I don't need these

background for this. I don't need these top two in here. So, what I'm going to

top two in here. So, what I'm going to do is just select ungroup. And then

do is just select ungroup. And then we're just going to go ahead and

we're just going to go ahead and actually delete it then by removing it.

actually delete it then by removing it. Okay. Now, these are ungrouped. I can

Okay. Now, these are ungrouped. I can just drag these into position where I

just drag these into position where I want them. All right. Not too bad.

want them. All right. Not too bad. Pressing control. I want to group these

Pressing control. I want to group these all back together. I rightcicked it.

all back together. I rightcicked it. Plus group. Renamed again to background.

Plus group. Renamed again to background. And then move all the way to the back.

And then move all the way to the back. It looks like I missed a shape. I'm

It looks like I missed a shape. I'm going to go ahead and just drag this on

going to go ahead and just drag this on down and then open up background and

down and then open up background and throw it inside of there because

throw it inside of there because apparently I forgot it. Okay, like last

apparently I forgot it. Okay, like last time, we need to remove these white

time, we need to remove these white backgrounds on here. So, going into each

backgrounds on here. So, going into each one of these visuals themselves, opening

one of these visuals themselves, opening up that visualization pane and then in

up that visualization pane and then in the search bar, putting in background,

the search bar, putting in background, I'm going to turn off the background

I'm going to turn off the background effects for these. Getting to the map, I

effects for these. Getting to the map, I was able to do that as well with

was able to do that as well with background effects. This one also. And

background effects. This one also. And then finally, our tree map. Okay, these

then finally, our tree map. Okay, these all need to get resized now to fit in

all need to get resized now to fit in there appropriately. Like last time, I

there appropriately. Like last time, I don't like these header icons with this

don't like these header icons with this color here. So, I'm going to make the

color here. So, I'm going to make the transparency 100%. And do this for all

transparency 100%. And do this for all of these. All right. So, boom. Let's

of these. All right. So, boom. Let's test this bad boy out. We're going to

test this bad boy out. We're going to navigate back. Okay. Inside of our data

navigate back. Okay. Inside of our data jobs dashboard, if we want to dive into

jobs dashboard, if we want to dive into something like data analyst, we can see

something like data analyst, we can see the key statistics here. And then diving

the key statistics here. And then diving into the drill through itself, I can see

into the drill through itself, I can see that just for data analyst, what are all

that just for data analyst, what are all the different metrics for it? And if I

the different metrics for it? And if I wanted to, I can filter down even

wanted to, I can filter down even further inside of here. All right, so

further inside of here. All right, so that wraps up what we're going to be

that wraps up what we're going to be doing for building out this first

doing for building out this first project. In the next lesson, we're going

project. In the next lesson, we're going to be jumping into actually how we can

to be jumping into actually how we can share it using PowerBI service. And if

share it using PowerBI service. And if you don't have that or you want to do a

you don't have that or you want to do a different option, we're going to have

different option, we're going to have GitHub as well. With that, I'll see you

GitHub as well. With that, I'll see you there. All

right, welcome to this lesson on going through how we're going to go share this

through how we're going to go share this first dashboard. And first of all,

first dashboard. And first of all, congratulations for completing this

congratulations for completing this first dashboard. It's quite an

first dashboard. It's quite an accomplishment. Now, this video is

accomplishment. Now, this video is completely optional. You can decide if

completely optional. You can decide if you want to go through it or not.

you want to go through it or not. Basically, at the beginning, I'm going

Basically, at the beginning, I'm going to go over what are the different

to go over what are the different options, and then the majority of it is

options, and then the majority of it is going to be spent on how we can actually

going to be spent on how we can actually set up and share your project on GitHub.

set up and share your project on GitHub. But if you don't want to share your work

But if you don't want to share your work and potentially get a new job with

and potentially get a new job with higher pay, feel free to skip to the

higher pay, feel free to skip to the next chapter on Power Query.

So, let's go over these three options that I'm going to recommend for how you

that I'm going to recommend for how you can go about sharing your dashboard. The

can go about sharing your dashboard. The first one is the most recommended option

first one is the most recommended option and I think that you'll get the most

and I think that you'll get the most visibility with it. Specifically, it

visibility with it. Specifically, it involves LinkedIn, and I have a lot of

involves LinkedIn, and I have a lot of success sharing projects on here and

success sharing projects on here and gaining future opportunities because of

gaining future opportunities because of it. Inside your profile area, they have

it. Inside your profile area, they have a section down here at the bottom on

a section down here at the bottom on projects. And this is where you can

projects. And this is where you can showcase all your different work. It's

showcase all your different work. It's pretty easy to go through and actually

pretty easy to go through and actually add in your project. Besides that, the

add in your project. Besides that, the second option is what I also recommend

second option is what I also recommend of actually just making a post and then

of actually just making a post and then linking to that project that you've

linking to that project that you've created about this. However, there's a

created about this. However, there's a pretty key limitation with sharing

pretty key limitation with sharing projects in LinkedIn. I'm going to go

projects in LinkedIn. I'm going to go through this data science one just to

through this data science one just to show specifically. I can write about it

show specifically. I can write about it inside of here and say what I did, but

inside of here and say what I did, but then if I want to direct them or give

then if I want to direct them or give them the file of what I did, where do I

them the file of what I did, where do I do? The only thing I can do is link them

do? The only thing I can do is link them to another location. which now gets into

to another location. which now gets into this is my second option for how you

this is my second option for how you should combine this with LinkedIn to not

should combine this with LinkedIn to not only share on LinkedIn but also put all

only share on LinkedIn but also put all your work inside of here. For those that

your work inside of here. For those that are not familiar with GitHub, GitHub is

are not familiar with GitHub, GitHub is an online repository that allows you to

an online repository that allows you to keep track of all your different

keep track of all your different projects that you're working on. I go

projects that you're working on. I go through and actually like you noticed

through and actually like you noticed before I've shared all my courses here

before I've shared all my courses here because I consider it my work and it

because I consider it my work and it makes it super simple for somebody to

makes it super simple for somebody to come in and actually view it. Anyway,

come in and actually view it. Anyway, let's get into what we're going to be

let's get into what we're going to be building for this. Specifically, we're

building for this. Specifically, we're going to be setting up what's called a

going to be setting up what's called a readme file, which that's what this is

readme file, which that's what this is below here. And this is going to

below here. And this is going to document on GitHub all of the different

document on GitHub all of the different work we did in creating this dashboard.

work we did in creating this dashboard. We're include all the different skills

We're include all the different skills we showcased while building this. And

we showcased while building this. And then we're going to break down each of

then we're going to break down each of those pages that we've created while

those pages that we've created while building this. Now, this, like I

building this. Now, this, like I mentioned, is the readme, but then I can

mentioned, is the readme, but then I can also store this PowerBI file, which is

also store this PowerBI file, which is right there. And so, if a user wanted to

right there. And so, if a user wanted to access it, all they got to do is just

access it, all they got to do is just download it. And they just do this by

download it. And they just do this by clicking on the file and then going down

clicking on the file and then going down to download. And you may be like, Luke,

to download. And you may be like, Luke, what about the PowerBI service? How

what about the PowerBI service? How could you integrate that with this?

could you integrate that with this? Well, I actually integrated this with

Well, I actually integrated this with this readme if you have the option for

this readme if you have the option for this. Anyway, I include a link inside of

this. Anyway, I include a link inside of here. It says, "Hey, view the

here. It says, "Hey, view the interactive dashboard here on the

interactive dashboard here on the PowerBI service." I click on it and this

PowerBI service." I click on it and this navigates me to the online version of

navigates me to the online version of our dashboard and people can go through

our dashboard and people can go through and actually interact with it to see all

and actually interact with it to see all that we built and get some use out of

that we built and get some use out of it. Now, I feel a majority of you are

it. Now, I feel a majority of you are not going to be actually using the

not going to be actually using the PowerBI service because it costs $14 a

PowerBI service because it costs $14 a month. And frankly, I feel that's

month. And frankly, I feel that's overpriced just to host a project. So,

overpriced just to host a project. So, that'll be a completely optional

that'll be a completely optional statement that you'll be able to include

statement that you'll be able to include if you want to or not in your GitHub

if you want to or not in your GitHub repo. So, for the remainder of this

repo. So, for the remainder of this video, we're going to be going through

video, we're going to be going through these three major steps in order to get

these three major steps in order to get our work into a readme and then onto

our work into a readme and then onto GitHub and then share on LinkedIn. Let's

GitHub and then share on LinkedIn. Let's walk through it real quick. In the first

walk through it real quick. In the first portion, we're going to install all the

portion, we're going to install all the required tools. Don't worry, they're

required tools. Don't worry, they're completely free. Specifically, they

completely free. Specifically, they consist of a tool of Git and also VS

consist of a tool of Git and also VS Code. Git works in the background and is

Code. Git works in the background and is basically going to track all of our

basically going to track all of our different changes and allow us to

different changes and allow us to monitor our work. And then VS Code is

monitor our work. And then VS Code is what's working on the front end to allow

what's working on the front end to allow us to create that readme, organize our

us to create that readme, organize our project files, and then send that up to

project files, and then send that up to GitHub. Because of that, we're going to

GitHub. Because of that, we're going to need a GitHub account, which we're going

need a GitHub account, which we're going to do during this portion. For the

to do during this portion. For the second part, we're going to prepare the

second part, we're going to prepare the project. We're going to create that

project. We're going to create that readme that I showed you on GitHub and

readme that I showed you on GitHub and then set up the file and folder se uh

then set up the file and folder se uh structure correctly to where we can then

structure correctly to where we can then upload it. Which takes us to our third

upload it. Which takes us to our third point of sharing our work. We're going

point of sharing our work. We're going to put it onto GitHub and then link it

to put it onto GitHub and then link it on our LinkedIn and also make a post.

on our LinkedIn and also make a post. Like I mentioned before, this isn't of

Like I mentioned before, this isn't of interest to you, feel free to skip to

interest to you, feel free to skip to the next video.

the next video. The first thing we need to do is get Git

The first thing we need to do is get Git installed. Now, Git is a free and

installed. Now, Git is a free and open-source distributed version control

open-source distributed version control system designed to handle everything

system designed to handle everything from small to very large projects. It's

from small to very large projects. It's by far the most popular tool used for

by far the most popular tool used for basically tracking changes with files

basically tracking changes with files and allowing large teams to collaborate

and allowing large teams to collaborate together. to break it down more simply

together. to break it down more simply on what it actually is or what's

on what it actually is or what's happening there. Here I am inside of my

happening there. Here I am inside of my folder or what we'll know as my repo

folder or what we'll know as my repo repository for PowerBI. Anyway, there's

repository for PowerBI. Anyway, there's some hidden files in here. I'm on a Mac.

some hidden files in here. I'm on a Mac. I'm going to press command shift period

I'm going to press command shift period and I can unhide these files. Anyway, I

and I can unhide these files. Anyway, I have agit file and a.get ignore. Anyway,

have agit file and a.get ignore. Anyway, the file is the more important one here.

the file is the more important one here. Basically, this is the file that's being

Basically, this is the file that's being maintained to keep track of all the

maintained to keep track of all the different tri uh different changes

different tri uh different changes inside of this project. Because I am

inside of this project. Because I am using git to manage this project, it

using git to manage this project, it then allows me to then host this project

then allows me to then host this project online, specifically on GitHub. GitHub

online, specifically on GitHub. GitHub being an online repository for those

being an online repository for those that are using Git. They can send things

that are using Git. They can send things to this what we call a remote repository

to this what we call a remote repository to keep your projects here. Anyway, if I

to keep your projects here. Anyway, if I didn't have Git locally on my computer

didn't have Git locally on my computer whenever I build these projects, then I

whenever I build these projects, then I can't use GitHub. So, this allows me to

can't use GitHub. So, this allows me to use GitHub. Anyway, back to the page

use GitHub. Anyway, back to the page where we need to download Git. You can

where we need to download Git. You can just navigate to the link below and

just navigate to the link below and we're going to go into downloads. You're

we're going to go into downloads. You're most likely on a Windows. Select that

most likely on a Windows. Select that and we'll start to click here to

and we'll start to click here to download using the ARM 64 version. for

download using the ARM 64 version. for you. It should probably recommend what

you. It should probably recommend what type of computer you have. You may have

type of computer you have. You may have an x64. It'll recommend up at the top.

an x64. It'll recommend up at the top. Click that one. Once download's

Click that one. Once download's complete, launch it, allow it to access

complete, launch it, allow it to access your device. We're going to go through

your device. We're going to go through the installation, and just leave

the installation, and just leave everything set to the default. There are

everything set to the default. There are about 10ish items that I selected. Okay.

about 10ish items that I selected. Okay. And now I'm installing. Once it's

And now I'm installing. Once it's complete, I'm going toclick this and

complete, I'm going toclick this and then just go in finish. Git is now

then just go in finish. Git is now installed. The next step is getting VS

installed. The next step is getting VS Code. Now, this tool of VS Code is a

Code. Now, this tool of VS Code is a text editor and it's pretty simple. I

text editor and it's pretty simple. I just want to demo it before we actually

just want to demo it before we actually go and install it. We're allowed to see

go and install it. We're allowed to see all the different files for a project.

all the different files for a project. So, in this case, this is my PowerBI

So, in this case, this is my PowerBI file and we're downloading this and

file and we're downloading this and using this for two main reasons. First

using this for two main reasons. First of all is because we'll be able to

of all is because we'll be able to create a readme inside of here. And this

create a readme inside of here. And this readme uses a special markup language

readme uses a special markup language which I can view what this markup

which I can view what this markup language looks like or what it's going

language looks like or what it's going to look like on the internet right next

to look like on the internet right next to it. If you recall, this looks very

to it. If you recall, this looks very similar to what's on GitHub. The other

similar to what's on GitHub. The other main reason why we're using this is

main reason why we're using this is because this allows us to interact and

because this allows us to interact and push this up to GitHub using Git behind

push this up to GitHub using Git behind the scenes. So this is a great tool

the scenes. So this is a great tool especially I like using this with things

especially I like using this with things like Python and SQL. Anyway, how are we

like Python and SQL. Anyway, how are we going to install it? Just go to the

going to install it? Just go to the Microsoft Store, search for VS Code, and

Microsoft Store, search for VS Code, and it should be the first one. Not this

it should be the first one. Not this one. That's the insiders, but this one

one. That's the insiders, but this one right here. Once it's installed, you can

right here. Once it's installed, you can just open it right up. They have a

just open it right up. They have a getting started screen right here. We're

getting started screen right here. We're going to skip this for the time being.

going to skip this for the time being. I'm just going to close out of it. The

I'm just going to close out of it. The last portion to do in setup is setting

last portion to do in setup is setting up our GitHub account. For this one,

up our GitHub account. For this one, pretty simple. All you have to do is

pretty simple. All you have to do is just go enter your email and sign up for

just go enter your email and sign up for GitHub. Once logged in, you should be

GitHub. Once logged in, you should be directed to your profile. If not, click

directed to your profile. If not, click your icon in the upper right hand corner

your icon in the upper right hand corner and navigate to your profile. Anyway,

and navigate to your profile. Anyway, with this, I would go forward and

with this, I would go forward and actually set up well, setting up your

actually set up well, setting up your profile, specifically adding a profile

profile, specifically adding a profile picture, and then any other links or

picture, and then any other links or information about yourself inside of

information about yourself inside of here. Over here on the right are my

here. Over here on the right are my pinned repositories, and you'll be able

pinned repositories, and you'll be able to add this later on whenever we add our

to add this later on whenever we add our project.

Now that we have everything needed installed, we're going to now jump into

installed, we're going to now jump into preparing our project. We're going to do

preparing our project. We're going to do two major steps. First is just setting

two major steps. First is just setting up our project and having our folder

up our project and having our folder where we need it access accessed inside

where we need it access accessed inside of VS Code. And the second step, which

of VS Code. And the second step, which is probably the longer step, is actually

is probably the longer step, is actually creating our readme. First thing we need

creating our readme. First thing we need to do in getting our project folder all

to do in getting our project folder all together is well getting our PowerBI

together is well getting our PowerBI file in order. Specifically, we've been

file in order. Specifically, we've been working within this entire folder or

working within this entire folder or this entire file that has all of our

this entire file that has all of our work from everything in chapter 2. I

work from everything in chapter 2. I only want to have the dashboard page and

only want to have the dashboard page and the drill through page in there. So, how

the drill through page in there. So, how are we going to do this? Well, first I'm

are we going to do this? Well, first I'm going to save this with the appropriate

going to save this with the appropriate title so that way I don't lose any work

title so that way I don't lose any work just in case. I'm going to save it as a

just in case. I'm going to save it as a new file. So, I'm going to save it with

new file. So, I'm going to save it with the name data jobs dashboard and save it

the name data jobs dashboard and save it to a name known location. I'm saving it

to a name known location. I'm saving it to the desktop for me. Now that I can

to the desktop for me. Now that I can see it is saved, I can then go through

see it is saved, I can then go through and get rid of all the different other

and get rid of all the different other pages. Also, I added these home icons on

pages. Also, I added these home icons on here. I don't think we you did, but if

here. I don't think we you did, but if you did happen to, I'd go ahead and

you did happen to, I'd go ahead and remove them cuz it's not going to take

remove them cuz it's not going to take you anywhere. With the dashboard or the

you anywhere. With the dashboard or the homepage selected, that's very important

homepage selected, that's very important you do this. Now, go ahead and save this

you do this. Now, go ahead and save this because whenever somebody opens this

because whenever somebody opens this file, it's going to be navigated to

file, it's going to be navigated to whatever page you have saved last. At

whatever page you have saved last. At this time, I'd also recommend going

this time, I'd also recommend going through and publishing to the PowerBI

through and publishing to the PowerBI service if you have that set up. Once

service if you have that set up. Once that's done, I'm going to go ahead and

that's done, I'm going to go ahead and close out of it. So, let's go ahead and

close out of it. So, let's go ahead and open the folder for our project or for

open the folder for our project or for the folder that we're going to be using

the folder that we're going to be using for this. In this, I'm going to go ahead

for this. In this, I'm going to go ahead and select file and then new window.

and select file and then new window. Whenever I do this, I have this start

Whenever I do this, I have this start open and it says, hey, do you want to

open and it says, hey, do you want to open a folder? Yes, I do. Specifically,

open a folder? Yes, I do. Specifically, we need to open our project folder,

we need to open our project folder, which we haven't created yet. So, I'm

which we haven't created yet. So, I'm going to click on desktop because that's

going to click on desktop because that's where I want it. Going to create a new

where I want it. Going to create a new folder. And then I'm going to name it

folder. And then I'm going to name it what I want the name of this project to

what I want the name of this project to be called, and I'm going to call it

be called, and I'm going to call it simple like PowerBI dashboard. It's

simple like PowerBI dashboard. It's going to select that and select folder.

going to select that and select folder. It's ask if I trust the author of this.

It's ask if I trust the author of this. The author's me. I don't really trust

The author's me. I don't really trust myself, but I'm going to click yes

myself, but I'm going to click yes anyway. Now, right now over here on the

anyway. Now, right now over here on the left hand side, this is the file

left hand side, this is the file explorer. And right now, there's no

explorer. And right now, there's no files inside of it. So, first of all, we

files inside of it. So, first of all, we want our PowerBI file inside of here.

want our PowerBI file inside of here. The easiest way to get it in there is

The easiest way to get it in there is just open up File Explorer, drag that

just open up File Explorer, drag that PowerBI file that we have wherever you

PowerBI file that we have wherever you have it saved and into PowerBI

have it saved and into PowerBI dashboards. Closing this out, see that

dashboards. Closing this out, see that the PowerBI file is now there. Notice

the PowerBI file is now there. Notice you're going to have this sort of error

you're going to have this sort of error message. This file is not displayed in

message. This file is not displayed in text ed because it's either binary and

text ed because it's either binary and well, it is binary. It doesn't support

well, it is binary. It doesn't support the method to actually view it here. You

the method to actually view it here. You can't view PowerBI files in VS Code. Not

can't view PowerBI files in VS Code. Not a big deal. going to close out of this.

a big deal. going to close out of this. Now, the next thing we want to do is

Now, the next thing we want to do is create a readme. So, up here on the

create a readme. So, up here on the icons, you can create a new file, a new

icons, you can create a new file, a new folder, and whatnot. We're going to

folder, and whatnot. We're going to create a new file. For this, we want to

create a new file. For this, we want to create a markdown file called readme.

create a markdown file called readme. It's really important we name this

It's really important we name this correctly. So, read me in all caps and

correctly. So, read me in all caps and then MD. When we do that, read me opens

then MD. When we do that, read me opens up on the right hand side for us to go

up on the right hand side for us to go through and text edit it. They have this

through and text edit it. They have this popup here for basically prompting us to

popup here for basically prompting us to use GitHub Copilot. You can use that if

use GitHub Copilot. You can use that if you want or not. We're not going to try

you want or not. We're not going to try to demo it for this video. Anyway, I can

to demo it for this video. Anyway, I can type things in here like this is a test.

type things in here like this is a test. And then if we want to see what it looks

And then if we want to see what it looks like, we come up here to the right hand

like, we come up here to the right hand side and we open the preview to the

side and we open the preview to the side. I'm going to minimize the explorer

side. I'm going to minimize the explorer right here. So, we can see things like

right here. So, we can see things like this is a test. Now, how we're going to

this is a test. Now, how we're going to go through some basics now of how to

go through some basics now of how to format in Markdown. Well, if you

format in Markdown. Well, if you navigate to the link below, this

navigate to the link below, this provides a cheat sheet of how you can

provides a cheat sheet of how you can use this basic syntax in order to mark

use this basic syntax in order to mark up your document. We're going to go

up your document. We're going to go through these basic items first. So,

through these basic items first. So, first thing is I want a heading. So, I'm

first thing is I want a heading. So, I'm going to put a hashtag and then followed

going to put a hashtag and then followed by what we want to put. In this case, I

by what we want to put. In this case, I put the title of our project. I could

put the title of our project. I could also do things like a heading. That was

also do things like a heading. That was a heading one. I could do a heading two

a heading one. I could do a heading two and put an introduction and then

and put an introduction and then underneath it put a couple sentences of

underneath it put a couple sentences of what we went through and actually did

what we went through and actually did here. Now, there's some things I want to

here. Now, there's some things I want to emphasize in this and so I want to bold

emphasize in this and so I want to bold them. In order to do that, I'm going to

them. In order to do that, I'm going to put two asterisk

put two asterisk asterises around where I want it to bold

asterises around where I want it to bold and luckily it makes it nice and

and luckily it makes it nice and highlighting it here inside the markdown

highlighting it here inside the markdown file, but then you can also see it in

file, but then you can also see it in the preview. I could also use something

the preview. I could also use something like a single asterisk to make things

like a single asterisk to make things into an italics. Now, let's move on to

into an italics. Now, let's move on to some other items. I'm going to go into

some other items. I'm going to go into what skills we showcased with this. And

what skills we showcased with this. And in this, I want to use bullet points.

in this, I want to use bullet points. So, what I can do is use a dash and then

So, what I can do is use a dash and then space. And as you can see, it made into

space. And as you can see, it made into a bullet point and then list the skill.

a bullet point and then list the skill. In this case, I put data transformation

In this case, I put data transformation ETL with Power Query and then put a

ETL with Power Query and then put a little synopsis about it. I'm going to

little synopsis about it. I'm going to go ahead and add some other skills as

go ahead and add some other skills as well. Feel free to choose which ones you

well. Feel free to choose which ones you want to highlight, but specifically I

want to highlight, but specifically I have implicit measures, core charts,

have implicit measures, core charts, geospatial analysis, KPIs, and dashboard

geospatial analysis, KPIs, and dashboard design. Also go over some interactive

design. Also go over some interactive reporting, how we use slicers, butings,

reporting, how we use slicers, butings, and drill through and whatnot. Anyway,

and drill through and whatnot. Anyway, whatever we want to highlight, put it

whatever we want to highlight, put it there. Now, if you remember from my repo

there. Now, if you remember from my repo on my readme, we had an image up at the

on my readme, we had an image up at the top. How the heck do we go about putting

top. How the heck do we go about putting an image in here? Well, navigating into

an image in here? Well, navigating into that PowerBI file, we're going to go

that PowerBI file, we're going to go ahead and use a tool called snip that's

ahead and use a tool called snip that's automatically on your Windows computer.

automatically on your Windows computer. For this, as it says, you need to press

For this, as it says, you need to press the Windows logo key plus shift plus S.

the Windows logo key plus shift plus S. So, I'm going to do that right here and

So, I'm going to do that right here and snapshot that bad boy. I selected markup

snapshot that bad boy. I selected markup and share. So, I want to go in and

and share. So, I want to go in and actually save it somewhere. So, I'm

actually save it somewhere. So, I'm going to navigate back to that PowerBI

going to navigate back to that PowerBI dashboards file folder that we have, and

dashboards file folder that we have, and I'm going to create a new folder in here

I'm going to create a new folder in here called images because I want to keep all

called images because I want to keep all my images in one spot. Not that I'm all

my images in one spot. Not that I'm all over the place. Anyway, inside of here,

over the place. Anyway, inside of here, I'm just going to call something like

I'm just going to call something like project one, page one. Then, I'm going

project one, page one. Then, I'm going to repeat this again for the second

to repeat this again for the second page. Saving it as project one, page

page. Saving it as project one, page two. Save it. So, now let's put this

two. Save it. So, now let's put this image inside of our readme. In our

image inside of our readme. In our little cheat sheet here, we can see that

little cheat sheet here, we can see that in order to insert an image, we need to

in order to insert an image, we need to use this syntax right here. So, I'm

use this syntax right here. So, I'm going to go ahead and just copy this and

going to go ahead and just copy this and then paste it right above the title.

then paste it right above the title. Notice here it's got this broken image

Notice here it's got this broken image because we haven't connected the image

because we haven't connected the image yet. I'm going to call this dashboard

yet. I'm going to call this dashboard page one. And then inside of parentheses

page one. And then inside of parentheses here, that's where we want to go to the

here, that's where we want to go to the location of the file. I'm going to hit

location of the file. I'm going to hit forward slash. And then this is going to

forward slash. And then this is going to pop up show me all the different files

pop up show me all the different files here in this repo. Remember if I open up

here in this repo. Remember if I open up the explorer I can also see them here.

the explorer I can also see them here. So let me demo that again. Whenever I

So let me demo that again. Whenever I hit this I get data jobs dashboard which

hit this I get data jobs dashboard which is right here. Images folder and read

is right here. Images folder and read me. Let's go into images. And then from

me. Let's go into images. And then from there I can do backslash again and

there I can do backslash again and insert in what file I want. I want this

insert in what file I want. I want this project one page one png. And then put

project one page one png. And then put that close parenthesis. And bam. Now

that close parenthesis. And bam. Now it's appearing right here on our

it's appearing right here on our readman. We'll close this out. Make this

readman. We'll close this out. Make this a little more readable. Now, below these

a little more readable. Now, below these skills showcased, I'd like to also go

skills showcased, I'd like to also go through and break down which uh contents

through and break down which uh contents or what each one of these pages

or what each one of these pages contains. So, I'm going to create a

contains. So, I'm going to create a heading two for dashboard overview and

heading two for dashboard overview and then do a heading three to de uh to go

then do a heading three to de uh to go over page one highle market view. For

over page one highle market view. For this, I'm just going to copy this image

this, I'm just going to copy this image that we have above and put it in right

that we have above and put it in right underneath this. Now, with that, now

underneath this. Now, with that, now that it's in there, I'm just going to

that it's in there, I'm just going to give a short little description under

give a short little description under this, maybe one to two sentences of what

this, maybe one to two sentences of what we're doing in this page one of our

we're doing in this page one of our dashboard. And then right underneath

dashboard. And then right underneath this, I'm going to do the same thing for

this, I'm going to do the same thing for page number two. And then finally,

page number two. And then finally, underneath it, I'm going to wrap it up

underneath it, I'm going to wrap it up with a conclusion. What are my lessons

with a conclusion. What are my lessons learned? What did I get out of this

learned? What did I get out of this project? All right. Bam. This project

project? All right. Bam. This project readme is done. One quick note, if you

readme is done. One quick note, if you did share this on the PowerBI service, I

did share this on the PowerBI service, I would go ahead and add that link in

would go ahead and add that link in right here, right below the picture, the

right here, right below the picture, the first picture. For links, what we're

first picture. For links, what we're going to do is we're going to put every

going to do is we're going to put every all the words that we want in the link

all the words that we want in the link in brackets and then in parentheses,

in brackets and then in parentheses, we're going to put the hyperlink to

we're going to put the hyperlink to where the dashboard is. It's similar to

where the dashboard is. It's similar to pictures, but it doesn't have that

pictures, but it doesn't have that exclamation point in the front of it.

exclamation point in the front of it. Anyway, I created a shorter link. And so

Anyway, I created a shorter link. And so now I whenever I go to click on this, it

now I whenever I go to click on this, it will navigate me into my web browser

will navigate me into my web browser which connects me directly to the

which connects me directly to the dashboards itself. Pretty neat.

dashboards itself. Pretty neat. All right, so we've set up our entire

All right, so we've set up our entire project folder and created that readme.

project folder and created that readme. Next thing we do, smooth sailing, we

Next thing we do, smooth sailing, we need to upload to GitHub and then share

need to upload to GitHub and then share on LinkedIn. So inside of VS Code, I'm

on LinkedIn. So inside of VS Code, I'm going to close out this preview, but I'm

going to close out this preview, but I'm going to go ahead and save this by

going to go ahead and save this by pressing Ctrl S. You can also just go up

pressing Ctrl S. You can also just go up into file and select save as well. And

into file and select save as well. And I'm going to close out of this. Open

I'm going to close out of this. Open back up that file explorer. Okay, so

back up that file explorer. Okay, so this is our folder structure right now,

this is our folder structure right now, right? We have a PowerBI file, the

right? We have a PowerBI file, the readme, and then our images in here. In

readme, and then our images in here. In project two, we're going to modify this

project two, we're going to modify this structure and also upload the same thing

structure and also upload the same thing into GitHub. But for now, being this is

into GitHub. But for now, being this is going to be perfectly fine. So I'm going

going to be perfectly fine. So I'm going to move on down here and go into source

to move on down here and go into source control. And what's going to happen with

control. And what's going to happen with this now is we have two options to

this now is we have two options to either initialize the repository and

either initialize the repository and that's basically setting up git to track

that's basically setting up git to track those changes within it. And then the

those changes within it. And then the second option is publish to GitHub which

second option is publish to GitHub which is basically initializing the repository

is basically initializing the repository and then publishing to GitHub. So it

and then publishing to GitHub. So it takes the next step of publishing to

takes the next step of publishing to GitHub. We want to do this second option

GitHub. We want to do this second option to just but because that's what our end

to just but because that's what our end goal is. So I'm going to go ahead and

goal is. So I'm going to go ahead and select publish to GitHub. and it says,

select publish to GitHub. and it says, "Hey, we want to sign into GitHub

"Hey, we want to sign into GitHub because right now VS Code is not linked

because right now VS Code is not linked to GitHub." Go ahead and authorize it.

to GitHub." Go ahead and authorize it. And I'm going to enable to always allow

And I'm going to enable to always allow VS Code to open links of this type. Oh

VS Code to open links of this type. Oh no, I didn't mean to click cancel.

no, I didn't mean to click cancel. Oh well, it'll be fine. In here for the

Oh well, it'll be fine. In here for the name of the dashboard, I'm going to

name of the dashboard, I'm going to press enter. Actually was a big deal. I

press enter. Actually was a big deal. I need to go back and select open. Anyway,

need to go back and select open. Anyway, it's asking, do we want to publish as a

it's asking, do we want to publish as a private or public repository? We want to

private or public repository? We want to make this public. It's asking us what

make this public. It's asking us what files do we want to include in this. We

files do we want to include in this. We want to include all of these files in

want to include all of these files in here. So, I'm going to select okay. It's

here. So, I'm going to select okay. It's telling me I'm thinking because I'm in

telling me I'm thinking because I'm in working in a virtual machine with

working in a virtual machine with parallels that I need to manage my

parallels that I need to manage my unsafe repositories. You may not have

unsafe repositories. You may not have that popup, but anyway, I selected it

that popup, but anyway, I selected it and moved on. Now, we're going to be

and moved on. Now, we're going to be committing this as their changes and

committing this as their changes and then from there pushing it to GitHub.

then from there pushing it to GitHub. So, I'm just going to enter a message in

So, I'm just going to enter a message in here. I'm going to enter in something

here. I'm going to enter in something original like first commit and then

original like first commit and then click commit. There's a popup and says

click commit. There's a popup and says there are no stage changes to commit.

there are no stage changes to commit. Okay. Would you like to stage all

Okay. Would you like to stage all changes and commit them directly? For

changes and commit them directly? For this, I will have this set to always. I

this, I will have this set to always. I always want to do this. Now, you may get

always want to do this. Now, you may get this error right here. Make sure you

this error right here. Make sure you configure your username and user email

configure your username and user email in Git. This is really common. So, what

in Git. This is really common. So, what I'm going to do is I'm going to cancel

I'm going to do is I'm going to cancel out of this. And then from there, in the

out of this. And then from there, in the search menu, I'm going to go enter git.

search menu, I'm going to go enter git. and we're going to go to get bash, which

and we're going to go to get bash, which is actually an app that we installed

is actually an app that we installed when we installed git. You're going to

when we installed git. You're going to type in this get config--global

type in this get config--global user.name Luke Bruce. So, we just set

user.name Luke Bruce. So, we just set our name. We now need to set our email.

our name. We now need to set our email. So, then we're going to run get

So, then we're going to run get config--global

config--global user.mail and then in double quotes your

user.mail and then in double quotes your email and then press enter. Now, back

email and then press enter. Now, back here in VS Code, it's going to say, hey,

here in VS Code, it's going to say, hey, I want to publish the branch. and it's

I want to publish the branch. and it's going to take you through. There's a

going to take you through. There's a little bit of a issue or a flaw with VS

little bit of a issue or a flaw with VS Code. It's going to try to take you

Code. It's going to try to take you through going through and setting up the

through going through and setting up the repository again. Say, "Hey, you've set

repository again. Say, "Hey, you've set this up already. It already exists." So,

this up already. It already exists." So, we got to do a little bit more

we got to do a little bit more interaction with the terminal. In order

interaction with the terminal. In order to do this, we're going to come up to

to do this, we're going to come up to the file menu and go into terminal and

the file menu and go into terminal and select, hey, we want a new terminal. And

select, hey, we want a new terminal. And we're going to run this command, get

we're going to run this command, get remote add origin. And it's really

remote add origin. And it's really important that we have the correct link

important that we have the correct link of where your dashboard is on GitHub.

of where your dashboard is on GitHub. Specifically, it should be github.com

Specifically, it should be github.com your username and then the name of our

your username and then the name of our dashboard and then.get. Go ahead and

dashboard and then.get. Go ahead and click enter. So, I did some changes up

click enter. So, I did some changes up here. Let's try this again. I have a

here. Let's try this again. I have a popup here to I don't know why it's

popup here to I don't know why it's super small like this. I got a popup

super small like this. I got a popup here and it says sign in with your

here and it says sign in with your computer. It asks if I want to authorize

computer. It asks if I want to authorize the Git ecosystem. I in fact do. And it

the Git ecosystem. I in fact do. And it looks like everything from VS Code is um

looks like everything from VS Code is um sent up there. So now inside of GitHub I

sent up there. So now inside of GitHub I can go here on the right hand side if I

can go here on the right hand side if I go to your repositories I can see that

go to your repositories I can see that my PowerBI dashboard is there that

my PowerBI dashboard is there that folder and it has everything all the

folder and it has everything all the folders and files that we had along with

folders and files that we had along with our readme that we created with all the

our readme that we created with all the different information in it. It's all

different information in it. It's all there. All right. There's only two steps

there. All right. There's only two steps left and they're both on LinkedIn. The

left and they're both on LinkedIn. The first is navigating down to your project

first is navigating down to your project section and adding in this new project.

section and adding in this new project. In here, I might give it a name, a short

In here, I might give it a name, a short description. I'm going to list some of

description. I'm going to list some of the key skills. You can list up to five,

the key skills. You can list up to five, but make sure you're calling out PowerBI

but make sure you're calling out PowerBI specifically. Also call it gout git and

specifically. Also call it gout git and GitHub. Adding media is probably the

GitHub. Adding media is probably the most important part. And that's for we

most important part. And that's for we want to include a link to our GitHub

want to include a link to our GitHub repo right here. Once that media is

repo right here. Once that media is added, just select a start and end date.

added, just select a start and end date. And then from there, go ahead click

And then from there, go ahead click save. Last main portion is now making a

save. Last main portion is now making a post in it. Feel free to call out myself

post in it. Feel free to call out myself and Kelly. I love checking out all your

and Kelly. I love checking out all your different projects. And then also share

different projects. And then also share that link to it. I included also a

that link to it. I included also a picture to make it a little more

picture to make it a little more interactive. And go ahead and post it.

interactive. And go ahead and post it. All right. So, you've made it this far

All right. So, you've made it this far in this video of sharing the dashboard.

in this video of sharing the dashboard. Congratulations. Sharing it, especially

Congratulations. Sharing it, especially GitHub, especially the first time, is

GitHub, especially the first time, is intimidating, but I promise you, the

intimidating, but I promise you, the more and more familiar you get with it,

more and more familiar you get with it, the easier it becomes. And it's my

the easier it becomes. And it's my recommended choice, this method of

recommended choice, this method of sharing that I use for sharing any type

sharing that I use for sharing any type of work, whether it's PowerBI, Python,

of work, whether it's PowerBI, Python, SQL, or whatnot. All right, in the next

SQL, or whatnot. All right, in the next video, we're going to be jumping

video, we're going to be jumping straight into more of an advanced

straight into more of an advanced section, jumping into Power Query, so we

section, jumping into Power Query, so we can see how to clean up some data. With

can see how to clean up some data. With that, I'll see you there.

All right, welcome to the second half of this course and we're about to crank

this course and we're about to crank things up a notch. Specifically, we have

things up a notch. Specifically, we have two chapters left. This one on Power

two chapters left. This one on Power Query and the next one on DAX. Both of

Query and the next one on DAX. Both of these are going to supercharge your

these are going to supercharge your powers and PowerBI in order to make more

powers and PowerBI in order to make more effective visualizations. Anyway, into

effective visualizations. Anyway, into this chapter. This one's going to be

this chapter. This one's going to be focused on Power Query. Power Query is a

focused on Power Query. Power Query is a tool used for ETL or extract, transform,

tool used for ETL or extract, transform, and load. You hear data engineers talk

and load. You hear data engineers talk about this all the time. Let's demo a

about this all the time. Let's demo a use case real quick. So, back to my old

use case real quick. So, back to my old days working as a data analyst. Every

days working as a data analyst. Every month, I'd get a new report similar to

month, I'd get a new report similar to this where in this case, that's the job

this where in this case, that's the job postings, but this is for all the job

postings, but this is for all the job postings in January. So, like any good

postings in January. So, like any good employee, I then take that Excel sheet

employee, I then take that Excel sheet for my boss and put it into a PowerBI

for my boss and put it into a PowerBI dashboard. this case, I have a job count

dashboard. this case, I have a job count and then also the jobs overtime in

and then also the jobs overtime in January. But then we get into our next

January. But then we get into our next month and February rolls around and I

month and February rolls around and I get this new Excel spreadsheet that we

get this new Excel spreadsheet that we can tell by job posted date has the jobs

can tell by job posted date has the jobs from February 2024. What the heck am I

from February 2024. What the heck am I supposed to do now? Well, I know what a

supposed to do now? Well, I know what a lot of you done before because I've done

lot of you done before because I've done it too. I've had to collect all this

it too. I've had to collect all this different data and specifically copying

different data and specifically copying it using control C and then navigating

it using control C and then navigating into my January file, scrolling all the

into my January file, scrolling all the way to the bottom and then putting this

way to the bottom and then putting this data in here inside of here, saving it,

data in here inside of here, saving it, and then going back into my PowerBI file

and then going back into my PowerBI file and clicking refresh now that that

and clicking refresh now that that January file is or that former January

January file is or that former January file is now January February. So I can

file is now January February. So I can get this new data in. And then these

get this new data in. And then these jobs are now updated to include not only

jobs are now updated to include not only January but also February. Also got to

January but also February. Also got to update this title. But our data is now

update this title. But our data is now updated. Mind you, I have to do that

updated. Mind you, I have to do that copy from that other file to the new

copy from that other file to the new file. If only there was an easier

file. If only there was an easier solution. Well, with Power Query, there

solution. Well, with Power Query, there is. All I have to do is put a folder

is. All I have to do is put a folder with the Excel files I need. So in this

with the Excel files I need. So in this case, I have the January and February.

case, I have the January and February. Let's now add in March. So I'll paste it

Let's now add in March. So I'll paste it in. And now we have three months worth

in. And now we have three months worth of data. Whenever I go back to PowerBI,

of data. Whenever I go back to PowerBI, click refresh. In this case, I've set it

click refresh. In this case, I've set it up to now pull from this folder. You

up to now pull from this folder. You didn't see this. I'll show this in the

didn't see this. I'll show this in the video. And now these files are updated

video. And now these files are updated for actually just go up to make this a

for actually just go up to make this a little bit easier. They're updated for

little bit easier. They're updated for January, February, and also March. Now,

January, February, and also March. Now, you may be like, Luke, you still had to

you may be like, Luke, you still had to click that refresh button. Well,

click that refresh button. Well, actually, if you put it into something

actually, if you put it into something like the PowerBI service using publish,

like the PowerBI service using publish, you can schedule automated refreshes.

you can schedule automated refreshes. But we're getting ahead of oursel.

So, just make sure we're on the same page. What is Power Query? Like I said,

page. What is Power Query? Like I said, it's an ETL tool in order to form

it's an ETL tool in order to form extraction, transformation, and loading.

extraction, transformation, and loading. In that previous example, we extracted

In that previous example, we extracted data out of a folder. We transformed it

data out of a folder. We transformed it by putting it all into a single table

by putting it all into a single table and then loaded it here into power query

and then loaded it here into power query or into PowerBI so we could visualize

or into PowerBI so we could visualize it. ETL you access power query inside of

it. ETL you access power query inside of PowerBI

PowerBI underneath the home tab. Basically this

underneath the home tab. Basically this entire section right here on data and

entire section right here on data and also queries. This is Power Query. Fun

also queries. This is Power Query. Fun fact, if you're inside of Excel, you

fact, if you're inside of Excel, you would just navigate to the data tab. And

would just navigate to the data tab. And there, like right here on get and

there, like right here on get and transform data and queries and

transform data and queries and connections. This is the portion that

connections. This is the portion that deals with Power Query. Everything that

deals with Power Query. Everything that you're going to learn from me today in

you're going to learn from me today in using Power Query inside of PowerBI is

using Power Query inside of PowerBI is going to be able to replicated and used

going to be able to replicated and used inside of Excel. So, this chapter on

inside of Excel. So, this chapter on Power Query is going to be broken up

Power Query is going to be broken up into six different lessons. In this

into six different lessons. In this lesson, we're going to have an intro, do

lesson, we're going to have an intro, do some basic examples, and then next one,

some basic examples, and then next one, we're going to get into using basically

we're going to get into using basically another guey called the Power Query

another guey called the Power Query Editor in order to edit our

Editor in order to edit our transformations. In the third lesson,

transformations. In the third lesson, we're going to be moving into actually

we're going to be moving into actually importing in the data we're going to be

importing in the data we're going to be using for the final project. And then

using for the final project. And then from there, with the three remaining

from there, with the three remaining lessons, we're going to get into some

lessons, we're going to get into some more advanced techniques and using

more advanced techniques and using things like the M language, append, and

things like the M language, append, and merge, and whatnot. Now, regarding the

merge, and whatnot. Now, regarding the PowerBI files for this chapter, things

PowerBI files for this chapter, things are going to be a little bit different

are going to be a little bit different from what we did previously. In this,

from what we did previously. In this, we're going to have a file for each

we're going to have a file for each lesson. These files are going to be what

lesson. These files are going to be what is done upon completion of a lesson. So,

is done upon completion of a lesson. So, in our case, 3.1 Power Query Intro. This

in our case, 3.1 Power Query Intro. This is the completed file at the end of this

is the completed file at the end of this video.

and using those files. It's actually a great segue into getting into how to use

great segue into getting into how to use Power Query inside the home ribbon. Now,

Power Query inside the home ribbon. Now, if you were to open up this Power Query

if you were to open up this Power Query intro file and then were to hit

intro file and then were to hit something like refresh in order to

something like refresh in order to connect to all these different data

connect to all these different data sources that we are going to do and try

sources that we are going to do and try to load them in, you're going to get a

to load them in, you're going to get a few errors. Specifically with our

few errors. Specifically with our monthly files, these are located on my

monthly files, these are located on my local machine. So the path for these is

local machine. So the path for these is in power query is directing them to my

in power query is directing them to my machine. You need to update them from

machine. You need to update them from where they are for you. So I'm going to

where they are for you. So I'm going to do is close this. Go here underneath the

do is close this. Go here underneath the queries under transform data into data

queries under transform data into data source settings. In our case, you're

source settings. In our case, you're going to look for the folder icon, in

going to look for the folder icon, in this case the Z desktop monthly files,

this case the Z desktop monthly files, and you're going to go to change source.

and you're going to go to change source. And then from there, you're going to go

And then from there, you're going to go to browse and you'll locate to the area

to browse and you'll locate to the area that you have those monthly files in.

that you have those monthly files in. Click okay and okay and then close. And

Click okay and okay and then close. And then upon refreshing this, the query

then upon refreshing this, the query should load with no issues. And bam,

should load with no issues. And bam, there we have it. Anyway, so this is a

there we have it. Anyway, so this is a completed file. Let's get out of this

completed file. Let's get out of this and get into a blank PowerBI file to get

and get into a blank PowerBI file to get through this lesson. So moving on into

through this lesson. So moving on into exploring this, the first thing to

exploring this, the first thing to understand is we have an option to get

understand is we have an option to get data. And this gets data from a

data. And this gets data from a multitude of different sources. As we've

multitude of different sources. As we've saw previously, I prefer using this more

saw previously, I prefer using this more option anytime I'm trying to look for

option anytime I'm trying to look for things. There's some major types that we

things. There's some major types that we can look at. One is file things like

can look at. One is file things like Excel, text files, PDFs, whatnot. The

Excel, text files, PDFs, whatnot. The next are databases, which we're going to

next are databases, which we're going to eventually get to demoing for this. And

eventually get to demoing for this. And databases are by far, especially in the

databases are by far, especially in the business world, are the source that I'm

business world, are the source that I'm going to be using to get data primarily

going to be using to get data primarily behind files themselves, like Excel

behind files themselves, like Excel files. Now, beyond file and databases,

files. Now, beyond file and databases, there's options that are specific to the

there's options that are specific to the Microsoft platform that if your

Microsoft platform that if your company's invested billions or millions

company's invested billions or millions of dollars into this, they probably have

of dollars into this, they probably have access to these, such as Azure as well.

access to these, such as Azure as well. And the only other other one we'll call

And the only other other one we'll call out is other, specifically other because

out is other, specifically other because we're going to be able to import data

we're going to be able to import data from things like a web page or you can

from things like a web page or you can even do things like R script or Python

even do things like R script or Python script. Now, navigating back now that

script. Now, navigating back now that we've seen all that, you can see that

we've seen all that, you can see that these three options right here are

these three options right here are basically just quick actions from the

basically just quick actions from the get data. So, I don't find myself using

get data. So, I don't find myself using unless I'm going directly to like an

unless I'm going directly to like an Excel workbook. We've seen previously

Excel workbook. We've seen previously also how we can just enter data in and

also how we can just enter data in and create our own table. That's done

create our own table. That's done through Power Query. And then once again

through Power Query. And then once again data versse is another source or recent

data versse is another source or recent sources itself you can connect right to.

sources itself you can connect right to. Now moving over to this query section

Now moving over to this query section under transform data. We've explored

under transform data. We've explored data source settings. But then there's

data source settings. But then there's transform data and this is how we're

transform data and this is how we're going to get to the power query editor.

going to get to the power query editor. We're not going to touch this editor in

We're not going to touch this editor in this first lesson. We're going to keep

this first lesson. We're going to keep it simple and just load data using the

it simple and just load data using the most simple method to get into PowerBI

most simple method to get into PowerBI without getting to the editor. The last

without getting to the editor. The last two things on here that are grayed out

two things on here that are grayed out are edit uh parameters and also edit

are edit uh parameters and also edit variables. Both of these are outside of

variables. Both of these are outside of the scope of this course. Editing

the scope of this course. Editing parameters and variables are more

parameters and variables are more advanced techniques and I don't think

advanced techniques and I don't think that they're necessarily necessary for

that they're necessarily necessary for the basics. So, we're not going to be

the basics. So, we're not going to be covering it. And the last button is

covering it. And the last button is refresh, which you've seen me demo just

refresh, which you've seen me demo just recently of getting those monthly files

recently of getting those monthly files updated. But that's how we update any

updated. But that's how we update any type of query. is clicking refresh.

All right, enough of the theory. Let's actually get into some examples

actually get into some examples demonstrating power query. First one is

demonstrating power query. First one is this. Now, let's say I want to do an

this. Now, let's say I want to do an analysis to understand how things like

analysis to understand how things like GDP, gross domestic product compares to

GDP, gross domestic product compares to or how it has an impact on maybe

or how it has an impact on maybe salaries in different countries. And so

salaries in different countries. And so what I can do is I went to Bing here. I

what I can do is I went to Bing here. I uh binged if you will countries by GDP

uh binged if you will countries by GDP by sector and this first result of

by sector and this first result of Wikipedia popup. Anyway, this Wikipedia

Wikipedia popup. Anyway, this Wikipedia page includes tables in it with in this

page includes tables in it with in this case it's the nominal GDP and then also

case it's the nominal GDP and then also this real GDP. Anyway, this data is in

this real GDP. Anyway, this data is in here in a table. Previously, I know

here in a table. Previously, I know you've probably done this before. You

you've probably done this before. You probably come in here and tried to

probably come in here and tried to select it and then try to copy it. Well,

select it and then try to copy it. Well, instead power query simplifies this. I

instead power query simplifies this. I can just copy this address right here.

can just copy this address right here. Pressing C and then navigating to get

Pressing C and then navigating to get data and we want to get this from this

data and we want to get this from this web source right here from a web page.

web source right here from a web page. We'll keep it in the basic. All we have

We'll keep it in the basic. All we have to do is just paste in that URL. Click

to do is just paste in that URL. Click okay. And what's really neat now is it

okay. And what's really neat now is it shows me all the different tables within

shows me all the different tables within here. I can even select like table two,

here. I can even select like table two, which is the one we're going to get.

which is the one we're going to get. Anyway, it's really important that you

Anyway, it's really important that you go through and select which table you

go through and select which table you actually want because a lot of these

actually want because a lot of these tables are not really useful. Table 2 is

tables are not really useful. Table 2 is the most useful for us. I'm going to

the most useful for us. I'm going to select this and then there's three

select this and then there's three options down there. Load, transform

options down there. Load, transform data, and cancel. If I click transform

data, and cancel. If I click transform data, what's going to happen is it's

data, what's going to happen is it's going to take me into the Power Query

going to take me into the Power Query editor itself. And it's not a big deal

editor itself. And it's not a big deal if you did this. You can just go ahead

if you did this. You can just go ahead click close and apply. Now, the other

click close and apply. Now, the other option instead of hitting transform data

option instead of hitting transform data is just clicking load. And this

is just clicking load. And this basically bypasses going into that power

basically bypasses going into that power query editor and just loads it directly

query editor and just loads it directly into here as it's doing now. And then

into here as it's doing now. And then bam, we have this table inside of here.

bam, we have this table inside of here. All the different fields. I can inspect

All the different fields. I can inspect it inside the table view. And all the

it inside the table view. And all the columns look like they're coming up

columns look like they're coming up correctly. I am going to rechange the

correctly. I am going to rechange the name of this to GDP nominal. And then

name of this to GDP nominal. And then updating it there. It's also going to

updating it there. It's also going to update it inside the data pane. So now I

update it inside the data pane. So now I can make something like a map visual,

can make something like a map visual, throw in the country into the location,

throw in the country into the location, and then for the bubble size, put in

and then for the bubble size, put in that total GDP. I could also just

that total GDP. I could also just duplicate this bad boy and make it into

duplicate this bad boy and make it into a stacked bar chart. In this case, the

a stacked bar chart. In this case, the world is well everything for it. So in

world is well everything for it. So in this visual itself, I could do something

this visual itself, I could do something like just filter out world by unchecking

like just filter out world by unchecking it and bam. Pretty crazy how we can just

it and bam. Pretty crazy how we can just now get this data from online directly

now get this data from online directly into our PowerBI file and then next year

into our PowerBI file and then next year or then the following year whenever this

or then the following year whenever this data updates all we got to do is go and

data updates all we got to do is go and click refresh assuming the table name

click refresh assuming the table name doesn't change it will go forward with

doesn't change it will go forward with refreshing and getting that updated

refreshing and getting that updated data.

Now, let's get into demoing how we can connect to a data source such as a

connect to a data source such as a folder as we did in that first exercise

folder as we did in that first exercise in this video. For this, I'm going to

in this video. For this, I'm going to start a new page that we can build any

start a new page that we can build any visualizations on. And we're going to go

visualizations on. And we're going to go into get data. And for this, I'm going

into get data. And for this, I'm going to go to more. And inside of here, I'm

to go to more. And inside of here, I'm going to type in folder. Now, there's

going to type in folder. Now, there's actually two options for folder. Folder

actually two options for folder. Folder itself, which is a folder on your local

itself, which is a folder on your local machine, and SharePoint folder. If

machine, and SharePoint folder. If you're using the Microsoft ecosystem,

you're using the Microsoft ecosystem, this is what I've used in the past where

this is what I've used in the past where basically I've had another stakeholder

basically I've had another stakeholder that was in charge of the data and they

that was in charge of the data and they were just in part in charge of putting

were just in part in charge of putting the data into the SharePoint folder and

the data into the SharePoint folder and then in PowerBI service it would

then in PowerBI service it would automatically update from there. We're

automatically update from there. We're not going to be working with SharePoint

not going to be working with SharePoint folder because I'm assuming you don't

folder because I'm assuming you don't have access to it. I don't even have

have access to it. I don't even have access to it. For this exercise, we're

access to it. For this exercise, we're going to be using the data folder,

going to be using the data folder, specifically these monthly files.

specifically these monthly files. Remember, we have files broken up for

Remember, we have files broken up for the same data broken up over every

the same data broken up over every single month. Anyway, for this example,

single month. Anyway, for this example, I'm going to start by only uploading

I'm going to start by only uploading these first three months. So, I'm going

these first three months. So, I'm going to just move these other ones out. Put

to just move these other ones out. Put them on my desktop for now. And I

them on my desktop for now. And I thought it was going to delete it for it

thought it was going to delete it for it moved it out of there. Apparently, it

moved it out of there. Apparently, it doesn't. So, I'm going to delete it by

doesn't. So, I'm going to delete it by right clicking, select delete. Yes, I

right clicking, select delete. Yes, I want to delete cuz I have copies on my

want to delete cuz I have copies on my desktop. All right, let's get into

desktop. All right, let's get into importing in this folder. So, I'm going

importing in this folder. So, I'm going to go into folder and click connect.

to go into folder and click connect. From there, you're going to navigate to

From there, you're going to navigate to where the folder is with the correct

where the folder is with the correct path in there. I'm going to click okay.

path in there. I'm going to click okay. And then we have this navigator window

And then we have this navigator window pop open. And in this, we can see

pop open. And in this, we can see basically the rows here are outlining

basically the rows here are outlining the three separate files or three Excel

the three separate files or three Excel files that we have in there, right?

files that we have in there, right? Because if I navigate to that folder on

Because if I navigate to that folder on my desktop, I see that yeah, it does

my desktop, I see that yeah, it does match those three files. But how we're

match those three files. But how we're going to combine it? Well, we'll get to

going to combine it? Well, we'll get to that. Anyway, we've gone over these

that. Anyway, we've gone over these buttons before. of transform data,

buttons before. of transform data, right? Because if we've transformed it,

right? Because if we've transformed it, we're going to enter the power query

we're going to enter the power query adder. They also have this load. If we

adder. They also have this load. If we were to click load, which is not

were to click load, which is not actually what we want to do, it's going

actually what we want to do, it's going to load all these monthly files in. But

to load all these monthly files in. But this is going to be basically in a

this is going to be basically in a table, meaning navigating to the table

table, meaning navigating to the table view, it just tells us about the Excel

view, it just tells us about the Excel file. It doesn't give us the data. It

file. It doesn't give us the data. It didn't combine it. So, I'm actually

didn't combine it. So, I'm actually going to just go ahead and delete this

going to just go ahead and delete this from model and start over again. And

from model and start over again. And then back to navigating through all

then back to navigating through all those steps to load it in. In this case,

those steps to load it in. In this case, I'm not going to do load or transform.

I'm not going to do load or transform. I'm going to go into combine. And then

I'm going to go into combine. And then it says, hey, you can combine and

it says, hey, you can combine and transform data or combine and load data.

transform data or combine and load data. Like I said, we don't want to get I'm

Like I said, we don't want to get I'm not going into the Power Query out of

not going into the Power Query out of this lesson. So, we're just going to go

this lesson. So, we're just going to go to combine and load. Now, there's a

to combine and load. Now, there's a couple more steps we have to navigate

couple more steps we have to navigate to. We need to select the object to be

to. We need to select the object to be extracted from each file. In this case,

extracted from each file. In this case, we're going to collect uh select this

we're going to collect uh select this sheet one and it's using the first file.

sheet one and it's using the first file. We could also call out a specific one

We could also call out a specific one like I could call out January in this

like I could call out January in this case and click this as well. I recommend

case and click this as well. I recommend just doing first file in case January

just doing first file in case January ever gets replaced. So, I'm going to go

ever gets replaced. So, I'm going to go ahead and change it. Select sheet one

ahead and change it. Select sheet one and then select okay. And now it's going

and then select okay. And now it's going through the mo loading process of

through the mo loading process of accessing each of these monthly files.

accessing each of these monthly files. As you see, it did March, February, and

As you see, it did March, February, and now January. Inspecting the monthly

now January. Inspecting the monthly files underneath the table view. I can

files underneath the table view. I can see that it looks like we have 156,000

see that it looks like we have 156,000 rows, which sounds about right. So to

rows, which sounds about right. So to visualize it, I create a stack bar

visualize it, I create a stack bar chart, put job title short into the

chart, put job title short into the y-axis, and then the count of job title

y-axis, and then the count of job title short into the x-axis. But let's

short into the x-axis. But let's actually view this over time. time. So,

actually view this over time. time. So, I'm going to insert in a line chart and

I'm going to insert in a line chart and we'll throw job posted date into the

we'll throw job posted date into the x-axis and then count into the y-axis.

x-axis and then count into the y-axis. If you notice from this, this count is

If you notice from this, this count is this this line chart is just okay, this

this this line chart is just okay, this is a mess to look at. And the problem is

is a mess to look at. And the problem is that that job posted date is not in a

that that job posted date is not in a date format. We can actually fix this in

date format. We can actually fix this in power query editor. Like I said, we're

power query editor. Like I said, we're staying out of that today. So for the

staying out of that today. So for the time being I'm just going to select this

time being I'm just going to select this and change the format to a data type of

and change the format to a data type of date time says hey do I want to change

date time says hey do I want to change this? Yeah I want to change it. It'll

this? Yeah I want to change it. It'll say hey one or more calculate objects

say hey one or more calculate objects need to be manually refreshed. Refresh

need to be manually refreshed. Refresh them now. And now I'm going to X out of

them now. And now I'm going to X out of this with job post to date. As we can

this with job post to date. As we can see job posted date now has a date

see job posted date now has a date hierarchy. So whenever I drag it onto

hierarchy. So whenever I drag it onto here it's aggregating it by day and by

here it's aggregating it by day and by month and by year. So drilling on down I

month and by year. So drilling on down I like this day view. This is good. Now,

like this day view. This is good. Now, what happens if we get more files? Well,

what happens if we get more files? Well, whenever we add them into here, they're

whenever we add them into here, they're now in this folder, but this this still

now in this folder, but this this still needs to refresh, right? So, remember,

needs to refresh, right? So, remember, we have to still click refresh all

we have to still click refresh all tables, and it's going to go into

tables, and it's going to go into monthly files, specifically into the

monthly files, specifically into the queries. And in this case, it's going to

queries. And in this case, it's going to access each of those. Right now, it's

access each of those. Right now, it's August, November, May. And then we have

August, November, May. And then we have all of it for the year. And this is just

all of it for the year. And this is just unreadable. some navigate up one and

unreadable. some navigate up one and we'll be able to see it on a monthly

we'll be able to see it on a monthly basis. Now,

basis. Now, the last example we're going to get to

the last example we're going to get to is like I said the most real world

is like I said the most real world example of connecting to a database.

example of connecting to a database. Specifically, we're going to get and

Specifically, we're going to get and connect to the database behind data.te.

connect to the database behind data.te. This app, which has collected up to 3.6

This app, which has collected up to 3.6 million jobs at the time of filming

million jobs at the time of filming this, has all of this in a database.

this, has all of this in a database. Specifically, it's a big query database.

Specifically, it's a big query database. Actually, just to prove that I have

Actually, just to prove that I have access to it, here I am inside of my

access to it, here I am inside of my Google Cloud account. I'm connected to

Google Cloud account. I'm connected to the table itself. Here's all the

the table itself. Here's all the different columns. Has a lot more

different columns. Has a lot more columns than you're used to seeing.

columns than you're used to seeing. Anyway, the details inside of it. It has

Anyway, the details inside of it. It has 3.6 million rows inside of it. I can

3.6 million rows inside of it. I can also, if I wanted to, see a preview of

also, if I wanted to, see a preview of the data within it. Anyway, we're going

the data within it. Anyway, we're going to use Power Query to connect to this

to use Power Query to connect to this online database. And by we, I mean me.

online database. And by we, I mean me. Unfortunately, it would cost entirely

Unfortunately, it would cost entirely too much money to try to get everybody

too much money to try to get everybody in account access. Also, I'd have to pay

in account access. Also, I'd have to pay for the resources for everybody

for the resources for everybody accessing it. It just be a giant

accessing it. It just be a giant headache. Sorry, it's only going to be

headache. Sorry, it's only going to be me. Anyway, I'm going to click get data

me. Anyway, I'm going to click get data and then more. And then from there,

and then more. And then from there, you're going to search for whatever

you're going to search for whatever database you have it in. In my case, I

database you have it in. In my case, I use BigQuery. So, I'm going to select

use BigQuery. So, I'm going to select BigQuery and select connect. From there,

BigQuery and select connect. From there, I'm going to specify all of my

I'm going to specify all of my connection information. I can also give

connection information. I can also give a SQL statement if I want to get maybe a

a SQL statement if I want to get maybe a subset of the data. Now, what you didn't

subset of the data. Now, what you didn't see prior to this is I actually had to

see prior to this is I actually had to go through log into Google and provide

go through log into Google and provide my credentials to access it. So, there

my credentials to access it. So, there is another step to get to to make sure

is another step to get to to make sure that just nobody can access your

that just nobody can access your database. Anyway, I navigated into the

database. Anyway, I navigated into the table itself and looking at it, it looks

table itself and looking at it, it looks like it's right. I could verify by going

like it's right. I could verify by going back to BigQuery, checking it out and be

back to BigQuery, checking it out and be like, "Yeah, that matches the columns."

like, "Yeah, that matches the columns." And then from there, we have options of

And then from there, we have options of load, transform data, i.e. open the

load, transform data, i.e. open the power query editor or cancel. I'm just

power query editor or cancel. I'm just going to load it in. Now, this is

going to load it in. Now, this is something new that we haven't covered

something new that we haven't covered yet. And it's asking me how do I want to

yet. And it's asking me how do I want to basically bring all this data in. Do I

basically bring all this data in. Do I want to import it or do I want to direct

want to import it or do I want to direct query it? Now, in all of our previous

query it? Now, in all of our previous example, we've always used import mode.

example, we've always used import mode. But we've never been given the option to

But we've never been given the option to do direct query. And import mode just

do direct query. And import mode just means we import in all the data. Now

means we import in all the data. Now direct query does not import it in

direct query does not import it in meaning the file size is much smaller.

meaning the file size is much smaller. The PowerBI file size is much smaller.

The PowerBI file size is much smaller. And then every time that you want to

And then every time that you want to maybe update a visualization, it has to

maybe update a visualization, it has to go out and get that data. So therefore

go out and get that data. So therefore with performance for import mode, it's

with performance for import mode, it's very fast. For direct query, super slow

very fast. For direct query, super slow depending on how big the data source is.

depending on how big the data source is. Import mode supports basically all our

Import mode supports basically all our different functionality. Yes, you have

different functionality. Yes, you have to manually refresh inside the PowerBI

to manually refresh inside the PowerBI app, but you can also set that up in the

app, but you can also set that up in the service. Direct query doesn't support

service. Direct query doesn't support other advanced features such as like

other advanced features such as like accessing this data while you're offline

accessing this data while you're offline or supporting full DAX, which we'll be

or supporting full DAX, which we'll be demonstrating in chapter 4. Anyway, the

demonstrating in chapter 4. Anyway, the point is you have to raise your costs

point is you have to raise your costs and your benefits of what you want to

and your benefits of what you want to actually accomplish with this. I have

actually accomplish with this. I have 3.6 million rows of data to get into

3.6 million rows of data to get into here. that would make this a heck of a

here. that would make this a heck of a size of a file and so I'm okay with

size of a file and so I'm okay with being a little bit slower and I'm going

being a little bit slower and I'm going to go with direct query. Now let's get

to go with direct query. Now let's get into inspecting it. If I wanted to or

into inspecting it. If I wanted to or what I can't actually do if I went to

what I can't actually do if I went to table view I can't view it because it's

table view I can't view it because it's not an import mo mode. It's in that

not an import mo mode. It's in that direct query. So unfortunately

direct query. So unfortunately everything I want to view from it I have

everything I want to view from it I have to do from the canvas. So I select a new

to do from the canvas. So I select a new card and I'm going to drag the job ID

card and I'm going to drag the job ID into the fields. And specifically I want

into the fields. And specifically I want a count of this. And this tells me I

a count of this. And this tells me I have 3.65

have 3.65 million jobs. Let's add a few more

million jobs. Let's add a few more visualizations. And bam, we get this bad

visualizations. And bam, we get this bad boy. So, I'm able to look at all the

boy. So, I'm able to look at all the different values we've looked at

different values we've looked at previously.

previously. But I will show something with this. If

But I will show something with this. If I'm trying to actually cross filter, in

I'm trying to actually cross filter, in this case, I selected software engineer.

this case, I selected software engineer. You can see everything is going through

You can see everything is going through and trying to load with 3.6 million rows

and trying to load with 3.6 million rows in a database. This is going to take a

in a database. This is going to take a little bit of time. And it looks like

little bit of time. And it looks like it's finally updated. I still have one

it's finally updated. I still have one more. Okay, everything's now loaded.

more. Okay, everything's now loaded. Now, if I wanted to at any point, if I

Now, if I wanted to at any point, if I wanted to switch this from that direct

wanted to switch this from that direct query into import mode, I could come

query into import mode, I could come down here to storage mode and say, "Hey,

down here to storage mode and say, "Hey, switch all tables to import." Once you

switch all tables to import." Once you do this though, you can't revert back to

do this though, you can't revert back to the direct query. Now, with direct

the direct query. Now, with direct query, we were able to load this in. You

query, we were able to load this in. You mean you saw how fast we went through

mean you saw how fast we went through the data source. I'm going through right

the data source. I'm going through right now, and it's loading in the rows. Look

now, and it's loading in the rows. Look at this. is at 350,000

at this. is at 350,000 and we still have to get to 3.6 million.

and we still have to get to 3.6 million. Also, we're still loading the data. So,

Also, we're still loading the data. So, the file hasn't updated uh necessarily

the file hasn't updated uh necessarily just yet, but just for reference to

just yet, but just for reference to remember this, right, the data size of

remember this, right, the data size of the file right now is at 11 megabytes.

the file right now is at 11 megabytes. And we're still loading. And it looks

And we're still loading. And it looks like we're wrapping up these rows of

like we're wrapping up these rows of this database. All right. So, truth be

this database. All right. So, truth be told, I got done with or was getting

told, I got done with or was getting close to loading and that file end up

close to loading and that file end up crashing. I didn't want to go through

crashing. I didn't want to go through that reload process again. So, I opened

that reload process again. So, I opened an old file that I worked with

an old file that I worked with previously. This one is connected with

previously. This one is connected with direct import to that database. This one

direct import to that database. This one only has 3.49 million cuz I did this a

only has 3.49 million cuz I did this a month ago. Anyway, it still has all the

month ago. Anyway, it still has all the data inside of here. But notice how fast

data inside of here. But notice how fast whenever I actually click this, how fast

whenever I actually click this, how fast it actually filters down because it's

it actually filters down because it's imported in. Now, there's a major

imported in. Now, there's a major drawback from that. Remember, our

drawback from that. Remember, our previous file was only 11 megabytes.

previous file was only 11 megabytes. This bad boy is 2500

This bad boy is 2500 megabytes.

megabytes. So nearly 200 times bigger. So importing

So nearly 200 times bigger. So importing a database isn't necessarily as simple

a database isn't necessarily as simple as importing a database cuz sometimes

as importing a database cuz sometimes you have to decide if you're going to

you have to decide if you're going to use import mode or direct query. How's

use import mode or direct query. How's it going to affect a performance or how

it going to affect a performance or how that's going to affect file size. My

that's going to affect file size. My recommendation is you just use import

recommendation is you just use import and if you can clean up your data into

and if you can clean up your data into as smallest size as possible before

as smallest size as possible before actually bring it into PowerBI. If

actually bring it into PowerBI. If that's not an option, go with direct

that's not an option, go with direct query. All right. So, now that we have

query. All right. So, now that we have that in-depth coverage of how we can

that in-depth coverage of how we can import databases, but also other data

import databases, but also other data sources as well, we now have some

sources as well, we now have some practice problems for you to go through

practice problems for you to go through and connect to some other different data

and connect to some other different data sources as well. With that, in the next

sources as well. With that, in the next lesson, we're going to be jumping into

lesson, we're going to be jumping into the Power Query editor. See you there.

Now that we're master at understanding what are all the different types of data

what are all the different types of data sources we can connect to. Now let's

sources we can connect to. Now let's jump into the Power Query editor itself

jump into the Power Query editor itself and actually get to cleaning up some

and actually get to cleaning up some data. Now in this video we're going to

data. Now in this video we're going to be performing cleanup of our CSV file

be performing cleanup of our CSV file that we've previously been using and

that we've previously been using and we're going to do that with the Power

we're going to do that with the Power Query editor while exploring it. Anyway,

Query editor while exploring it. Anyway, the first case we're going to take

the first case we're going to take advantage of, as we saw previously, that

advantage of, as we saw previously, that job posted date time. Whenever we drag

job posted date time. Whenever we drag it into a line chart, it doesn't do it

it into a line chart, it doesn't do it because the data wasn't correct in being

because the data wasn't correct in being in a hierarchy or recognizes date time

in a hierarchy or recognizes date time as it was here. Demoed that in the last

as it was here. Demoed that in the last lesson. Anyway, we're going to use Power

lesson. Anyway, we're going to use Power Query to actually clean up this column.

Query to actually clean up this column. The other thing we're going to do, which

The other thing we're going to do, which is quite common with Power Query, is

is quite common with Power Query, is create new columns that we can then

create new columns that we can then analyze further with. Specifically here,

analyze further with. Specifically here, we're going to be analyzing a column

we're going to be analyzing a column called salary hour adjusted. And that

called salary hour adjusted. And that adjusted column is going to take our

adjusted column is going to take our salary hour average column and multiply

salary hour average column and multiply it times 2080 or basically 40 hours

it times 2080 or basically 40 hours times 52 weeks in a year. And therefore

times 52 weeks in a year. And therefore we can understand what would be the

we can understand what would be the yearly salary based on an hourly pay.

yearly salary based on an hourly pay. Anyway, we're going to make these charts

Anyway, we're going to make these charts associated with it. And this allows us

associated with it. And this allows us now to compare yearly median salary to

now to compare yearly median salary to now hourly adjusted salary like we're

now hourly adjusted salary like we're doing in this line chart.

doing in this line chart. So let's not get ahead of oursel. We're

So let's not get ahead of oursel. We're going to get an intro now into the Power

going to get an intro now into the Power Query editor. And for this lesson, feel

Query editor. And for this lesson, feel free to just start a new blank report.

free to just start a new blank report. Inside out of our new file, let's

Inside out of our new file, let's connect to our previous CSV that we've

connect to our previous CSV that we've been using. Selecting this of text CSV

been using. Selecting this of text CSV and then navigating to the data source

and then navigating to the data source itself of job postings flat. Select

itself of job postings flat. Select open. Inside the navigator window,

open. Inside the navigator window, remember we don't want to go to load. We

remember we don't want to go to load. We want to actually we're going to explore

want to actually we're going to explore the power query editor itself. So we're

the power query editor itself. So we're going to go into transform data. So,

going to go into transform data. So, let's go over this UI of this Power

let's go over this UI of this Power Query editor that just opened up. It has

Query editor that just opened up. It has a very similar format to all Microsoft

a very similar format to all Microsoft products in that up at the top, we have

products in that up at the top, we have the ribbon itself. Then underneath this,

the ribbon itself. Then underneath this, we have the data view area. And it may

we have the data view area. And it may be confusing to some cuz it looks like

be confusing to some cuz it looks like it's Excel, but I can't go in here and

it's Excel, but I can't go in here and like try like I'm trying to I can't type

like try like I'm trying to I can't type inside of here. This is just showing me

inside of here. This is just showing me what is the current view view of the

what is the current view view of the query that we're operating on. So this

query that we're operating on. So this has all the different columns of our

has all the different columns of our final data set. With this we have a

final data set. With this we have a queries pane over to the left hand side.

queries pane over to the left hand side. So we're currently operating on the job

So we're currently operating on the job postings flat query which is this query.

postings flat query which is this query. Conveniently they just named it this

Conveniently they just named it this based on our CSV. And then over here on

based on our CSV. And then over here on the right hand side is our query

the right hand side is our query settings. And notice here, so we have

settings. And notice here, so we have our name right here. If I wanted to, I

our name right here. If I wanted to, I could name it to just job postings.

could name it to just job postings. Click enter. And then our query itself

Click enter. And then our query itself updates that. I want job postings flat,

updates that. I want job postings flat, so we're going to leave it as that. And

so we're going to leave it as that. And then underneath this, we have what steps

then underneath this, we have what steps were applied in Power Query for this. So

were applied in Power Query for this. So I can actually go back into previous

I can actually go back into previous steps. Right now, we're on change type.

steps. Right now, we're on change type. That's the last and most recent step. I

That's the last and most recent step. I can go to promoted headers. And in this

can go to promoted headers. And in this one, there's not really much of a

one, there's not really much of a difference. But if I go into the source

difference. But if I go into the source step, we can actually see that from the

step, we can actually see that from the source itself, it imported in and those

source itself, it imported in and those job titles or sorry the uh actual column

job titles or sorry the uh actual column headers were on row one. So that's why

headers were on row one. So that's why it went through once it did the source,

it went through once it did the source, then it went into promoted headers and

then it went into promoted headers and then finally into change type. Sometime

then finally into change type. Sometime to access the properties within here,

to access the properties within here, you need to click this settings icon. So

you need to click this settings icon. So in the case of the promoted headers, I

in the case of the promoted headers, I would click this and then I could select

would click this and then I could select different options. We're not going to

different options. We're not going to change anything for these so far. All

change anything for these so far. All right. So that's the main portions of

right. So that's the main portions of this. One last thing to call out after

this. One last thing to call out after this query setup now that we've seen it.

this query setup now that we've seen it. Notice there's a formula bar up here.

Notice there's a formula bar up here. And if I go to this like change type,

And if I go to this like change type, the formula inside of here actually

the formula inside of here actually changes. Anyway, this itself inside of

changes. Anyway, this itself inside of here is what's called the M language.

here is what's called the M language. Power Query has a special language

Power Query has a special language that's put together to basically compile

that's put together to basically compile all the different steps to build this

all the different steps to build this query. If I wanted to go check it out, I

query. If I wanted to go check it out, I can go into this advanced editor up here

can go into this advanced editor up here and this is the full query for this

and this is the full query for this actual data set or this query. But once

actual data set or this query. But once again, we're getting ahead of oursel.

So, we're going to start with a going over the view tab first. And it's

over the view tab first. And it's important for that because sometimes

important for that because sometimes people think that, oh, I'm doing edits

people think that, oh, I'm doing edits in here. I can't actually evaluate and

in here. I can't actually evaluate and find out what's going until I go back to

find out what's going until I go back to the home tab and then close and apply to

the home tab and then close and apply to therefore load it into PowerBI. But

therefore load it into PowerBI. But actually with this view tab, we can do a

actually with this view tab, we can do a lot of analysis here and prevent us from

lot of analysis here and prevent us from having to jump back and forth between

having to jump back and forth between the Power Query editor and the PowerBI

the Power Query editor and the PowerBI app. What do I mean by that? Okay. Well,

app. What do I mean by that? Okay. Well, let's actually go check out something

let's actually go check out something like the job title short column. Now,

like the job title short column. Now, what's really neat about this is we have

what's really neat about this is we have up at the top here some icons that say,

up at the top here some icons that say, hey, we have 10 distinct value and zero

hey, we have 10 distinct value and zero unique. And if you actually count these

unique. And if you actually count these bars, that's the 10 distinct values. And

bars, that's the 10 distinct values. And we know from exploring the job title

we know from exploring the job title short column before that if I were to

short column before that if I were to actually use this drop- down arrow here,

actually use this drop- down arrow here, there are 10 values in here.

there are 10 values in here. Specifically, there's 10 different job

Specifically, there's 10 different job titles. Now, this drop- down arrow, now

titles. Now, this drop- down arrow, now that we have it open, it works similar

that we have it open, it works similar to how you could filter in Excel. I can

to how you could filter in Excel. I can sort ascending, sort descending. I'll do

sort ascending, sort descending. I'll do sort descending. Right now, it's going

sort descending. Right now, it's going to go through a load process. And if you

to go through a load process. And if you notice, we have an applied step of

notice, we have an applied step of sorted rows, but now it's uh sorted in

sorted rows, but now it's uh sorted in descending order. And it's now saying

descending order. And it's now saying there's only one distinct value. Why the

there's only one distinct value. Why the heck is that? Well, let's actually

heck is that? Well, let's actually inspect this. So, selecting the job tile

inspect this. So, selecting the job tile short column using the view tab. What I

short column using the view tab. What I can do here is come up here and select

can do here is come up here and select something like the column profile. This

something like the column profile. This thing is invaluable. Like I said,

thing is invaluable. Like I said, invaluable. I mean, invaluable. Anyway,

invaluable. I mean, invaluable. Anyway, what we find out is, yeah, everything

what we find out is, yeah, everything the software engineer makes up

the software engineer makes up everything. But it says, hey, the

everything. But it says, hey, the software engineer makes up 100% but a

software engineer makes up 100% but a thousand of them are software engineers.

thousand of them are software engineers. What's going on is I'm going to actually

What's going on is I'm going to actually close out of column profiling real

close out of column profiling real quick. Column profiling is based on the

quick. Column profiling is based on the top 1,00 rows at least selected from the

top 1,00 rows at least selected from the bottom. What I can do is change this to

bottom. What I can do is change this to the entire data set. Now, this is going

the entire data set. Now, this is going to actually have to load more data now

to actually have to load more data now and so it's going to take some time. So

and so it's going to take some time. So that's why by default it's set to a

that's why by default it's set to a th00and values because sometimes it

th00and values because sometimes it takes a while to actually load depending

takes a while to actually load depending on how big the data set is. So it loaded

on how big the data set is. So it loaded and now we can see that this preview

and now we can see that this preview area shows that there is 10 unique

area shows that there is 10 unique values. Whenever I go to column profile

values. Whenever I go to column profile and actually look inside the job tile

and actually look inside the job tile short column, I can see there's almost

short column, I can see there's almost 500,000 jobs here. And we actually see

500,000 jobs here. And we actually see all the different jobs, not just those

all the different jobs, not just those software engineers since we had it

software engineers since we had it sorted in descending order. And you can

sorted in descending order. And you can do this with any column. I can just go

do this with any column. I can just go through here and select different

through here and select different columns that I want to actually view. In

columns that I want to actually view. In this case, I'm looking at job location.

this case, I'm looking at job location. I can see that anywhere comprises nearly

I can see that anywhere comprises nearly 13% of jobs. Some more things to explore

13% of jobs. Some more things to explore up here from this view tab. I'm going to

up here from this view tab. I'm going to turn off that column profile. But we

turn off that column profile. But we also have this column distribution

also have this column distribution enabled. I can uncheck that or check

enabled. I can uncheck that or check that. I like to have that visually so I

that. I like to have that visually so I don't have to necessarily open the

don't have to necessarily open the column profile to view that. We have

column profile to view that. We have this of column quality which you can see

this of column quality which you can see that it tells you which data is valid,

that it tells you which data is valid, which one's the error, which one's

which one's the error, which one's empty. I'm not a fan of this because you

empty. I'm not a fan of this because you can actually, if you look up here at the

can actually, if you look up here at the top, there's a bar right here that shows

top, there's a bar right here that shows that. And we can see this a little bit

that. And we can see this a little bit more clearly using this these three

more clearly using this these three columns here where for something like

columns here where for something like the salary rate or salary year average,

the salary rate or salary year average, nearly 96% of them are empty, hence the

nearly 96% of them are empty, hence the gray bar. And then the other 4% are

gray bar. And then the other 4% are valid. Anyway, this bar is located up

valid. Anyway, this bar is located up here. Because of that, I don't really

here. Because of that, I don't really care about column quality. The other two

care about column quality. The other two of monospace just changes the font tape

of monospace just changes the font tape or displays or doesn't display whites

or displays or doesn't display whites space. I just leave show whites space

space. I just leave show whites space collect. Navigating back over to job

collect. Navigating back over to job tile short. Remember, we do have it

tile short. Remember, we do have it right now sorted. I can tell there's a

right now sorted. I can tell there's a sort on there. I actually didn't want to

sort on there. I actually didn't want to apply that step. If I want to remove a

apply that step. If I want to remove a step, I just navigate to whatever one,

step, I just navigate to whatever one, in this case, sorted rows, click this

in this case, sorted rows, click this red X right here, and it gets rid of it.

red X right here, and it gets rid of it. I also, it's taking a while for this

I also, it's taking a while for this data set to load in between each one.

data set to load in between each one. So, I'm going to change this column

So, I'm going to change this column profiling back to the top 1,000 rows.

profiling back to the top 1,000 rows. And with that, the last thing to capture

And with that, the last thing to capture on this column distribution, remember we

on this column distribution, remember we talked about here there was 10 distinct

talked about here there was 10 distinct values and zero unique, which distinct

values and zero unique, which distinct means they have repeating values. Unique

means they have repeating values. Unique means that there's only that value once.

means that there's only that value once. So in the case of job location of these

So in the case of job location of these top 10,00 values, there's 48 distinct

top 10,00 values, there's 48 distinct values, so repeating values, and 277

values, so repeating values, and 277 unique, but that's only for the top

unique, but that's only for the top thousand.

Jumping next into the home tab on the ribbon. This is where I spend the

ribbon. This is where I spend the majority of my time selecting different

majority of my time selecting different options that I want to do with cleaning

options that I want to do with cleaning up my data. We're going to be exploring

up my data. We're going to be exploring all of these features over the course of

all of these features over the course of the chapter, but I'm not going to dive

the chapter, but I'm not going to dive into each individual one because then

into each individual one because then this video would be too long and you're

this video would be too long and you're not going to pay attention. So, instead,

not going to pay attention. So, instead, we're just going to highlight key things

we're just going to highlight key things that I'm using very frequently with this

that I'm using very frequently with this specifically. If we go over to that job

specifically. If we go over to that job posted date column, we can see that it

posted date column, we can see that it is of the date time using this home

is of the date time using this home ribbon date time, which if you remember

ribbon date time, which if you remember from the last lesson, it didn't

from the last lesson, it didn't automatically select this because we

automatically select this because we didn't go through the power query editor

didn't go through the power query editor for the transformation. Anyway, not a

for the transformation. Anyway, not a big deal. The main point of showing is

big deal. The main point of showing is we can control the data type. And in

we can control the data type. And in here, it automatically did select job

here, it automatically did select job posted date as a date time. So, let's

posted date as a date time. So, let's actually demo a use case where we

actually demo a use case where we actually change a value. In this case, I

actually change a value. In this case, I can select the salary year average

can select the salary year average column. In our case, it is whole number.

column. In our case, it is whole number. And this value, as we can see here, it's

And this value, as we can see here, it's like 120,000. We're a salary hour

like 120,000. We're a salary hour average. It is of the data type decimal

average. It is of the data type decimal number. And we can see that it actually

number. And we can see that it actually has decimal numbers associated with it.

has decimal numbers associated with it. If I also want to get salary year

If I also want to get salary year average into this format, I would just

average into this format, I would just click decimal number. And it's asking me

click decimal number. And it's asking me this important step. Hey, do I want to

this important step. Hey, do I want to replace the current applied step? So

replace the current applied step? So this change type or do I want to add a

this change type or do I want to add a new step? Let's do add a new step. Just

new step? Let's do add a new step. Just a demo. It adds another step underneath

a demo. It adds another step underneath here. And all that's done here with this

here. And all that's done here with this change type one selected is a change

change type one selected is a change salary year average to type number. Now

salary year average to type number. Now I'm not a big fan of creating additional

I'm not a big fan of creating additional steps in here because there can be times

steps in here because there can be times where we get to we have 15 or 20 applied

where we get to we have 15 or 20 applied test sets. We want to minimize this as

test sets. We want to minimize this as much as possible. So I'm going to click

much as possible. So I'm going to click X out of this and we're going to do this

X out of this and we're going to do this again. with the salary or average column

again. with the salary or average column selected. Change it to decimal number.

selected. Change it to decimal number. And in this case, we're going to go

And in this case, we're going to go ahead with replace current and it's

ahead with replace current and it's updated cuz I can see it says decimal

updated cuz I can see it says decimal number up here. Along within this M

number up here. Along within this M language for the formula bar, it updated

language for the formula bar, it updated to the type number. I actually don't

to the type number. I actually don't want this as a decimal. We're going to

want this as a decimal. We're going to change this back to a whole number.

change this back to a whole number. We're going to replace current as well.

We're going to replace current as well. and salary year average changed back

and salary year average changed back into what it was originally for whole

into what it was originally for whole number which is this characteristic of

number which is this characteristic of in64.type type. Not something you need

in64.type type. Not something you need to have memorized. I just like to

to have memorized. I just like to sometimes look at the M language,

sometimes look at the M language, understand what's going on there. But

understand what's going on there. But clearly, we can see from this columns

clearly, we can see from this columns are put inside of parentheses and then

are put inside of parentheses and then referenced as necessary. So that's the

referenced as necessary. So that's the home tab. We're going to be going into a

home tab. We're going to be going into a lot of these other features,

lot of these other features, specifically manage columns, reduce

specifically manage columns, reduce rows, and everything else under

rows, and everything else under transform and some upcoming lessons.

Moving into the transform tab here. And with this, you're going to see a lot of

with this, you're going to see a lot of stuff repeated from the home tab.

stuff repeated from the home tab. Specifically right here, right? I see

Specifically right here, right? I see the salary year average. I can control

the salary year average. I can control the data type from here. Anyway, this

the data type from here. Anyway, this tab itself, it allows us to do more

tab itself, it allows us to do more control of how we want to modify a

control of how we want to modify a column. Let's take for example this job

column. Let's take for example this job via column, which shows us the platform

via column, which shows us the platform in which a job posted was posted on. If

in which a job posted was posted on. If I inspect it using column profile, I can

I inspect it using column profile, I can see that these different platforms have

see that these different platforms have the word via and then a space in front

the word via and then a space in front of it. If I'm trying to present this to

of it. If I'm trying to present this to my boss, I really don't want this via

my boss, I really don't want this via space in front of it. I just want it to

space in front of it. I just want it to say like in this case the second one,

say like in this case the second one, LinkedIn. Well, that's where the

LinkedIn. Well, that's where the transform tab comes to the rescue. With

transform tab comes to the rescue. With job via selected, I can go into replace

job via selected, I can go into replace values. Specifically, I want to replace

values. Specifically, I want to replace via with well with nothing. I'm gonna go

via with well with nothing. I'm gonna go ahead and click okay. So, this removed

ahead and click okay. So, this removed the via. But if I actually click

the via. But if I actually click something like this of boingsreveal.com,

something like this of boingsreveal.com, I'd have to actually check out this job

I'd have to actually check out this job posting website. Anyway, it shows down

posting website. Anyway, it shows down here at the bottom. But whenever you

here at the bottom. But whenever you highlight it, what you don't what you do

highlight it, what you don't what you do see is there's actually some white space

see is there's actually some white space in front of here. and I highlighted it

in front of here. and I highlighted it here. What we could do is we could do

here. What we could do is we could do one more step and in this case we want

one more step and in this case we want to have with the job via column selected

to have with the job via column selected go to format and I can do things like

go to format and I can do things like lower cases which is demonstrated here.

lower cases which is demonstrated here. I could even uppercase it make

I could even uppercase it make everything uppercase. I'm going to

everything uppercase. I'm going to delete both these steps. It's not what I

delete both these steps. It's not what I want to do. Instead what I want to do is

want to do. Instead what I want to do is I want to trim it. And now with this

I want to trim it. And now with this selected we can see that there's no

selected we can see that there's no white space in front of this. Now this

white space in front of this. Now this is actually unnecessary. I'm mainly just

is actually unnecessary. I'm mainly just doing this for demo purposes. I'm going

doing this for demo purposes. I'm going to remove this trim text portion. And

to remove this trim text portion. And underneath replace values, go into here

underneath replace values, go into here to modify it. And for the via, I'll put

to modify it. And for the via, I'll put via and then space. And now click okay.

via and then space. And now click okay. And in this case, whenever I select it,

And in this case, whenever I select it, I can see that that white space was

I can see that that white space was removed. We don't have to do an extra

removed. We don't have to do an extra step. Minimize steps.

Next up is the add column tab. And as the name implies, this adds a column.

the name implies, this adds a column. Transform, looking at transform and then

Transform, looking at transform and then looking at add column, there's a lot of

looking at add column, there's a lot of similarities in functions between the

similarities in functions between the two. And the key difference is is

two. And the key difference is is transform does it to the column itself

transform does it to the column itself and add column adds a new column. So

and add column adds a new column. So besides just formatting text like

besides just formatting text like extracting out or cleaning up the white

extracting out or cleaning up the white space around different columns and

space around different columns and creating a new column, I could use it

creating a new column, I could use it for a use case like this in job posted

for a use case like this in job posted date. This column name is actually

date. This column name is actually slightly misleading because it says job

slightly misleading because it says job posted date, but it's a date time. So if

posted date, but it's a date time. So if I wanted to make this column into a date

I wanted to make this column into a date and then this one into something called

and then this one into something called job posted date time, I could do that.

job posted date time, I could do that. So with this selected, I'm going to

So with this selected, I'm going to select up here to change this to a date

select up here to change this to a date and specifically date only. Now I want

and specifically date only. Now I want to change this name to job posted date.

to change this name to job posted date. So I could click on it and try to enter

So I could click on it and try to enter job posted date and then press enter.

job posted date and then press enter. But it conflicts with our other name. We

But it conflicts with our other name. We have to rename the other one first. So,

have to rename the other one first. So, our original job posted date. I'm

our original job posted date. I'm actually going to change this to job

actually going to change this to job posted date time. And then change this

posted date time. And then change this one to job posted date. Now, I don't

one to job posted date. Now, I don't like where this job posted date is, like

like where this job posted date is, like how far it is from job posted date time.

how far it is from job posted date time. So, I can drag it. I can also rightclick

So, I can drag it. I can also rightclick it and select to move it. It allows me

it and select to move it. It allows me to move it to the left, right to

to move it to the left, right to beginning, end. I can move it to the

beginning, end. I can move it to the beginning. And we got this new step of

beginning. And we got this new step of reorder columns. This actually isn't

reorder columns. This actually isn't where I want it. I'm going to drag it

where I want it. I'm going to drag it over. Get there. And now it's here. Now,

over. Get there. And now it's here. Now, unfortunately, with dates or date times

unfortunately, with dates or date times of Word, go to something like column

of Word, go to something like column profile and try to visualize it. I could

profile and try to visualize it. I could even change it to something like the

even change it to something like the entire data set. I'm not going to get

entire data set. I'm not going to get much value out of this new distribution

much value out of this new distribution once all those values loaded in other

once all those values loaded in other than well, this bad boy. So there are

than well, this bad boy. So there are some cases where now in this case I

some cases where now in this case I would go close and apply it and then

would go close and apply it and then load it directly into our file. Also we

load it directly into our file. Also we haven't done it already. We need to

haven't done it already. We need to actually go in and save this. With that

actually go in and save this. With that saved, let's actually get into

saved, let's actually get into visualizing it. We're going to be making

visualizing it. We're going to be making line charts with this. Specifically, I

line charts with this. Specifically, I want to compare that job posted date

want to compare that job posted date time basically without the hierarchy. So

time basically without the hierarchy. So I'm going to rightclick this and remove

I'm going to rightclick this and remove that date hierarchy. And then we're

that date hierarchy. And then we're going to do a count of the job title

going to do a count of the job title short. Remember previously in the last

short. Remember previously in the last lesson, this is what we're seeing with

lesson, this is what we're seeing with that job posted date time whenever it

that job posted date time whenever it wasn't formatted correctly. It's a hot

wasn't formatted correctly. It's a hot mess. Not what we want to view. Anyway,

mess. Not what we want to view. Anyway, I copied and pasted this over here to

I copied and pasted this over here to the right hand side. And even if I were

the right hand side. And even if I were to put job posted date in here now, and

to put job posted date in here now, and yeah, it's working. But even when I

yeah, it's working. But even when I convert this to away from that hierarchy

convert this to away from that hierarchy itself, the values are still usable.

itself, the values are still usable. Unlike this one, not necessarily usable

Unlike this one, not necessarily usable with this datetime format. All right,

with this datetime format. All right, good enough to inspect. Let's jump back

good enough to inspect. Let's jump back into Power Query Editor. We go back into

into Power Query Editor. We go back into transform data and then transform data.

transform data and then transform data. You can also access it by clicking these

You can also access it by clicking these three dots right here for any data

three dots right here for any data source and going into edit query. All

source and going into edit query. All right, so here we are back here. There's

right, so here we are back here. There's one final cleanup I want to do, one

one final cleanup I want to do, one final exercise, if you will, and that

final exercise, if you will, and that deals with that salary hour average

deals with that salary hour average column. Oops, looks like I have column

column. Oops, looks like I have column profile turned on. I'm going to turn

profile turned on. I'm going to turn that off. Anyway, if you remember, I

that off. Anyway, if you remember, I wanted to compare or we're going to

wanted to compare or we're going to compare the salary year average column

compare the salary year average column to an adjusted val value of the salary

to an adjusted val value of the salary hour average column. Specifically,

hour average column. Specifically, salary hour average is in an hourly

salary hour average is in an hourly format. We want to get into what would

format. We want to get into what would it be for a yearly salary. So what we

it be for a yearly salary. So what we need to do is take these values inside

need to do is take these values inside of salary hour average such as this one

of salary hour average such as this one here of 61.15 and we want to multiply it

here of 61.15 and we want to multiply it times the number of hours in a week and

times the number of hours in a week and the number of weeks in a year. So we

the number of weeks in a year. So we come up here into the ad column

come up here into the ad column underneath standard. We want to multiply

underneath standard. We want to multiply this column that we have selected and

this column that we have selected and there's 40 hours in a week and 52 weeks

there's 40 hours in a week and 52 weeks in a year. This actually comes out to

in a year. This actually comes out to 2080. You have to put in actual whole

2080. You have to put in actual whole number in here for this. And we're go

number in here for this. And we're go ahead and click okay. Now, I want to

ahead and click okay. Now, I want to double check my values real quick. So,

double check my values real quick. So, I'm just going to filter real quick to

I'm just going to filter real quick to remove null. Remember, this will

remove null. Remember, this will actually apply a step here of filter

actually apply a step here of filter row. So, we'll need to remove this. And

row. So, we'll need to remove this. And then, if I drag it on over next to

then, if I drag it on over next to salary average, so we can view it. We

salary average, so we can view it. We can see that. Okay. Yeah, it did

can see that. Okay. Yeah, it did actually apply the necessary

actually apply the necessary multiplication to get the values we

multiplication to get the values we need. All right. So, let's remove these

need. All right. So, let's remove these last two steps to get back where we

last two steps to get back where we were. We just reordered it and then also

were. We just reordered it and then also filtered. And we're doing this because

filtered. And we're doing this because navigating this multiplication column. I

navigating this multiplication column. I need to rename it. And we could do this

need to rename it. And we could do this by double clicking this, typing in the

by double clicking this, typing in the name of salary, hour adjusted. I need to

name of salary, hour adjusted. I need to get that right. Click enter. And then

get that right. Click enter. And then this adds another step. Remember, I'm

this adds another step. Remember, I'm not really a big fan of adding extra

not really a big fan of adding extra steps. I'm going to go ahead and stop

steps. I'm going to go ahead and stop this or close out of that step. If I

this or close out of that step. If I select the inserted multiplication, open

select the inserted multiplication, open up this formula bar right here. Now, we

up this formula bar right here. Now, we don't need to be experts at reading this

don't need to be experts at reading this M language here, this formula bar. But

M language here, this formula bar. But what you can see, as we've seen

what you can see, as we've seen previously with something like the

previously with something like the change type, I'm going to select that

change type, I'm going to select that one. These column headers are in

one. These column headers are in parentheses.

parentheses. Similarly, if I go to insert and

Similarly, if I go to insert and multiplication and I look at this column

multiplication and I look at this column header is in parenthesis, these two

header is in parenthesis, these two match. So for this step of insert

match. So for this step of insert multiplication, it's giving it this

multiplication, it's giving it this name. Instead, I could change it up here

name. Instead, I could change it up here to salary hour adjusted. Press enter and

to salary hour adjusted. Press enter and then it does it within the same step and

then it does it within the same step and there's no extra step or applied step.

there's no extra step or applied step. Now I could take salary hour adjusted

Now I could take salary hour adjusted and drag it over here. next to salary

and drag it over here. next to salary hour average. And I'm gonna get nitpicky

hour average. And I'm gonna get nitpicky because I I really like minimizing my

because I I really like minimizing my steps. If you've noticed right now, we

steps. If you've noticed right now, we have two separate reorder columns. We

have two separate reorder columns. We know that the the we have multiple of it

know that the the we have multiple of it because it now says reordered columns

because it now says reordered columns one. My recommendation instead, this is

one. My recommendation instead, this is an advanced technique. Don't worry if

an advanced technique. Don't worry if you're getting this and you made it this

you're getting this and you made it this far with doing this, you're perfectly

far with doing this, you're perfectly fine. I like being make sure we minimize

fine. I like being make sure we minimize our steps, right? So, I'm going to X out

our steps, right? So, I'm going to X out of this. I'm going to and this puts that

of this. I'm going to and this puts that salary hour adjusted back on the end.

salary hour adjusted back on the end. I'm going to drag reordered columns to

I'm going to drag reordered columns to the bottom. Now, whenever I take salary

the bottom. Now, whenever I take salary hour adjusted over here, it's going to

hour adjusted over here, it's going to insert it into that current step of

insert it into that current step of reorg. So, there's no duplicate of that.

reorg. So, there's no duplicate of that. So, you can feel free to move these

So, you can feel free to move these applied steps around to wherever you

applied steps around to wherever you need them to be. Um, but you need to be

need them to be. Um, but you need to be careful with how you do it. In this

careful with how you do it. In this case, right, I get an error message. the

case, right, I get an error message. the column salary hour average wasn't found

column salary hour average wasn't found because it's created in this step. So

because it's created in this step. So you need to make sure whenever you're

you need to make sure whenever you're moving things you are keeping them in an

moving things you are keeping them in an order that keeps track of the current

order that keeps track of the current columns. Anyway, let's go inspect this.

columns. Anyway, let's go inspect this. I'm going to close and apply and I'm

I'm going to close and apply and I'm going to create a new page. And in this

going to create a new page. And in this we're going to be making a clustered bar

we're going to be making a clustered bar chart. We want to compare this for the

chart. We want to compare this for the different job title shorts. So I'll drag

different job title shorts. So I'll drag that to the y ais. And now we can take

that to the y ais. And now we can take something like the salary year average

something like the salary year average to the x-axis. I want to aggregate this

to the x-axis. I want to aggregate this by a median and salary hour adjusted

by a median and salary hour adjusted also to the x-axis. This one will also

also to the x-axis. This one will also be median. And bam, what we can see from

be median. And bam, what we can see from this is well, let's go into focus mode

this is well, let's go into focus mode that consistently hourly salaries are

that consistently hourly salaries are consistently below yearly salaries. And

consistently below yearly salaries. And this is a great data point or a good

this is a great data point or a good insight because we need to understand or

insight because we need to understand or you want need to understand that yeah

you want need to understand that yeah you may be taking a job as hourly but

you may be taking a job as hourly but most likely you're going to get

most likely you're going to get underpaid somebody that's paying on a

underpaid somebody that's paying on a yearly basis which is kind of jacked up

yearly basis which is kind of jacked up and kind of pushes or trying to make

and kind of pushes or trying to make people get full-time jobs with salaries

people get full-time jobs with salaries vice somebody just working hourly. And

vice somebody just working hourly. And in the final file I put together this

in the final file I put together this scatter plot which I'm trying to show by

scatter plot which I'm trying to show by this. I made them the axises equal where

this. I made them the axises equal where this one the left side is the adjusted

this one the left side is the adjusted or sorry the y- axis is the adjusted

or sorry the y- axis is the adjusted salary and the x-axis is the yearly

salary and the x-axis is the yearly salary and these the axises goes from

salary and these the axises goes from 80,000 to 160,000 for both of these. So

80,000 to 160,000 for both of these. So the the actual area itself the plot area

the the actual area itself the plot area is similar across each. Anyway, what

is similar across each. Anyway, what we'd hope to see with this line of best

we'd hope to see with this line of best fit between all these different job

fit between all these different job titles right here is that it goes

titles right here is that it goes exactly splits in between the two.

exactly splits in between the two. Unfortunately, it's inclined or morely

Unfortunately, it's inclined or morely more towards the yearly median salary,

more towards the yearly median salary, which is also what we showed in this bar

which is also what we showed in this bar chart in that yearly salaries are higher

chart in that yearly salaries are higher than those adjusted hour salaries. All

than those adjusted hour salaries. All right, so you have some practice

right, so you have some practice problems now to go through and get more

problems now to go through and get more familiar with using the Power Query

familiar with using the Power Query Editor. In the next lesson, we're going

Editor. In the next lesson, we're going to be using the Power Query editor

to be using the Power Query editor specifically to bring in and clean up

specifically to bring in and clean up the data set that we're going to be

the data set that we're going to be using for the final project. All right,

using for the final project. All right, with that, I'll see you in the next one.

In this lesson, we're going to be importing in our final data set that

importing in our final data set that we're going to be using for our second

we're going to be using for our second project. It's quick to note there's no

project. It's quick to note there's no difference in data between the two.

difference in data between the two. We'll get to all that in a little bit,

We'll get to all that in a little bit, but it's more important if you

but it's more important if you understand there's not going to be

understand there's not going to be really a change in data, but in how we

really a change in data, but in how we can actually analyze it. Now, what do I

can actually analyze it. Now, what do I mean by this?

So, I'm inside the project file from the last lesson where we imported in our job

last lesson where we imported in our job postings flat CSV. And if I scroll on

postings flat CSV. And if I scroll on over here specifically to these two

over here specifically to these two columns on job skills and job type

columns on job skills and job type skills, we're going to focus on job

skills, we're going to focus on job skills for a time being. It is of the

skills for a time being. It is of the format text and it's a list of skills in

format text and it's a list of skills in here. But how the heck am I supposed to

here. But how the heck am I supposed to use this these skills when they're

use this these skills when they're associated with a certain job? Like you

associated with a certain job? Like you could have multiple skills to a job. If

could have multiple skills to a job. If I were try to create a bar chart of

I were try to create a bar chart of these skills and as as you expect if I

these skills and as as you expect if I try to drop something like the job

try to drop something like the job skills into here doing it by count going

skills into here doing it by count going into focus mode so we can see it better.

into focus mode so we can see it better. Basically it's just providing counts of

Basically it's just providing counts of these different lists but this doesn't

these different lists but this doesn't really provide us cuz the it hasn't

really provide us cuz the it hasn't broken up these skills. And remember

broken up these skills. And remember from this looking at the model view this

from this looking at the model view this is only one table that has everything in

is only one table that has everything in it. It's called a flat table. Well, here

it. It's called a flat table. Well, here I am in the final file for this lesson.

I am in the final file for this lesson. And in it, I'm an able to analyze what

And in it, I'm an able to analyze what are the top skills and data. Basically,

are the top skills and data. Basically, we're looking at those skills and

we're looking at those skills and they're actually broken out individually

they're actually broken out individually for this, but have the appropriate job

for this, but have the appropriate job count for how many jobs there are. How

count for how many jobs there are. How is this even possible? Well, if we go

is this even possible? Well, if we go into the model view for this final video

into the model view for this final video file of the lesson, we can see that we

file of the lesson, we can see that we have multiple tables inside of here and

have multiple tables inside of here and we have these lines between them

we have these lines between them allowing us to connect relationships to

allowing us to connect relationships to it. Now, we're going to get all into all

it. Now, we're going to get all into all this and break it down further, but the

this and break it down further, but the gist of it is is we have one table with

gist of it is is we have one table with all of our job postings and then one

all of our job postings and then one table with our skills and we're

table with our skills and we're connecting it to the two and we're able

connecting it to the two and we're able to query across these tables. So, let's

to query across these tables. So, let's break this concept down by looking at

break this concept down by looking at this erd or entity relationship diagram

this erd or entity relationship diagram of the final table of what we just saw

of the final table of what we just saw in PowerBI but broken out here. So for

in PowerBI but broken out here. So for this this is arranged into what's called

this this is arranged into what's called a star star schema and we have fact

a star star schema and we have fact tables and then dimensional tables

tables and then dimensional tables specifically we have our job postings

specifically we have our job postings fact table that's why facts at the end

fact table that's why facts at the end and then all these other ones are

and then all these other ones are dimensional table tables that's why we

dimensional table tables that's why we have the dim at the end this is a

have the dim at the end this is a shorthand notation and is pretty common

shorthand notation and is pretty common whenever you're using this type of

whenever you're using this type of structure on how you'd expect things to

structure on how you'd expect things to be named anyway the job posting fact

be named anyway the job posting fact table contains all the measurable data.

table contains all the measurable data. So, every single job posting, if you

So, every single job posting, if you worked in something like sales and had a

worked in something like sales and had a similar thing, all of the different

similar thing, all of the different orders would probably be in the fact

orders would probably be in the fact table and then things like information

table and then things like information on the customers or information on the

on the customers or information on the stores would be in the dimensional

stores would be in the dimensional tables. Similarly, our dimension tables

tables. Similarly, our dimension tables have things like the company and then

have things like the company and then also the skills. These fact tables are

also the skills. These fact tables are going to take much more rows because

going to take much more rows because they contain every single attribute.

they contain every single attribute. Whereas something like a company dim is

Whereas something like a company dim is only going to contain a company once or

only going to contain a company once or a skills dim is only going to contain a

a skills dim is only going to contain a skill once but then link it to the job.

skill once but then link it to the job. Now because we're using this fact and

Now because we're using this fact and dimensional tables, it's commonly

dimensional tables, it's commonly referred to as a star schema. Going back

referred to as a star schema. Going back to PowerBI, it's hard to really see star

to PowerBI, it's hard to really see star schema with this one because we only

schema with this one because we only have basically two relationships coming

have basically two relationships coming off of the skills in the company. But if

off of the skills in the company. But if we go to the very last lesson file,

we go to the very last lesson file, which we'll get to at the end of chapter

which we'll get to at the end of chapter 4, this starts to look more like a star

4, this starts to look more like a star schema because we have our job posting

schema because we have our job posting fact table and then we're going to

fact table and then we're going to create even more dimensional tables

create even more dimensional tables creating this star schema. But that's

creating this star schema. But that's for chapter four. Getting ahead of

for chapter four. Getting ahead of oursel. Now, this isn't to say flat

oursel. Now, this isn't to say flat tables are completely useless. Flat

tables are completely useless. Flat tables have their time and place,

tables have their time and place, especially for those that are maybe new

especially for those that are maybe new to analyzing data or aren't familiar

to analyzing data or aren't familiar with data set. It makes it have a simple

with data set. It makes it have a simple structure and super easy to actually

structure and super easy to actually query. But as we demonstrated with that

query. But as we demonstrated with that star schema in the final lesson for

star schema in the final lesson for this, right, we're going to be able to

this, right, we're going to be able to actually get in and do some deeper

actually get in and do some deeper analysis because now we can actually

analysis because now we can actually analyze multiple skills for a job

analyze multiple skills for a job posting and flat tables can't handle

posting and flat tables can't handle this. And so they're only they're

this. And so they're only they're somewhat limited if you will.

somewhat limited if you will. So enough yapping. Let's actually put

So enough yapping. Let's actually put this into practice and import in this

this into practice and import in this data set. And during this, we're going

data set. And during this, we're going to be diving into an important concept

to be diving into an important concept of reference first query. Anyway,

of reference first query. Anyway, starting off with a blank report for

starting off with a blank report for this. So, we have this file. Let's get

this. So, we have this file. Let's get into getting it. Specifically, we want

into getting it. Specifically, we want to get our data. As we learned

to get our data. As we learned previously, we can get it from a folder.

previously, we can get it from a folder. So, after selecting more, I navigate

So, after selecting more, I navigate into folder and select connect. And for

into folder and select connect. And for this, I'm going to select the location

this, I'm going to select the location of our star schema files. And this is

of our star schema files. And this is inside of our PowerBI data analytics

inside of our PowerBI data analytics course project folder underneath data.

course project folder underneath data. And in this folder called star schema

And in this folder called star schema files, we have four different CSV files

files, we have four different CSV files that we're going to be importing for

that we're going to be importing for this. Anyway, I've navigated to it. I'm

this. Anyway, I've navigated to it. I'm clicking okay. And similar before, this

clicking okay. And similar before, this is showing the four different files

is showing the four different files which is going to be made into the four

which is going to be made into the four different tables. We do not want to

different tables. We do not want to combine and transform data or combine

combine and transform data or combine and load. We don't want to do this

and load. We don't want to do this option. We need to go into the power

option. We need to go into the power query editor. So we're going to go to

query editor. So we're going to go to transform data. What we need to do now

transform data. What we need to do now inside of this power query editor is we

inside of this power query editor is we need to drill down or create queries for

need to drill down or create queries for each one of these. I can actually

each one of these. I can actually demonstrate this inside the star schema

demonstrate this inside the star schema files query. If I want to navigate to

files query. If I want to navigate to job postings fact, I can click binary

job postings fact, I can click binary and it basically does the entire cleanup

and it basically does the entire cleanup necessary to get this job postings fact

necessary to get this job postings fact table all ready to go. But the problem

table all ready to go. But the problem is now you know I'm lazy. I need to now

is now you know I'm lazy. I need to now do this for these three other sources. I

do this for these three other sources. I don't want to go through and actually

don't want to go through and actually select new source, do that again of we

select new source, do that again of we selecting the file and then uploading

selecting the file and then uploading again. I'm going to do something

again. I'm going to do something actually even simpler than that. So I'm

actually even simpler than that. So I'm going to remove these different steps

going to remove these different steps that we did right here up to the point

that we did right here up to the point of source. And this is getting into now

of source. And this is getting into now how we can create new queries. We can

how we can create new queries. We can either duplicate it and in this case

either duplicate it and in this case this duplicated one which has the

this duplicated one which has the parenthesis 2 just has the same files or

parenthesis 2 just has the same files or same code that if we look at the

same code that if we look at the previous query as that one that's

previous query as that one that's actually not showing much. So let's

actually not showing much. So let's actually instead I'm going to delete

actually instead I'm going to delete this one and with this star schema file

this one and with this star schema file I'm going to make some changes to it.

I'm going to make some changes to it. Specifically let's just say go into the

Specifically let's just say go into the job postings fact table. And now

job postings fact table. And now whenever I rightclick this one and

whenever I rightclick this one and select duplicate, notice all of these

select duplicate, notice all of these applied steps were done with this

applied steps were done with this whenever I duplicated this. However, if

whenever I duplicated this. However, if I were to rightclick this one and

I were to rightclick this one and instead click reference to create a new

instead click reference to create a new query number three, the reference one,

query number three, the reference one, this one only has one step. And the step

this one only has one step. And the step looking at it up here is hey set it

looking at it up here is hey set it equal to star schema files which is this

equal to star schema files which is this first query. So the point I'm trying to

first query. So the point I'm trying to make is with the duplicate you don't

make is with the duplicate you don't sometimes want to do this especially if

sometimes want to do this especially if it's going to just repeat all the steps

it's going to just repeat all the steps when they've been done already. It's

when they've been done already. It's going to cause a necessary load time

going to cause a necessary load time later on. So let's go ahead and delete

later on. So let's go ahead and delete both these and then from there remove

both these and then from there remove those steps to get into showing more

those steps to get into showing more steps necessary. And now for this, what

steps necessary. And now for this, what we're actually going to do, I'm going to

we're actually going to do, I'm going to rightclick this and I'm going to

rightclick this and I'm going to reference it. And this first one, I want

reference it. And this first one, I want to reference it to be the job postings

to reference it to be the job postings fact table, which I've changed the name

fact table, which I've changed the name up in the properties right here. This

up in the properties right here. This one, we're going to dive into here. And

one, we're going to dive into here. And now this has our necessary table. So

now this has our necessary table. So we'll need to do this again for all

we'll need to do this again for all these other tables. I'll start with

these other tables. I'll start with skills dim once again. We'll reference

skills dim once again. We'll reference it. We'll click into skills dim. And I

it. We'll click into skills dim. And I didn't rename it first, but that's fine.

didn't rename it first, but that's fine. And I can still go in and say skills

And I can still go in and say skills dim. All right, just need to do the

dim. All right, just need to do the remaining two now. So now we have all

remaining two now. So now we have all four of the tables in. We have our fact

four of the tables in. We have our fact table, our skills dimensional table,

table, our skills dimensional table, skills job dim, and our company dim.

skills job dim, and our company dim. We're going to go ahead now and click

We're going to go ahead now and click close and apply to get this into our

close and apply to get this into our PowerBI file. In the data pane, I can

PowerBI file. In the data pane, I can see all our four tables along with our

see all our four tables along with our star schema files. I can even go to the

star schema files. I can even go to the model view, which is the most important

model view, which is the most important for this. and rearranging this all so we

for this. and rearranging this all so we can see it a little bit better. We can

can see it a little bit better. We can see that it established the

see that it established the relationships necessary between here.

relationships necessary between here. Now, we're going to jump into

Now, we're going to jump into relationships here in a second. I first

relationships here in a second. I first want to focus on this this star schema

want to focus on this this star schema file. This isn't really necessary inside

file. This isn't really necessary inside of this area here. And specifically, I'm

of this area here. And specifically, I'm not going to need it inside of the

not going to need it inside of the canvas. I don't really want that. And if

canvas. I don't really want that. And if I were to share this with somebody, I

I were to share this with somebody, I wouldn't want somebody to have or to see

wouldn't want somebody to have or to see it. Now, I could do something like this

it. Now, I could do something like this and I could hide it, but this isn't

and I could hide it, but this isn't hiding it in the model view. It's only

hiding it in the model view. It's only hiding it here inside of the data view.

hiding it here inside of the data view. So, actually, I'm going to recommend not

So, actually, I'm going to recommend not even loading this table into PowerBI.

even loading this table into PowerBI. So, all we need to do is go back into

So, all we need to do is go back into Power Query editor by going to transform

Power Query editor by going to transform data and with this star schema file, I'm

data and with this star schema file, I'm going to rightclick it and look at this.

going to rightclick it and look at this. We have this that's checked right now.

We have this that's checked right now. Enable load. We're gonna click it and

Enable load. We're gonna click it and it's gonna say, "Hey, there's possible

it's gonna say, "Hey, there's possible possible data loss warning. Promise you

possible data loss warning. Promise you we're not going to lose any data." And

we're not going to lose any data." And the name went into italics. And now

the name went into italics. And now right clicking, we'd see enable load is

right clicking, we'd see enable load is no longer clicked next to it. So

no longer clicked next to it. So whenever I click close and apply and

whenever I click close and apply and navigate to the model view, it's not in

navigate to the model view, it's not in here. And it keeps things super simple

here. And it keeps things super simple for anybody that you may pass this file

for anybody that you may pass this file on to.

on to. All right, so let's now get into

All right, so let's now get into relationships. And I'm actually going to

relationships. And I'm actually going to assume PowerBI was smart enough in this

assume PowerBI was smart enough in this case to pick up these different these

case to pick up these different these three different relationships between

three different relationships between our tables. But I'm going to assume that

our tables. But I'm going to assume that maybe it didn't work for you. So what

maybe it didn't work for you. So what we're going to do is go ahead and delete

we're going to do is go ahead and delete all of these different relationships in

all of these different relationships in here by right-clicking on them,

here by right-clicking on them, selecting delete. Now let's recreate all

selecting delete. Now let's recreate all of these for the job posting fact table.

of these for the job posting fact table. I want to connect these. And we can see

I want to connect these. And we can see we have company ID. We want to connect

we have company ID. We want to connect it to company ID. So I just drag it to

it to company ID. So I just drag it to each other. And then this new

each other. And then this new relationship pop-up window comes up

relationship pop-up window comes up here. In it, it has the table selected,

here. In it, it has the table selected, which columns are they selected on, and

which columns are they selected on, and it automatically selects the cardality.

it automatically selects the cardality. And this describes how rows from one

And this describes how rows from one table correlate to another. In this

table correlate to another. In this case, it's saying many to one. In the

case, it's saying many to one. In the job postings fact table, we can see here

job postings fact table, we can see here just from this snippet here at the top,

just from this snippet here at the top, the company ID, there's many.

the company ID, there's many. Specifically, we have 1145 multiple

Specifically, we have 1145 multiple times. There are many values for the

times. There are many values for the company dim. There's one value. Even

company dim. There's one value. Even just looking at this snippet, there's

just looking at this snippet, there's only one unique value in here. It

only one unique value in here. It automatically figures this out. This is

automatically figures this out. This is not something you need to do. There's

not something you need to do. There's also cross filter direction, which we're

also cross filter direction, which we're going to get to towards the end of this

going to get to towards the end of this lesson. And always we want to make this

lesson. And always we want to make this relationship active. I'll go ahead and

relationship active. I'll go ahead and click save. Now the other way we could

click save. Now the other way we could create a relationship is okay let's say

create a relationship is okay let's say in this case I want to now connect our

in this case I want to now connect our skills tables now together specifically

skills tables now together specifically skills job dim has a job ID column so I

skills job dim has a job ID column so I know I can connect to this job ID I'm

know I can connect to this job ID I'm not going to drag it instead I'm going

not going to drag it instead I'm going to rightclick it and I'm going to go

to rightclick it and I'm going to go into manage relationship in this I'm

into manage relationship in this I'm going to select hey let's add a new

going to select hey let's add a new relationship and that similar window

relationship and that similar window that we saw before is going to show

that we saw before is going to show there I'm going to put job postings fact

there I'm going to put job postings fact up at the top and then skills job dim

up at the top and then skills job dim underneath it and it automatically

underneath it and it automatically picked up that job ID that these are the

picked up that job ID that these are the columns it's going to be related on. Now

columns it's going to be related on. Now somewhat similar to the last one instead

somewhat similar to the last one instead of being a many to one this one is going

of being a many to one this one is going to be a one to many. Specifically

to be a one to many. Specifically there's only going to be one unique job

there's only going to be one unique job ID in here but in our skills job dim

ID in here but in our skills job dim there's going to be many job IDs.

there's going to be many job IDs. Unfortunately we only have a data

Unfortunately we only have a data preview of three values. So, I can't

preview of three values. So, I can't show that. In fact, job ID five. There's

show that. In fact, job ID five. There's there's multiple values of five in

there's multiple values of five in there. Anyway, I'm going to click go

there. Anyway, I'm going to click go ahead and save because we're going to

ahead and save because we're going to keep everything else by default. And I'm

keep everything else by default. And I'm going to close out of this. And now we

going to close out of this. And now we can see that this relationship is going.

can see that this relationship is going. And when I highlight it over, we can see

And when I highlight it over, we can see that the job ID is there. All right.

that the job ID is there. All right. Let's just connect the last table. And

Let's just connect the last table. And that's going to be connecting the SC uh

that's going to be connecting the SC uh skills job dim down into the skills dim.

skills job dim down into the skills dim. Okay. In this case, we're once again

Okay. In this case, we're once again going to connect on a skill ID. This is

going to connect on a skill ID. This is a many to one relationship. Inside the

a many to one relationship. Inside the skills job dim table, there's many

skills job dim table, there's many values. In this case, multiple ones. But

values. In this case, multiple ones. But there's only one unique value inside the

there's only one unique value inside the skill dim. Basically, this table lists

skill dim. Basically, this table lists all the different skills in there, but

all the different skills in there, but only lists them once. I'm going to go

only lists them once. I'm going to go ahead and click okay. And now we have up

ahead and click okay. And now we have up all our relationships established. Now,

all our relationships established. Now, we can also see these relationships here

we can also see these relationships here with the nomenclature they're using.

with the nomenclature they're using. They have a one on this side and then an

They have a one on this side and then an asterisk here. So in this case from

asterisk here. So in this case from company dim to job posting facts it's a

company dim to job posting facts it's a one to many relationship. You're

one to many relationship. You're typically going to always see either a

typically going to always see either a one to many many to one or a one to one

one to many many to one or a one to one relationship. If you're getting to

relationship. If you're getting to situations of a many to many that's

situations of a many to many that's going to cause a lot of confusion and

going to cause a lot of confusion and it's going to eat up a lot of resources

it's going to eat up a lot of resources trying to match up data. Anyway that's

trying to match up data. Anyway that's beyond scope of this. That's more of an

beyond scope of this. That's more of an advanced technique to deal with. In most

advanced technique to deal with. In most cases, you're going to be seeing this

cases, you're going to be seeing this one to many or many to one. Anyway,

one to many or many to one. Anyway, let's get into verifying that this is

let's get into verifying that this is set up correctly by first visualizing

set up correctly by first visualizing the company table. So, we're going to

the company table. So, we're going to drag a stack bar chart into here. Going

drag a stack bar chart into here. Going to minimize the filter so we can see

to minimize the filter so we can see this a little bit better. Anyway, with

this a little bit better. Anyway, with our company DIM table that has the

our company DIM table that has the company information in it, such as

company information in it, such as company ID, links to the companies, the

company ID, links to the companies, the name of the company, I'm going to go

name of the company, I'm going to go ahead and drag that into the Yaxis. And

ahead and drag that into the Yaxis. And then inside of job postings fact, recall

then inside of job postings fact, recall that no, we no longer have the company

that no, we no longer have the company name inside of here. That's why we

name inside of here. That's why we dragged it from above. But also with

dragged it from above. But also with this, making this a little bit bigger.

this, making this a little bit bigger. We do have inside of here a job ID. So

We do have inside of here a job ID. So we can now start doing the job ID, the

we can now start doing the job ID, the count of the job ID vice doing that

count of the job ID vice doing that count of that job title short, which I

count of that job title short, which I feel a count of job ID is more common in

feel a count of job ID is more common in practice. Anyway, we can see that we're

practice. Anyway, we can see that we're using these multiple different tables

using these multiple different tables because we have these check marks here

because we have these check marks here saying we're using these tables. So,

saying we're using these tables. So, we're filtering across tables. And with

we're filtering across tables. And with this going into focus mode, we can now

this going into focus mode, we can now see the count of those different jobs, I

see the count of those different jobs, I mean companies. We could even take this

mean companies. We could even take this a step further by copying this and then

a step further by copying this and then pasting it. And instead of doing counts

pasting it. And instead of doing counts of jobs, we could drag that salary year

of jobs, we could drag that salary year average into here, adjust it to a median

average into here, adjust it to a median aggregation, and then see things like,

aggregation, and then see things like, hey, Goldman Tech Resourcing gives up to

hey, Goldman Tech Resourcing gives up to $870,000

$870,000 for a median salary. Not too bad. So,

for a median salary. Not too bad. So, that's the company information. Let's

that's the company information. Let's create a new page and analyze the skills

create a new page and analyze the skills or if we can analyze the skills. Now,

or if we can analyze the skills. Now, just to remember, right, this one's a

just to remember, right, this one's a little bit more complicated. We have a

little bit more complicated. We have a skills job dim and then a skills dim

skills job dim and then a skills dim table. Why do we have two? Well, going

table. Why do we have two? Well, going to the skills job dim table, we have our

to the skills job dim table, we have our job IDs and then our skill ID. I'm going

job IDs and then our skill ID. I'm going to sort the job ID in ascending order to

to sort the job ID in ascending order to better showcase this. But basically,

better showcase this. But basically, remember, we could have multiple

remember, we could have multiple different skills for a job. So, in this

different skills for a job. So, in this case, job ID of one has skill of 205 and

case, job ID of one has skill of 205 and 178. and then our skills dim table. We

178. and then our skills dim table. We could then inspect from here. Navigating

could then inspect from here. Navigating to 205, we could see, hey, the skill ID

to 205, we could see, hey, the skill ID is a skill ID of flow and that 178 is

is a skill ID of flow and that 178 is for Tableau, which is the type analyst

for Tableau, which is the type analyst tools. Anyway, you're probably like,

tools. Anyway, you're probably like, Luke, why the heck do you have multiple

Luke, why the heck do you have multiple tables in this case? Well, the reason is

tables in this case? Well, the reason is to solve an issue where there can be

to solve an issue where there can be multiple skills for a job. But for our

multiple skills for a job. But for our companies, there can't be multiple

companies, there can't be multiple companies listing the same job posting.

companies listing the same job posting. There's only one. So, in this case, we

There's only one. So, in this case, we were able to make it into only one

were able to make it into only one table. And we have one value or one

table. And we have one value or one company to many different job postings

company to many different job postings is put out. But we wouldn't be able to

is put out. But we wouldn't be able to do that with just a skills dim directly

do that with just a skills dim directly connected to this. Remember, we'd have a

connected to this. Remember, we'd have a many to many relationship. This would be

many to many relationship. This would be a mess. It'd be a heck to deal with.

a mess. It'd be a heck to deal with. That's why we have to have this

That's why we have to have this intermediate table skills job dim.

intermediate table skills job dim. Anyway, enough me yapping. Let's

Anyway, enough me yapping. Let's actually get into trying to visualize

actually get into trying to visualize this. Once again, we're going to insert

this. Once again, we're going to insert in a stack bar chart. We're going to use

in a stack bar chart. We're going to use the skills dim to insert in our skills

the skills dim to insert in our skills into the yaxis and then go to the job

into the yaxis and then go to the job postings fact table to put remember that

postings fact table to put remember that count of job ID. Now, if you notice by

count of job ID. Now, if you notice by this, we have relationship issues with

this, we have relationship issues with this. Mainly, all of these values, the

this. Mainly, all of these values, the count of the job ID is 470,000.

count of the job ID is 470,000. [Music]

[Music] Basically, the count of all the

Basically, the count of all the different job IDs. What's going on here?

different job IDs. What's going on here? Well, it deals with cross filter.

So, what's going on here? Well, let's navigate back to that model view. And

navigate back to that model view. And I'm going to move these around slightly.

I'm going to move these around slightly. specifically. I don't care about the

specifically. I don't care about the company dim table. Dragged it off the

company dim table. Dragged it off the screen for right now. We care about

screen for right now. We care about these skills tables. All right. So,

these skills tables. All right. So, what's going on here? And that has to

what's going on here? And that has to deal with this cross filter direction.

deal with this cross filter direction. Right now, if I were to double click on

Right now, if I were to double click on this, I can see cross filter direction

this, I can see cross filter direction is on single. And there's only one arrow

is on single. And there's only one arrow on there. And it shows it's going from

on there. And it shows it's going from the job postings fact table so to the SC

the job postings fact table so to the SC skills job dim. And then over here on

skills job dim. And then over here on the skills dim table, we can see that

the skills dim table, we can see that the direction is from skills dim to

the direction is from skills dim to skills job dim. This arrow is correlated

skills job dim. This arrow is correlated with the filter direction or the flow of

with the filter direction or the flow of how we want data to go. Right now for

how we want data to go. Right now for this visualization, we're using the job

this visualization, we're using the job ID of job postings fact and then skills

ID of job postings fact and then skills of skills dim. And so we're trying to

of skills dim. And so we're trying to filter from skills and get what the job

filter from skills and get what the job I the count of the job ID is over here.

I the count of the job ID is over here. And so if we had and we did a flow of

And so if we had and we did a flow of the arrow, yeah, we can go into this

the arrow, yeah, we can go into this table, but then when we try to go

table, but then when we try to go through this relationship, it's only in

through this relationship, it's only in the direction opposite to this. So you

the direction opposite to this. So you can't do it. But job ID is in here.

can't do it. But job ID is in here. Could we do it for that? Well, let's

Could we do it for that? Well, let's find out. We're going to duplicate this

find out. We're going to duplicate this table and instead of doing the count the

table and instead of doing the count the job ID from this job postings fact

job ID from this job postings fact table, like I said, we're going to do it

table, like I said, we're going to do it from the skills job dim table and throw

from the skills job dim table and throw it into here. And bam, this one does

it into here. And bam, this one does work and shows us the counts of this.

work and shows us the counts of this. But if you remember from our company

But if you remember from our company analysis, we not only were able to do a

analysis, we not only were able to do a count of the jobs, we were also able to

count of the jobs, we were also able to do the median salary. So it solves the

do the median salary. So it solves the problem of getting the count. But now if

problem of getting the count. But now if I wanted to use the actual salary year

I wanted to use the actual salary year average column to get the median, I

average column to get the median, I still can't do that. So this is not the

still can't do that. So this is not the solution we necessarily want to do or

solution we necessarily want to do or the most optimal. I'm going to go ahead

the most optimal. I'm going to go ahead and remove this. What we can do instead

and remove this. What we can do instead to make sure that we get this filtering

to make sure that we get this filtering of skills to be able to happen all the

of skills to be able to happen all the way back to the job postings fact is I'm

way back to the job postings fact is I'm going to take this and rightclick it. Go

going to take this and rightclick it. Go to properties and for between these two

to properties and for between these two tables of job postings fact and skills

tables of job postings fact and skills job dim I'm going to change the cross

job dim I'm going to change the cross filter direction to both it ungrade this

filter direction to both it ungrade this area of apply security filter in both

area of apply security filter in both directions. We're not going to be

directions. We're not going to be applying security filters for this. I'm

applying security filters for this. I'm not controlling who has access to this

not controlling who has access to this data. Anybody can have access to it.

data. Anybody can have access to it. It's public so I don't care about that.

It's public so I don't care about that. Click save. So it updated here as that

Click save. So it updated here as that double arrow. Now when I go into report

double arrow. Now when I go into report view, bam, it is updated. And I can even

view, bam, it is updated. And I can even do something similar crl +v. Instead of

do something similar crl +v. Instead of doing that count of the job ID, I can

doing that count of the job ID, I can drag in that salary year average and do

drag in that salary year average and do that median value. And then we can see

that median value. And then we can see something like unreal has a median

something like unreal has a median salary of almost $101,000.

salary of almost $101,000. It looks like a lot more less frequent

It looks like a lot more less frequent skills have these higher salaries.

skills have these higher salaries. Basically, we can filter this for the

Basically, we can filter this for the top. We'll say top 20 skills. So, I'm

top. We'll say top 20 skills. So, I'm going to go to the skills filter. Do a

going to go to the skills filter. Do a top N. And we're going to do a count by

top N. And we're going to do a count by count of job ID. Selecting the top 20

count of job ID. Selecting the top 20 values. Go apply filter. And now,

values. Go apply filter. And now, closing this on out. We can see these

closing this on out. We can see these are the top 20 values. We can see

are the top 20 values. We can see something that's actually more useful

something that's actually more useful like Scala Spar, Kafka, and then we can

like Scala Spar, Kafka, and then we can even see data analytical tools down

even see data analytical tools down here. Even PowerBI makes the list.

here. Even PowerBI makes the list. Although second from last but at least

Although second from last but at least above Excel. So anytime you're working

above Excel. So anytime you're working with these fact and dimensional tables,

with these fact and dimensional tables, it's important to understand what is

it's important to understand what is going on with the filtering direction.

going on with the filtering direction. In the case of our company thing, we

In the case of our company thing, we were able to do that our company

were able to do that our company analysis because the company when we

analysis because the company when we selected the name here, we were able to

selected the name here, we were able to filter back into the job postings fact

filter back into the job postings fact table to get the count of that job ID.

table to get the count of that job ID. Now there's no practice problems for

Now there's no practice problems for this lesson. So congratulations. I do

this lesson. So congratulations. I do want you to make sure that you do

want you to make sure that you do understand what's going on with this

understand what's going on with this cross filtering and also relationships.

cross filtering and also relationships. So if you need to feel free to go back

So if you need to feel free to go back and rework any of this. We will be

and rework any of this. We will be testing these concepts in upcoming

testing these concepts in upcoming problems in the next lessons, but like I

problems in the next lessons, but like I said, none for this lesson. All right,

said, none for this lesson. All right, with that, I'll see you in the next

with that, I'll see you in the next lesson where we're going into advanced

lesson where we're going into advanced transformations. See you there.

Welcome to this lesson. We're going to be diving in deeper into using advanced

be diving in deeper into using advanced transformations to make that data set we

transformations to make that data set we just imported in for project 2 even more

just imported in for project 2 even more usable. Now, don't get too nervous that

usable. Now, don't get too nervous that the fact that the name of this is

the fact that the name of this is advanced transformations. It's well

advanced transformations. It's well within your capabilities. Let's take a

within your capabilities. Let's take a look at what we're going to do. In the

look at what we're going to do. In the last lesson we imported in, we went

last lesson we imported in, we went through and we were able to visualize

through and we were able to visualize based on the different skills what were

based on the different skills what were their counts. But if you have bosses or

their counts. But if you have bosses or stakeholders like me that are super

stakeholders like me that are super picky, they're not going to really like

picky, they're not going to really like that all these skills are lowercase and

that all these skills are lowercase and don't have the proper capitalization.

don't have the proper capitalization. Well, navigating to the lesson file that

Well, navigating to the lesson file that we're going to be completing by the end

we're going to be completing by the end of this and we're going to be have it

of this and we're going to be have it where the column for skills are going to

where the column for skills are going to be nice and cleaned up with all the

be nice and cleaned up with all the proper capitalization as necessary so we

proper capitalization as necessary so we have no complaining boss or

have no complaining boss or stakeholders. This is going to be done

stakeholders. This is going to be done using some basic text cleanup, a lot of

using some basic text cleanup, a lot of which we've seen before, but also a new

which we've seen before, but also a new concept of conditional columns,

concept of conditional columns, basically an if statement. Now, the

basically an if statement. Now, the first thing that we're actually going to

first thing that we're actually going to be cleaning up revolves around this job

be cleaning up revolves around this job schedule type column, which is inside of

schedule type column, which is inside of our job postings fact table. If we go

our job postings fact table. If we go and click this drop- down arrow here, we

and click this drop- down arrow here, we can see that there is multiple different

can see that there is multiple different values. And this makes it very difficult

values. And this makes it very difficult to say like, hey, what happens if I want

to say like, hey, what happens if I want to analyze a job that has part-time and

to analyze a job that has part-time and contractor? Why can't I make it to where

contractor? Why can't I make it to where the job is part-time and contractor?

the job is part-time and contractor? Previously, in the past, we've just gone

Previously, in the past, we've just gone through and analyzed this by unselecting

through and analyzed this by unselecting this and then only selecting the

this and then only selecting the keywords of like contractor, full-time,

keywords of like contractor, full-time, internship, part-time, and whatnot. But

internship, part-time, and whatnot. But unfortunately, that leaves out a lot of

unfortunately, that leaves out a lot of different jobs. So, we're going to clean

different jobs. So, we're going to clean up this column. Specifically, what we're

up this column. Specifically, what we're going to be doing, we go into the final

going to be doing, we go into the final file for this lesson. We're going to be

file for this lesson. We're going to be creating another dimensional table. And

creating another dimensional table. And this is going to have the job schedule

this is going to have the job schedule type in it. and it's going to be

type in it. and it's going to be connected to our job postings fact table

connected to our job postings fact table via a job ID. If I navigate into table

via a job ID. If I navigate into table view and check out this schedule dim,

view and check out this schedule dim, what I can see is that it's going to

what I can see is that it's going to have an associated job ID and then the

have an associated job ID and then the second column is going to be the uh the

second column is going to be the uh the job schedule type. If I sort this job ID

job schedule type. If I sort this job ID in ascending order, we can see things

in ascending order, we can see things like the job ID zero has multiple

like the job ID zero has multiple different conditions that meets it. So

different conditions that meets it. So now, similar to how jobs can have

now, similar to how jobs can have multiple skills, we can also have it to

multiple skills, we can also have it to where jobs have multiple job schedule

where jobs have multiple job schedule types. With this, we'll be able now to

types. With this, we'll be able now to analyze not only different job counts or

analyze not only different job counts or the correct job counts for a specific uh

the correct job counts for a specific uh job type, but also things like this, the

job type, but also things like this, the median median yearly salary of different

median median yearly salary of different job types. All right, enough meapping.

job types. All right, enough meapping. Let's jump into creating this table

Let's jump into creating this table where we have these two columns, and

where we have these two columns, and it's specific to the job schedule type.

So for this, feel free to start with the file that we were using at the end of

file that we were using at the end of last lesson. If you lost track along the

last lesson. If you lost track along the way, you can just jump into the previous

way, you can just jump into the previous lessons file of 3.3 project 2 import.

lessons file of 3.3 project 2 import. Right now in here under the model view,

Right now in here under the model view, I'm going to go ahead and close these

I'm going to go ahead and close these tabs. We only have dimensional tables

tabs. We only have dimensional tables regarding the skills and the company

regarding the skills and the company down. Like I said, we want to create one

down. Like I said, we want to create one for the schedule type. And the schedule

for the schedule type. And the schedule type information is inside of this job

type information is inside of this job postings fact table. So we need to get

postings fact table. So we need to get it out of there into its own table. So

it out of there into its own table. So I'm going go into transform data.

I'm going go into transform data. Transform data in order to open Power

Transform data in order to open Power Query Editor. So we're going to want to

Query Editor. So we're going to want to be cleaning up this job schedule type

be cleaning up this job schedule type column which is in that job postings

column which is in that job postings fact, right? Which is right here. So the

fact, right? Which is right here. So the first thing we need to do in order to

first thing we need to do in order to get our own dimensional table is we want

get our own dimensional table is we want to create a new table with well one job

to create a new table with well one job schedule type and then also the job ID.

schedule type and then also the job ID. Remember going to that final table this

Remember going to that final table this is what we want it to look like. So with

is what we want it to look like. So with this we need to either we can rightclick

this we need to either we can rightclick it and see we can either duplicate or

it and see we can either duplicate or reference. Now you can also access this

reference. Now you can also access this by ensuring job postings facts is

by ensuring job postings facts is selected and then under the home tab

selected and then under the home tab going to manage your queries. In this

going to manage your queries. In this case I can delete it, duplicate it or

case I can delete it, duplicate it or reference. Remember duplicate as I just

reference. Remember duplicate as I just did now keeps all those same steps going

did now keeps all those same steps going on. I don't want to add unnecessary

on. I don't want to add unnecessary steps and increase the load time. So

steps and increase the load time. So we're not going to do this one. I'm

we're not going to do this one. I'm going to select this one, go to manage,

going to select this one, go to manage, and delete it. Instead, we're going to

and delete it. Instead, we're going to select this one. And now we're going to

select this one. And now we're going to manage it and reference it. And as we

manage it and reference it. And as we can see the source for this step is only

can see the source for this step is only job postings fact which relates to this

job postings fact which relates to this table right here which has those

table right here which has those multiple different steps in it. And as a

multiple different steps in it. And as a quick refresher you can for this table

quick refresher you can for this table just to verify the steps you can do

just to verify the steps you can do something underneath the home tab going

something underneath the home tab going into the advanced editor and this has

into the advanced editor and this has all the code necessary to build this

all the code necessary to build this table which is only a few lines of code.

table which is only a few lines of code. Anyway, cool story. Let's rename this.

Anyway, cool story. Let's rename this. Now up here, we'll name it to schedule

Now up here, we'll name it to schedule dim. On renaming it, press enter and

dim. On renaming it, press enter and everything updates for it.

everything updates for it. So with this table, we now want to keep

So with this table, we now want to keep just two columns. Job ID and job

just two columns. Job ID and job schedule type. Now what we could do is

schedule type. Now what we could do is we come to the very last column here

we come to the very last column here underneath the home tab. I can go to

underneath the home tab. I can go to remove columns. And they have this first

remove columns. And they have this first one of columns. So I can select remove

one of columns. So I can select remove it. And then I can just keep on doing

it. And then I can just keep on doing this until all the different columns are

this until all the different columns are removed and that would take a while. So

removed and that would take a while. So I'm actually going to remove this step

I'm actually going to remove this step of remove columns. We're going to do a

of remove columns. We're going to do a better approach and instead we're going

better approach and instead we're going to select the two columns we want. I'm

to select the two columns we want. I'm going to select this one. Scroll on over

going to select this one. Scroll on over to job schedule type. Holding control

to job schedule type. Holding control I'm going to press job schedule type. So

I'm going to press job schedule type. So now both of these are now selected. And

now both of these are now selected. And then I can go to remove other columns.

then I can go to remove other columns. Now the other method we could do this

Now the other method we could do this that's also just as easy is I can remove

that's also just as easy is I can remove that step again and in this case I could

that step again and in this case I could just doesn't we don't have to have any

just doesn't we don't have to have any column specific column selected. I could

column specific column selected. I could go to choose column and then select

go to choose column and then select choose columns and then from now we

choose columns and then from now we select the columns we want. We want job

select the columns we want. We want job ID and job schedule type. This is

ID and job schedule type. This is actually probably the easiest way and it

actually probably the easiest way and it eventually ends up using just that

eventually ends up using just that removed other column step. So

removed other column step. So remembering our end goal, we want to

remembering our end goal, we want to create only one schedule type per row.

create only one schedule type per row. If I just open this up here to select or

If I just open this up here to select or in view inside of here, I can see that

in view inside of here, I can see that we have multiple different values for

we have multiple different values for these. Also, you may notice this in many

these. Also, you may notice this in many of these different lists that it says

of these different lists that it says list is incomplete. All you have to do

list is incomplete. All you have to do is load more and then you get all the

is load more and then you get all the list of all the different options in

list of all the different options in there. Remember, this is due to the fact

there. Remember, this is due to the fact that we're doing column profiling based

that we're doing column profiling based on that top thousand rows. So whenever I

on that top thousand rows. So whenever I do the drop down like this, it's only

do the drop down like this, it's only showing the values or the unique values

showing the values or the unique values for the first thousand rows. Anyway,

for the first thousand rows. Anyway, well, we need to get a game plan

well, we need to get a game plan together on how we're going to create

together on how we're going to create these into different rows. My idea is

these into different rows. My idea is this. We're going to be using the comma

this. We're going to be using the comma within these fields in order to split

within these fields in order to split them into new rows. Specifically, I can

them into new rows. Specifically, I can select job schedule type. This is what

select job schedule type. This is what we're skipping some steps right now. I

we're skipping some steps right now. I just want to show this. We're going to

just want to show this. We're going to go into split column by delimiter and I

go into split column by delimiter and I can specify that we're going to be

can specify that we're going to be splitting by comma and each occurrence

splitting by comma and each occurrence of the delimiter itself and other

of the delimiter itself and other advanced options we're going to be doing

advanced options we're going to be doing rows. Now the problem with this is as

rows. Now the problem with this is as you're going to see like this case where

you're going to see like this case where we have an and value it didn't split it

we have an and value it didn't split it as necessary and we'd have to do it

as necessary and we'd have to do it again. Also just popping out this

again. Also just popping out this dropdown here. We can see that we have

dropdown here. We can see that we have like oh this one has and in this this is

like oh this one has and in this this is just I'm not liking I'm not liking how

just I'm not liking I'm not liking how this is done. So here's my

this is done. So here's my recommendation. Let's actually go ahead

recommendation. Let's actually go ahead and remove this change type and this

and remove this change type and this split column by delimiter and let's go

split column by delimiter and let's go forth with replacing

forth with replacing like in this case let's replace this

like in this case let's replace this value right here with a comma. So under

value right here with a comma. So under transform I can select this column and

transform I can select this column and we can go to replace values and for this

we can go to replace values and for this for the value to find we're going to do

for the value to find we're going to do space and it is going to be able to pick

space and it is going to be able to pick up this space and we want to replace

up this space and we want to replace this with a comma and then go ahead and

this with a comma and then go ahead and click okay. All right not bad. We can

click okay. All right not bad. We can inspect these values and we can see that

inspect these values and we can see that in the cases where there was multiple

in the cases where there was multiple like three it replaced that and but now

like three it replaced that and but now it has two commas in there. So what I'm

it has two commas in there. So what I'm going to recommend instead is we're

going to recommend instead is we're going to go back one step to remove

going to go back one step to remove other columns and let's start by

other columns and let's start by actually replacing this portion

actually replacing this portion specifically. I'm going to click on it

specifically. I'm going to click on it so we can see it. Let's go forth with

so we can see it. Let's go forth with replacing this section of a comma space

replacing this section of a comma space and with just a comma before we do our

and with just a comma before we do our next one. So, making sure that I am on

next one. So, making sure that I am on that second step, I'm going to go to

that second step, I'm going to go to replace values. It asks if I want to

replace values. It asks if I want to insert a step. I do. I have this value

insert a step. I do. I have this value selected, so it automatically put in

selected, so it automatically put in there. I don't want it. What I want is

there. I don't want it. What I want is comma space and and we're going to

comma space and and we're going to replace it with a comma and then go.

replace it with a comma and then go. Okay. Okay. So, that step did it just

Okay. Okay. So, that step did it just fine, right? It's looking good. And then

fine, right? It's looking good. And then we have our next replace values. And

we have our next replace values. And that one replaces the commas in the just

that one replaces the commas in the just and alone. And I can just inspect this

and alone. And I can just inspect this by looking inside of here. Looking

by looking inside of here. Looking pretty good. And now I can use this

pretty good. And now I can use this split column. And specifically we want

split column. And specifically we want we can split by a number of different

we can split by a number of different things, number of characters, positions,

things, number of characters, positions, whatnot. We're going to be doing it by

whatnot. We're going to be doing it by delimiter because a comma is a

delimiter because a comma is a delimiter. So we're going to select or

delimiter. So we're going to select or enter that comma. You can do leftmost,

enter that comma. You can do leftmost, rightmost, or each occurrence, right? We

rightmost, or each occurrence, right? We could have multiple different values. So

could have multiple different values. So we want each occurrence. And for this,

we want each occurrence. And for this, we want to go into rows. We want to put

we want to go into rows. We want to put it underneath each other uh uh

it underneath each other uh uh underneath itself. We're actually going

underneath itself. We're actually going to demonstrate columns after this, but

to demonstrate columns after this, but for the time being, this is actually

for the time being, this is actually what is our solution going to be. So

what is our solution going to be. So we're going to click okay. And bam. Not

we're going to click okay. And bam. Not too bad. We have Well, let's actually

too bad. We have Well, let's actually inspect it. go to uh go to view and then

inspect it. go to uh go to view and then column profile. Look at this. We have

column profile. Look at this. We have these multiple different values in here.

these multiple different values in here. But if you look at it, some of these

But if you look at it, some of these like we have internship twice and we

like we have internship twice and we also have temp work twice and contractor

also have temp work twice and contractor twice. Why is that? Well, the issue

twice. Why is that? Well, the issue resolves in if I select something like

resolves in if I select something like part-time, I can see this right with

part-time, I can see this right with part-time there's actually an empty

part-time there's actually an empty space right before this. Not a big deal.

space right before this. Not a big deal. This is really easy to clean up

This is really easy to clean up underneath transform. We're going to go

underneath transform. We're going to go into once again text columns format and

into once again text columns format and we want to trim this trim these values.

we want to trim this trim these values. Basically remove any white space on the

Basically remove any white space on the left or right hand side. Now when we go

left or right hand side. Now when we go in to look at this column profile,

in to look at this column profile, there's only six unique values. I can

there's only six unique values. I can actually do the entire data set. And

actually do the entire data set. And looking at everything, there's actually

looking at everything, there's actually one additional this one of volunteer.

one additional this one of volunteer. And it looks like it's uh very minuscule

And it looks like it's uh very minuscule compared to all the rest. But at least

compared to all the rest. But at least with this we confirm we have just unique

with this we confirm we have just unique values. Now this is the final form of

values. Now this is the final form of our dimensional table. Everything is

our dimensional table. Everything is good to go.

good to go. Now I do want to quickly demonstrate

Now I do want to quickly demonstrate splitting columns. We split columns into

splitting columns. We split columns into rows. We're going to split into columns.

rows. We're going to split into columns. This by no means is necessary for you to

This by no means is necessary for you to do. I just want you to have an

do. I just want you to have an understanding that there are different

understanding that there are different options to actually split columns. So,

options to actually split columns. So, I'm going to remove these last few

I'm going to remove these last few steps. If you don't feel comfortable

steps. If you don't feel comfortable following along with this, don't feel uh

following along with this, don't feel uh feel like you're necessary to anyway.

feel like you're necessary to anyway. We're going to go back to the step where

We're going to go back to the step where we've had it necess or have it set up

we've had it necess or have it set up properly where the commas for all the

properly where the commas for all the different values are in the right

different values are in the right spaces. Also, it's doing column profile

spaces. Also, it's doing column profile on the entire data set. I'm just going

on the entire data set. I'm just going to check this back to 1,00 rows. That

to check this back to 1,00 rows. That way, it loads a little bit quicker.

way, it loads a little bit quicker. Anyway, underneath the home tab, we're

Anyway, underneath the home tab, we're going to split column. Once again, we're

going to split column. Once again, we're going to split by delimiter. Remember we

going to split by delimiter. Remember we did this by a comma and we did each

did this by a comma and we did each occurrence. Now under advanced options,

occurrence. Now under advanced options, it's actually selected by default to be

it's actually selected by default to be a column and the number of columns to

a column and the number of columns to split into is set. They put it in here

split into is set. They put it in here of three because looking at those three

of three because looking at those three rows, that's what it assumes it needs to

rows, that's what it assumes it needs to be. I mean, looking at those a thousand

be. I mean, looking at those a thousand rows, it looks like there's only three

rows, it looks like there's only three values at max or three commas at max. So

values at max or three commas at max. So that's why it has the suggestion of

that's why it has the suggestion of three. Anyway, let's go ahead and split

three. Anyway, let's go ahead and split this way. And what we can see is okay

this way. And what we can see is okay job zero has three values whereas job

job zero has three values whereas job one has only one value and then null for

one has only one value and then null for the rest. This is still technically

the rest. This is still technically usable. We just have to unpivot the

usable. We just have to unpivot the data. If I go underneath the transform

data. If I go underneath the transform tab and select underneath here, I can

tab and select underneath here, I can hear see here that we have unpivot

hear see here that we have unpivot columns which with the popup that it

columns which with the popup that it comes it says it translates all but the

comes it says it translates all but the currently unselected columns into

currently unselected columns into attribute and value pairs. Now we have

attribute and value pairs. Now we have job ID selected. So we actually want the

job ID selected. So we actually want the opposite unpivot other columns.

opposite unpivot other columns. Translate all but the currently selected

Translate all but the currently selected columns into attribute value pairs. And

columns into attribute value pairs. And what does it mean by attribute value

what does it mean by attribute value pairs? Well, let's look at it. Inside of

pairs? Well, let's look at it. Inside of here, we have our values, which are the

here, we have our values, which are the different job schedule types now. And

different job schedule types now. And the attribute was that former column

the attribute was that former column name, which I can go back into split

name, which I can go back into split column delimiter. These were the

column delimiter. These were the different column names. They are now

different column names. They are now underneath the attribute. So remember,

underneath the attribute. So remember, we only want to have two columns for

we only want to have two columns for ours. I can select this, go into home,

ours. I can select this, go into home, go to remove this column, and then under

go to remove this column, and then under the value, remember we don't want that

the value, remember we don't want that name. We could change it to job schedule

name. We could change it to job schedule type. And then I can see just looking at

type. And then I can see just looking at part-time that there is a white space

part-time that there is a white space before and after this. So going into

before and after this. So going into transform, I can go into format and

transform, I can go into format and trim. And now we have it in the same

trim. And now we have it in the same manner that we had it previously,

manner that we had it previously, although we had to do a heck of a lot

although we had to do a heck of a lot more different steps. I wanted to cover

more different steps. I wanted to cover that mainly for the aspect of the

that mainly for the aspect of the unpivot columns as you may receive data

unpivot columns as you may receive data in this format right here basically in a

in this format right here basically in a pivot table format and you'll need to

pivot table format and you'll need to unpivot it in order to get it into this

unpivot it in order to get it into this format. So pivoting and unpivoting is a

format. So pivoting and unpivoting is a super useful technique to know about.

super useful technique to know about. Anyway, I'm going to remove all these

Anyway, I'm going to remove all these steps, split the column by delimiter,

steps, split the column by delimiter, specifying it's a comma, and then we're

specifying it's a comma, and then we're going into rows, and then formatting

going into rows, and then formatting this comma to actually or formatting

this comma to actually or formatting this column to trim up the white space.

this column to trim up the white space. We'll leave it like this cuz this one

We'll leave it like this cuz this one easier. This is good to go. Let's close

easier. This is good to go. Let's close and load it in to analyze it.

That's pretty cool. Once we loaded it in, schedule dim the dimensional table

in, schedule dim the dimensional table automatically created a relationship to

automatically created a relationship to job postings fact. If it didn't do this,

job postings fact. If it didn't do this, I can delete this relationship and just

I can delete this relationship and just drag job ID over to here. Ensure that

drag job ID over to here. Ensure that it's selected to connect between these

it's selected to connect between these two. It's a many to one relationship.

two. It's a many to one relationship. Maintaining it single for now. #spoiler

Maintaining it single for now. #spoiler alert. And bam. So, let's get into

alert. And bam. So, let's get into analyzing this. I'm going to create a

analyzing this. I'm going to create a new page. And let's start off simple. We

new page. And let's start off simple. We just want to analyze the count of the

just want to analyze the count of the different job schedule types. So, I'm

different job schedule types. So, I'm going to drag from the SC schedule dim

going to drag from the SC schedule dim the job schedule type into the Y-axis.

the job schedule type into the Y-axis. And then from the job posting fact

And then from the job posting fact table, we want to count that job ID for

table, we want to count that job ID for all those unique job postings and get a

all those unique job postings and get a count. Now going into focus mode, we're

count. Now going into focus mode, we're going to see there's a problem with this

going to see there's a problem with this that we noticed before that we may have

that we noticed before that we may have noticed before. Well, it's actually two

noticed before. Well, it's actually two problems with this. First of all,

problems with this. First of all, there's blank values, null values in

there's blank values, null values in here. I don't like that. The second is

here. I don't like that. The second is that look at the counts of this for

that look at the counts of this for contractor. It's 478,000. Basically,

contractor. It's 478,000. Basically, every single row is being attributed to

every single row is being attributed to that. Why is that? Well, let's inspect

that. Why is that? Well, let's inspect from the model view. Remember schedule

from the model view. Remember schedule dim, we're using job schedule type and

dim, we're using job schedule type and we're trying to filter in the direction

we're trying to filter in the direction of the job ID. Right now, look at where

of the job ID. Right now, look at where this arrow is point. It's pointing

this arrow is point. It's pointing towards the schedule dim. So, it's

towards the schedule dim. So, it's preventing us from actually filtering in

preventing us from actually filtering in the direction we need to filter to get

the direction we need to filter to get the count from this table. So, how do we

the count from this table. So, how do we fix this? Well, we can rightclick it, go

fix this? Well, we can rightclick it, go to the properties, and we can change

to the properties, and we can change this cross filter direction right from

this cross filter direction right from single to both to where now once we save

single to both to where now once we save it, we can see that job schedule type

it, we can see that job schedule type should be allowed to filter the job ID

should be allowed to filter the job ID in this direction. Now, going back to

in this direction. Now, going back to report view, it is looking good. The

report view, it is looking good. The only thing I do not like with this is

only thing I do not like with this is this null value. Now, I could go apply a

this null value. Now, I could go apply a filter and remove job, uh, remove this

filter and remove job, uh, remove this blank value right here, but I'm going to

blank value right here, but I'm going to have to do that for every time I make a

have to do that for every time I make a visualization with this. Instead, the

visualization with this. Instead, the best bet to do is go back into Power

best bet to do is go back into Power Query and looking at that schedule dim,

Query and looking at that schedule dim, we want to remove the blanks. Now, I

we want to remove the blanks. Now, I will caution on this cuz this may be an

will caution on this cuz this may be an approach you may try to take. You can

approach you may try to take. You can select remove rows and they have this of

select remove rows and they have this of remove duplicates, remove blank rows and

remove duplicates, remove blank rows and remove errors. So in our case, let's try

remove errors. So in our case, let's try to remove blank rows. Well, whenever I

to remove blank rows. Well, whenever I go here and select this down arrow, I

go here and select this down arrow, I still have blank values in there. So

still have blank values in there. So technically, it's not filtering for the

technically, it's not filtering for the type of blank values that are located

type of blank values that are located inside of here that are actually

inside of here that are actually technically null values. Anyway, I'm

technically null values. Anyway, I'm going to remove this step of remove

going to remove this step of remove blank rows because it didn't work

blank rows because it didn't work anyway. And the best way to filter for

anyway. And the best way to filter for this is just uncheck blank inside of

this is just uncheck blank inside of here and then click okay. And it adds a

here and then click okay. And it adds a step for filtered rows to remove this.

step for filtered rows to remove this. Also, we can see that this green bar now

Also, we can see that this green bar now takes up the entire length of this. So,

takes up the entire length of this. So, I know it worked fine. Click close and

I know it worked fine. Click close and apply. Once it loads in the data, boom,

apply. Once it loads in the data, boom, it removes that blank value. So, now we

it removes that blank value. So, now we can do something like this. Since both

can do something like this. Since both of these are set up, instead of just

of these are set up, instead of just doing job count, we can also do the

doing job count, we can also do the median salary, changing this to median.

median salary, changing this to median. And we can see things like, oo,

And we can see things like, oo, part-time pays better than full-time.

part-time pays better than full-time. So, that's another unique thing that we

So, that's another unique thing that we found out about this. Not only do yearly

found out about this. Not only do yearly salary jobs pay better than hoursly, but

salary jobs pay better than hoursly, but part-time jobs are paying better than

part-time jobs are paying better than full-time jobs.

Let's get into the second portion of this, which should be a little bit

this, which should be a little bit quicker. We're going to go into focus

quicker. We're going to go into focus mode. Remember, we're trying to clean up

mode. Remember, we're trying to clean up now, as we demonstrated earlier, what

now, as we demonstrated earlier, what are the top skills and data,

are the top skills and data, specifically those different skills. We

specifically those different skills. We want to add the proper capitalization

want to add the proper capitalization for each of these as necessary. This

for each of these as necessary. This however is going to take a little bit

however is going to take a little bit more cleanup because if we navigate to

more cleanup because if we navigate to our final file we can see that yeah some

our final file we can see that yeah some of them do just start with only one

of them do just start with only one capital letter like Python but then you

capital letter like Python but then you have things like SQL and AWS which are

have things like SQL and AWS which are all capitals letters or you even have

all capitals letters or you even have something like PowerBI where the last

something like PowerBI where the last two letters are all capital letters.

two letters are all capital letters. Anyway, back in our file where we want

Anyway, back in our file where we want to actually modify this. Let's jump into

to actually modify this. Let's jump into cleaning this up. We're going to be

cleaning this up. We're going to be cleaning up specifically the skills dim

cleaning up specifically the skills dim table and this skills column right here.

table and this skills column right here. So, as we've seen before underneath the

So, as we've seen before underneath the transform tab, we have the option to

transform tab, we have the option to format different values. In this case,

format different values. In this case, text values. We could make our skills

text values. We could make our skills all lowercase, which they all are, or we

all lowercase, which they all are, or we can make it all uppercase in this case,

can make it all uppercase in this case, or we can even capitalize each word,

or we can even capitalize each word, which is actually what we want to do for

which is actually what we want to do for the first step. So, I'm going to go

the first step. So, I'm going to go ahead and remove this uppercase text one

ahead and remove this uppercase text one cuz we're not going to keep that one.

cuz we're not going to keep that one. All right. So, that's a good first step.

All right. So, that's a good first step. But now, if we look at values like SQL,

But now, if we look at values like SQL, it's not proper. We would expect to be

it's not proper. We would expect to be all caps for SQL. And there's only a

all caps for SQL. And there's only a handful of other names in here that I

handful of other names in here that I actually do want to clean up, such as

actually do want to clean up, such as NoSQL, PowerBI, DAX, of course, DAX, and

NoSQL, PowerBI, DAX, of course, DAX, and we'll give some love to SAS. So let's

we'll give some love to SAS. So let's just start with PowerBI first. We're

just start with PowerBI first. We're going to be using a conditional column.

going to be using a conditional column. Unfortunately, there's nothing under

Unfortunately, there's nothing under transform for this. So we have to add a

transform for this. So we have to add a new column. And we can come up here into

new column. And we can come up here into conditional column. Our current column

conditional column. Our current column is called skills. So we'll say that this

is called skills. So we'll say that this new column is called skills clean. Now

new column is called skills clean. Now we need to go through and fill out this

we need to go through and fill out this basically if statement. So if column

basically if statement. So if column name if skills equals in our case

name if skills equals in our case powerbi you have to give the correct

powerbi you have to give the correct proper uh punctuation for that that you

proper uh punctuation for that that you expect to see. We want it to be powerbi

expect to see. We want it to be powerbi with two capital letters for this. And

with two capital letters for this. And then from there we'll go ahead and just

then from there we'll go ahead and just click okay. This has a couple errors.

click okay. This has a couple errors. Specifically one none of the values

Specifically one none of the values transfer. So everything's null, but our

transfer. So everything's null, but our beloved PowerBI is transferred just

beloved PowerBI is transferred just fine. So let's go back into editing

fine. So let's go back into editing that. I can go to that step of add

that. I can go to that step of add conditional columns, click that settings

conditional columns, click that settings icon, and the first thing I want to do

icon, and the first thing I want to do is so we have this if and then we have

is so we have this if and then we have an else. And right now it's set to the

an else. And right now it's set to the value of null and that's why it's all

value of null and that's why it's all null. Instead, we want to select a

null. Instead, we want to select a column, specifically the column of

column, specifically the column of skills. Now, whenever we do this of

skills. Now, whenever we do this of okay, all these columns are filled in

okay, all these columns are filled in along with PowerBI. I'm also noticing

along with PowerBI. I'm also noticing that there's a PowerBI without a space

that there's a PowerBI without a space in it. I'm going to fix this one as

in it. I'm going to fix this one as well. So, we can go back into modifying

well. So, we can go back into modifying this column. We can add a clause in

this column. We can add a clause in here. We want to look in the skills

here. We want to look in the skills column equals PowerBI all lowercase. And

column equals PowerBI all lowercase. And I'm just going to copy this above and

I'm just going to copy this above and paste this in here. Click okay. And this

paste this in here. Click okay. And this one is now fixed. Okay. Next, remember

one is now fixed. Okay. Next, remember also we want to change SAS. So, I'm

also we want to change SAS. So, I'm going to go into here, add another

going to go into here, add another clause, skills equals SAS, and change

clause, skills equals SAS, and change this to all caps, and go ahead, click

this to all caps, and go ahead, click okay. Remember, the other one we want to

okay. Remember, the other one we want to do was SQL. But I'm going to actually

do was SQL. But I'm going to actually recommend a different approach besides

recommend a different approach besides conditional columns because look, we

conditional columns because look, we have things like SQL here. We have NoSQL

have things like SQL here. We have NoSQL here. We have TSQL down here. Once

here. We have TSQL down here. Once again, no SQL SQL server. Anyway,

again, no SQL SQL server. Anyway, there's a bunch of different SQLs in

there's a bunch of different SQLs in here. Conditional columns aren't going

here. Conditional columns aren't going to be able to fix that specifically

to be able to fix that specifically those cases without replacing the entire

those cases without replacing the entire contents. Instead, with that skills

contents. Instead, with that skills clean column, I want to replace values.

clean column, I want to replace values. And so, in cases where they have capital

And so, in cases where they have capital SQL, I want to have SQL in all caps. So,

SQL, I want to have SQL in all caps. So, replaced up here. And then let's also

replaced up here. And then let's also replace these other conditions of like

replace these other conditions of like no SQL. So once again, we can do replace

no SQL. So once again, we can do replace values SQL all lowercase. We'll do

values SQL all lowercase. We'll do capital SQL. Click okay. And bam. This

capital SQL. Click okay. And bam. This is looking a lot better. Now, there's a

is looking a lot better. Now, there's a bunch of other letters in here that you

bunch of other letters in here that you can feel free to go through and clean

can feel free to go through and clean up, but I'll leave that for you. This is

up, but I'll leave that for you. This is good enough for what I need. I do,

good enough for what I need. I do, however, want to clean up these columns.

however, want to clean up these columns. This is just unnecessary amount of

This is just unnecessary amount of different columns in here. So, I don't

different columns in here. So, I don't want two skills and skills clean. What

want two skills and skills clean. What I'll do is I'm going to remove the

I'll do is I'm going to remove the skills column. And then with skills

skills column. And then with skills clean, we're going to just rename that

clean, we're going to just rename that to skills. Also, not a fan of this

to skills. Also, not a fan of this order, so I'm going to drag it over.

order, so I'm going to drag it over. This is looking good. We'll go ahead and

This is looking good. We'll go ahead and close and apply. Now, looking at these

close and apply. Now, looking at these skills, let's go into focus mode for top

skills, let's go into focus mode for top skills. We can see that all these values

skills. We can see that all these values are now properly formatted. Not too bad.

are now properly formatted. Not too bad. So it shows the power of power query and

So it shows the power of power query and even doing simple thing like this like

even doing simple thing like this like text cleanup. And remember because all

text cleanup. And remember because all of these steps are in power query if we

of these steps are in power query if we ever have to refresh our data source

ever have to refresh our data source it's going to be still running through

it's going to be still running through the same data cleaning process. And so

the same data cleaning process. And so after actually going through that it

after actually going through that it still will apply it and you're going to

still will apply it and you're going to have your cleaned up values. All right

have your cleaned up values. All right it's now your turn to give it a try. We

it's now your turn to give it a try. We have some practice problems around

have some practice problems around practicing with splitting column by rows

practicing with splitting column by rows and also columns and then also how to

and also columns and then also how to create conditional columns. With that,

create conditional columns. With that, I'll see you in the next lesson. We're

I'll see you in the next lesson. We're going to be getting into how to append

going to be getting into how to append and also merge data sets. With that, see

and also merge data sets. With that, see you there.

Welcome to this second to last lesson in Power Query. We're going to get deeper

Power Query. We're going to get deeper and deeper into more advanced features.

and deeper into more advanced features. Specifically in this one, we're going to

Specifically in this one, we're going to be going over two major concepts.

be going over two major concepts. Specifically, how to append queries.

Specifically, how to append queries. Basically, put queries that have similar

Basically, put queries that have similar columns together on top of each other.

columns together on top of each other. And then merge queries, which is

And then merge queries, which is basically connecting two tables that

basically connecting two tables that have similar maybe column ids and then

have similar maybe column ids and then merging them together. We'll also have a

merging them together. We'll also have a bonus topic after the merge section

bonus topic after the merge section jumping into how to perform group by

jumping into how to perform group by analysis which is very similar to

analysis which is very similar to basically pivoting our data.

So jumping into our append example, we navigate into our project folder under

navigate into our project folder under data. We can see we have this data set

data. We can see we have this data set called job postings monthly. I'm going

called job postings monthly. I'm going go ahead and open it up. Now this is

go ahead and open it up. Now this is really common how my co-workers love to

really common how my co-workers love to send me data in that in this we have all

send me data in that in this we have all these different sheets within this

these different sheets within this workbook and each sheet is a different

workbook and each sheet is a different month. It's in very important to note

month. It's in very important to note that these sheets all have the same

that these sheets all have the same column format meaning they all go to

column format meaning they all go to column Q they maintain the same column

column Q they maintain the same column titles and I can verify this by going to

titles and I can verify this by going to another thing saying that it also goes

another thing saying that it also goes to Q. Anyway, we want to use append to

to Q. Anyway, we want to use append to make all 12 of these sheets here into

make all 12 of these sheets here into one single table. And we could do this

one single table. And we could do this with Power Query. For this example only,

with Power Query. For this example only, we're going to be starting with a blank

we're going to be starting with a blank workbook because after we get done with

workbook because after we get done with this, we're not going to keep it any

this, we're not going to keep it any further. The only point of this is to

further. The only point of this is to demonstrate how to do append. Anyway,

demonstrate how to do append. Anyway, start a blank report. Let's bring this

start a blank report. Let's bring this data into Power Query now by going to

data into Power Query now by going to get data. We're going to be getting this

get data. We're going to be getting this from Excel workbook. I guess I could

from Excel workbook. I guess I could also click it right there. Inside of our

also click it right there. Inside of our course project folder, I'm going to

course project folder, I'm going to navigate into data and select job

navigate into data and select job postings monthly and select open. Inside

postings monthly and select open. Inside of here, I have my Excel workbook and I

of here, I have my Excel workbook and I have all the different sheets. I need to

have all the different sheets. I need to now go through and I want all this data.

now go through and I want all this data. So, I'm going to select it all. From

So, I'm going to select it all. From there, we have the option to load it all

there, we have the option to load it all or get into Power Query. So, I'm going

or get into Power Query. So, I'm going to do that and select transform data.

to do that and select transform data. So, looking in our queries pane, we can

So, looking in our queries pane, we can see that all 12 of the sheets are loaded

see that all 12 of the sheets are loaded into here. And I can also verify by

into here. And I can also verify by scrolling on over. Looks like all the

scrolling on over. Looks like all the data is there. Looks good enough for me.

data is there. Looks good enough for me. Now we want to combine these queries

Now we want to combine these queries conveniently under the home tab. I can

conveniently under the home tab. I can come over here into combine. Select the

come over here into combine. Select the drop down. We have a few different

drop down. We have a few different options. We have merge. We're

options. We have merge. We're demonstrating append. I don't know if we

demonstrating append. I don't know if we can zoom in on this and see that append.

can zoom in on this and see that append. Basically we're appending on all the

Basically we're appending on all the different tables. With this clicking

different tables. With this clicking this dropown, we can append queries or

this dropown, we can append queries or append queries as new. Basically a new

append queries as new. Basically a new query. If I do append queries with that

query. If I do append queries with that December 2024 selected, it's going to do

December 2024 selected, it's going to do the modifications inside that current

the modifications inside that current query. So in this case, if I were to

query. So in this case, if I were to just append on January in this case,

just append on January in this case, it's going to append it on to that

it's going to append it on to that query. And that's not necessarily what I

query. And that's not necessarily what I want. Instead, what I'm going to do is

want. Instead, what I'm going to do is come up here to a a combine, select

come up here to a a combine, select append queries, append queries as new to

append queries, append queries as new to a new query. You can do two tables or

a new query. You can do two tables or three or more tables in our case. And

three or more tables in our case. And then we need to move all the ones we

then we need to move all the ones we want over to the other side. In my case,

want over to the other side. In my case, I accidentally added December twice. So

I accidentally added December twice. So make sure you don't do that as all you

make sure you don't do that as all you got to do is just select remove. And now

got to do is just select remove. And now all the ones were selected. Go and

all the ones were selected. Go and select okay. Now here we have our append

select okay. Now here we have our append query. If I look at the source step by

query. If I look at the source step by just open this up, we can see that all

just open this up, we can see that all we're doing is combining all these

we're doing is combining all these different other queries. We're going to

different other queries. We're going to name this as data jobs append. Now,

name this as data jobs append. Now, before we get into closing and loading

before we get into closing and loading this, we don't want to close and load

this, we don't want to close and load all these other queries in here. Also, I

all these other queries in here. Also, I don't really like the organization of

don't really like the organization of this. What I'm going to do is I'm going

this. What I'm going to do is I'm going to select all of these and hold control

to select all of these and hold control to select all the different monthly

to select all the different monthly queries. And then I'm going to

queries. And then I'm going to rightclick it and select move to group.

rightclick it and select move to group. We're going to create a new group for

We're going to create a new group for this and call this data jobs monthly.

this and call this data jobs monthly. Real original. Okay. So now it makes it

Real original. Okay. So now it makes it a lot easier to hide these queries. And

a lot easier to hide these queries. And then the other query, if you will, the

then the other query, if you will, the data jobs append is inside other

data jobs append is inside other queries. All right, one last thing,

queries. All right, one last thing, right? That the one that I actually

right? That the one that I actually really care about is I don't want to

really care about is I don't want to load this. So I'm going to rightclick

load this. So I'm going to rightclick one of the queries and I'm going to

one of the queries and I'm going to uncheck enable load and it's going to go

uncheck enable load and it's going to go to italics. So know that to sign

to italics. So know that to sign symbolize that that's what happened.

symbolize that that's what happened. Then once we did it all for these, I'm

Then once we did it all for these, I'm going to hit close and apply. So we can

going to hit close and apply. So we can load it in and only that one query is

load it in and only that one query is loaded in. You will notice it will

loaded in. You will notice it will evaluate the other ones because it needs

evaluate the other ones because it needs to evaluate them and go through them,

to evaluate them and go through them, but only one will load. Inside the data

but only one will load. Inside the data pane, we can see that one is loaded in.

pane, we can see that one is loaded in. As always, we should go into table view

As always, we should go into table view and verify that yep, everything's

and verify that yep, everything's looking like it's right. I can also see

looking like it's right. I can also see there that we have 479,000

there that we have 479,000 rows, which is the number we would

rows, which is the number we would expect to see for all our different data

expect to see for all our different data jobs posting. Just so it doesn't go

jobs posting. Just so it doesn't go without saying this data is exactly the

without saying this data is exactly the same that we've operated previously

same that we've operated previously with. I just broke it up into different

with. I just broke it up into different sheets. Anyway, with this just verifying

sheets. Anyway, with this just verifying it, I can throw in something like a line

it, I can throw in something like a line chart. Minimize this filters. Then put

chart. Minimize this filters. Then put job posted date on the x-axis and then a

job posted date on the x-axis and then a count of jobs in the y-axis. Remember,

count of jobs in the y-axis. Remember, we got to drill down. We can see this on

we got to drill down. We can see this on a quarterly basis, monthly basis, and

a quarterly basis, monthly basis, and then daily basis. and inspecting. It

then daily basis. and inspecting. It doesn't look like there's any gaps

doesn't look like there's any gaps between January all the way to December.

Now that we have a pen down, let's move into merge, which is slightly more

into merge, which is slightly more complex. Okay, so this is usually the

complex. Okay, so this is usually the use case that I find for it. Remember,

use case that I find for it. Remember, we have our data in this format.

we have our data in this format. Specifically, we have our fact table

Specifically, we have our fact table here and then our other dimensional

here and then our other dimensional tables. Let's say now we need to give

tables. Let's say now we need to give this data to our boss, but they haven't

this data to our boss, but they haven't watched this tutorial. So, they don't

watched this tutorial. So, they don't know the difference between fact and

know the difference between fact and dimensional tables or how to establish

dimensional tables or how to establish relationships. They just want a flat

relationships. They just want a flat table with everything on it. And this is

table with everything on it. And this is the table that we're going to create

the table that we're going to create with this specifically. You can see from

with this specifically. You can see from it, it looks very similar to our job

it, it looks very similar to our job postings fact table. But if I scroll all

postings fact table. But if I scroll all the way to the right, it also has all

the way to the right, it also has all those skills and then also that skill

those skills and then also that skill type in it. What we're going to do is

type in it. What we're going to do is merge the data set. Now, it's important

merge the data set. Now, it's important to remember this. So, look at this

to remember this. So, look at this table, right? The job skills flat. It is

table, right? The job skills flat. It is now 2.3 million rows. If we go back to

now 2.3 million rows. If we go back to job posting fact, it's 478,000.

job posting fact, it's 478,000. Why is that? Well, whenever we merge it,

Why is that? Well, whenever we merge it, so going back to that flat table, we can

so going back to that flat table, we can see like in this case, these job IDs are

see like in this case, these job IDs are repeating. So, this is a repeating job

repeating. So, this is a repeating job posting in order to capture all the

posting in order to capture all the different skills on different rows. So,

different skills on different rows. So, something to think about whenever we get

something to think about whenever we get to the end of this. Anyway, let's jump

to the end of this. Anyway, let's jump into doing this. So for the file for

into doing this. So for the file for this and the remaining portion of this

this and the remaining portion of this lesson, you can use what we had from the

lesson, you can use what we had from the previous lesson on advanced

previous lesson on advanced transformations or you can just open up

transformations or you can just open up this file in the project folder on

this file in the project folder on advanced transformations and use that.

advanced transformations and use that. Our final results are going to be in

Our final results are going to be in that 3.5_merge.

that 3.5_merge. So let's jump into it. Remember we want

So let's jump into it. Remember we want to combine our job postings fact table

to combine our job postings fact table with our skills. Because of this, we

with our skills. Because of this, we have to merge in this connector uh table

have to merge in this connector uh table first and then in our finally skills dim

first and then in our finally skills dim table. So, it's going to be more of a

table. So, it's going to be more of a multi-step process. In order to do this,

multi-step process. In order to do this, you know what we got to do? Go into

you know what we got to do? Go into power query editor. So, let's jump into

power query editor. So, let's jump into our first step. We're going to want to

our first step. We're going to want to connect our job postings fact to skills

connect our job postings fact to skills job dim. So, we have the job postings

job dim. So, we have the job postings fact connected. I can come up here in

fact connected. I can come up here in the home tab to combine. And we have

the home tab to combine. And we have once again similar options of merge

once again similar options of merge queries and merge queries as new. I

queries and merge queries as new. I don't want to affect our original job

don't want to affect our original job postings fact table. So I'm going to say

postings fact table. So I'm going to say merge queries as new. And it's going to

merge queries as new. And it's going to say, hey, select the tables and matching

say, hey, select the tables and matching columns to create a merge table. We do

columns to create a merge table. We do want the job postings fact table. And

want the job postings fact table. And then we want the skills job dim. What

then we want the skills job dim. What are we going to be connecting them on?

are we going to be connecting them on? Well, the job ID. So we need to make

Well, the job ID. So we need to make sure we select both of those. Now, what

sure we select both of those. Now, what do we select next for the join kind?

do we select next for the join kind? There are underneath here six different

There are underneath here six different ones that we have. So, we're going to

ones that we have. So, we're going to walk through each of these six different

walk through each of these six different types of joins. And in order to do this,

types of joins. And in order to do this, it's important to understand we're going

it's important to understand we're going to be showing some visuals with it. And

to be showing some visuals with it. And in this, we're going to be demonstrating

in this, we're going to be demonstrating how whenever you join table A to table

how whenever you join table A to table B, what data is included and the visuals

B, what data is included and the visuals associated with this. And this

associated with this. And this represents with the shaded color in blue

represents with the shaded color in blue represents what data we're going to keep

represents what data we're going to keep with this. Just to be clear, table A in

with this. Just to be clear, table A in our case is the job postings fact table

our case is the job postings fact table and then table B is the skills job dim.

and then table B is the skills job dim. So the first option is left outer and

So the first option is left outer and with that we're going to select all from

with that we're going to select all from the first and then matching from the

the first and then matching from the second. So with that, this demonstrates

second. So with that, this demonstrates that no matter what, everything from

that no matter what, everything from that table A, the job postings fact

that table A, the job postings fact table is going to be included. And then

table is going to be included. And then in table B, only things that match up

in table B, only things that match up will be included. So that's why only

will be included. So that's why only that center portion is colored. Now with

that center portion is colored. Now with this, we can see down at the bottom, the

this, we can see down at the bottom, the selection matches 410,000 out of 478,000

selection matches 410,000 out of 478,000 rows from the first table. Basically

rows from the first table. Basically what this saying is 410,000 job postings

what this saying is 410,000 job postings have associated skill or skills with it.

have associated skill or skills with it. This is actually a perfectly fine join

This is actually a perfectly fine join to use, but we need to talk about the

to use, but we need to talk about the other five still. Next up is a right

other five still. Next up is a right outer and this does all from the second

outer and this does all from the second and then matching from the first. This

and then matching from the first. This is basically just opposite from the last

is basically just opposite from the last one. Table B or our skills job dim.

one. Table B or our skills job dim. We're going to keep every single value

We're going to keep every single value from that and then only matching records

from that and then only matching records from our leftmost table or that job

from our leftmost table or that job postings fact table. Inspecting this

postings fact table. Inspecting this down at the bottom, we can see that 2.2

down at the bottom, we can see that 2.2 million out of 2.2 million rows from the

million out of 2.2 million rows from the second table are matched. But that's not

second table are matched. But that's not the full story. Remember whenever we did

the full story. Remember whenever we did left outer, it basically said that of

left outer, it basically said that of the 478,000

the 478,000 job postings, there were only 410,000

job postings, there were only 410,000 that obtained that had skills. So what's

that obtained that had skills. So what's happening now with this write outer is

happening now with this write outer is that there's about 68,000

that there's about 68,000 differences or job postings without a

differences or job postings without a skill. Therefore, whenever we do this

skill. Therefore, whenever we do this route out right outer, we would be

route out right outer, we would be missing those 68,000 jobs because they

missing those 68,000 jobs because they don't have a skill in our final join.

don't have a skill in our final join. Because of that, we're not going to use

Because of that, we're not going to use a right outer because we want all the

a right outer because we want all the job postings. Next up, we're going to

job postings. Next up, we're going to skip over full outer and get into inner.

skip over full outer and get into inner. In this, it only matches matching rows.

In this, it only matches matching rows. Looking at this visually, we can see

Looking at this visually, we can see that okay, only the matching rows from

that okay, only the matching rows from table A and table B are going to be

table A and table B are going to be included in that. So if we remember from

included in that. So if we remember from our write outer, what do you think is

our write outer, what do you think is going to happen? Well, it tells us only

going to happen? Well, it tells us only 410,000 of the 478,000 job postings are

410,000 of the 478,000 job postings are going to be included in this. If we

going to be included in this. If we scroll over, we also see that from the

scroll over, we also see that from the second table, all the skills would be

second table, all the skills would be included. So that makes sense with that

included. So that makes sense with that because every single skill has an

because every single skill has an associated job posting. Anyway, we're

associated job posting. Anyway, we're not going to use enter for this. All

not going to use enter for this. All right, next up is left anti. And in

right, next up is left anti. And in this, it is rows only in the first

this, it is rows only in the first table. In this case, we're only going to

table. In this case, we're only going to be keeping those in table A, so the job

be keeping those in table A, so the job posting facts table that have no

posting facts table that have no correlated skill with it. And we confirm

correlated skill with it. And we confirm this by saying, hey, this selection

this by saying, hey, this selection excludes 410,000 of 478,000. So

excludes 410,000 of 478,000. So basically this is going to return 68,000

basically this is going to return 68,000 job postings without any skills. This is

job postings without any skills. This is completely useless at least in our case.

completely useless at least in our case. Moving on to right anti. This one is

Moving on to right anti. This one is going to have only rows only in the

going to have only rows only in the second. This is only going to include

second. This is only going to include rows from the right table that do not

rows from the right table that do not have any matches in the left table,

have any matches in the left table, which means that there's no rows. And as

which means that there's no rows. And as we can see, the selection excludes all

we can see, the selection excludes all 227,000 of the 227,000. Sorry, 2.2

227,000 of the 227,000. Sorry, 2.2 million. So no matches are going to be

million. So no matches are going to be done with this one. Also completely

done with this one. Also completely useless in our case. All right, last one

useless in our case. All right, last one is full outer and that's going to be

is full outer and that's going to be doing all rows from both. This one will

doing all rows from both. This one will recruit all results from table A and

recruit all results from table A and table B whether they match or not. In

table B whether they match or not. In our case, we do know that table B

our case, we do know that table B matches all of table A. So whenever we

matches all of table A. So whenever we do this, there's not going to be any

do this, there's not going to be any problems. And actually this full outer

problems. And actually this full outer in our case is basically the same thing

in our case is basically the same thing as a left outer because like I said all

as a left outer because like I said all the skills are associated with a certain

the skills are associated with a certain job posting and it gives us that similar

job posting and it gives us that similar response of the selection matches

response of the selection matches 410,000 out of 478,000

410,000 out of 478,000 that basically have skills and

that basically have skills and everything from the second table matches

everything from the second table matches completely. We're going to go with this

completely. We're going to go with this full outer. I'm going to click okay. So

full outer. I'm going to click okay. So we have this merge data in. I'm going to

we have this merge data in. I'm going to go ahead and change this name from merge

go ahead and change this name from merge one to job skills flat. Also, you're

one to job skills flat. Also, you're going to notice with this, especially

going to notice with this, especially with this large of a data set, that when

with this large of a data set, that when we start doing this, it takes a while to

we start doing this, it takes a while to load. So, one of the drawbacks of doing

load. So, one of the drawbacks of doing merge in Power Query, if your computer's

merge in Power Query, if your computer's not robust enough to handle this, feel

not robust enough to handle this, feel free to just watch along so you can gain

free to just watch along so you can gain the experience at least seeing what

the experience at least seeing what happens. No need to get frustrated

happens. No need to get frustrated because you don't have enough RAM in

because you don't have enough RAM in your computer to handle this. So now

your computer to handle this. So now scrolling all over to the right, what we

scrolling all over to the right, what we can see is that we did merge on that

can see is that we did merge on that skills job dim table into our job

skills job dim table into our job posting flat table, which is all the

posting flat table, which is all the columns to the left. But they merged it

columns to the left. But they merged it in with this in a table. I'm actually

in with this in a table. I'm actually going to click on this just to show what

going to click on this just to show what it is. And it's going to navigate into

it is. And it's going to navigate into this specific row of the table. And so

this specific row of the table. And so what we can see from this is that we

what we can see from this is that we clicked into one row. That row was for

clicked into one row. That row was for the job ID of five. and it had all of

the job ID of five. and it had all of these different skills associated with

these different skills associated with it. So, pretty cool. We can navigate

it. So, pretty cool. We can navigate into it. Overall, not too important. We

into it. Overall, not too important. We need to actually do this step. We need

need to actually do this step. We need to actually do this for all the rows.

to actually do this for all the rows. And we can do this by scrolling on over

And we can do this by scrolling on over here. And up here at the top, there's

here. And up here at the top, there's this expand icon. Whenever I click it, I

this expand icon. Whenever I click it, I can either expand or I can also

can either expand or I can also aggregate it based on the sum of these

aggregate it based on the sum of these things in here, which is not what we

things in here, which is not what we want to do. We want to actually expand

want to do. We want to actually expand it out. And with this, it's going to

it out. And with this, it's going to give us the job ID and the skill ID.

give us the job ID and the skill ID. Remember, in job posting facts, we

Remember, in job posting facts, we already have the job ID. So, I don't

already have the job ID. So, I don't want to repeat it again here. So, I'm

want to repeat it again here. So, I'm only going to import in the skill ID and

only going to import in the skill ID and click okay. It's now expanded out. And

click okay. It's now expanded out. And if I scroll on over, I can see now that

if I scroll on over, I can see now that these job postings are duplicated

these job postings are duplicated because this case, job ID is repeated

because this case, job ID is repeated multiple times for five, nine, and

multiple times for five, nine, and whatnot. But we're not done. Remember,

whatnot. But we're not done. Remember, we just connected the job postings fact

we just connected the job postings fact table to SC skills job dim. We still

table to SC skills job dim. We still need to connect to the skills dim table.

need to connect to the skills dim table. Because of that, we need to do another

Because of that, we need to do another merge. With job skills flat table

merge. With job skills flat table selected, I'm going to go into the home

selected, I'm going to go into the home tab. I'm going to combine and we need to

tab. I'm going to combine and we need to merge. Again, I don't need to create a

merge. Again, I don't need to create a new query. We can continue working in

new query. We can continue working in this one. So, we're just going to do

this one. So, we're just going to do merge queries. Now for this one from the

merge queries. Now for this one from the job skills flat table we want to connect

job skills flat table we want to connect on that skill ID on skills job dim and

on that skill ID on skills job dim and we want to connect to the skills dim

we want to connect to the skills dim table on that skill ID. So for this join

table on that skill ID. So for this join which one do you think we're going to

which one do you think we're going to use for this in the fact that we want to

use for this in the fact that we want to keep everything from the job postings

keep everything from the job postings fact table while also putting all of our

fact table while also putting all of our skill information into this table. Well,

skill information into this table. Well, with left outer, what we're seeing is

with left outer, what we're seeing is that we're going to match 2.2 million

that we're going to match 2.2 million out of 2.3 million rows from the first

out of 2.3 million rows from the first table. And why is there a difference in

table. And why is there a difference in this? Well, it has to do with the fact

this? Well, it has to do with the fact that there are some jobs that don't have

that there are some jobs that don't have a skill. In our case, this left outer is

a skill. In our case, this left outer is going to work. And also, we can see we

going to work. And also, we can see we get the same output for full outer. In

get the same output for full outer. In this case, once again, both of those are

this case, once again, both of those are perfectly fine to use. I'm going to go

perfectly fine to use. I'm going to go ahead and click okay because I know it's

ahead and click okay because I know it's going to preserve all the job postings

going to preserve all the job postings in the first table and just match up

in the first table and just match up those skills for all those that should

those skills for all those that should have a match. Now that we have this

have a match. Now that we have this skills dim added once again it's going

skills dim added once again it's going to provide it in a table like format. I

to provide it in a table like format. I can click into it so we can investigate

can click into it so we can investigate it. You don't need to do this portion

it. You don't need to do this portion but what I would expect to see is since

but what I would expect to see is since this is only zero we should only see the

this is only zero we should only see the skill for zero. And ding ding ding

skill for zero. And ding ding ding that's what we got. Zero SQL and it's

that's what we got. Zero SQL and it's the type programming. All right, I'm

the type programming. All right, I'm gonna get rid of this. I was just doing

gonna get rid of this. I was just doing that for demo. So, what we need to do is

that for demo. So, what we need to do is come up here to that skills dim table

come up here to that skills dim table and actually expand it. Once again, we

and actually expand it. Once again, we have a skill ID already in there. We

have a skill ID already in there. We don't want that. We're going to uncheck

don't want that. We're going to uncheck that and then expand out the other two

that and then expand out the other two remaining columns. All right, not too

remaining columns. All right, not too bad. We have this in here. I just want

bad. We have this in here. I just want to do a little bit of cleanup.

to do a little bit of cleanup. Specifically, I don't need this skill ID

Specifically, I don't need this skill ID column. So, I'm going to end up removing

column. So, I'm going to end up removing that column. And then we're going to

that column. And then we're going to rename both this and instead of being

rename both this and instead of being skills dim skills, it's just going to be

skills dim skills, it's just going to be skills. And I'll change this one to

skills. And I'll change this one to skill type. Now we're complete and we've

skill type. Now we're complete and we've cleaned up this table, this job skills

cleaned up this table, this job skills flat. We're going to now load this into

flat. We're going to now load this into PowerBI and just visualize it to make

PowerBI and just visualize it to make sure that it's working correctly. But I

sure that it's working correctly. But I do want to show something specifically.

do want to show something specifically. We just created a new table. And this

We just created a new table. And this table has like 2.2 million rows in it.

table has like 2.2 million rows in it. It's a lot bigger. Anyway, our current

It's a lot bigger. Anyway, our current file for 3.4 for advanced

file for 3.4 for advanced transformations that I'm working right

transformations that I'm working right now is 39 megabytes or 39,000 kilobytes.

now is 39 megabytes or 39,000 kilobytes. Let's see how big it gets after we save

Let's see how big it gets after we save the file. So, first I'm going to close

the file. So, first I'm going to close and load it. And now it's loaded in. As

and load it. And now it's loaded in. As we can see here, I'm going to go ahead

we can see here, I'm going to go ahead and now save this file. Navigating back

and now save this file. Navigating back to here. This bad boy went from 39

to here. This bad boy went from 39 megabytes to 76 megabytes or 76,000

megabytes to 76 megabytes or 76,000 kilobytes. It basically doubled in size.

kilobytes. It basically doubled in size. So something to think about whenever

So something to think about whenever you're building these type of files. It

you're building these type of files. It is going to make your file size a lot

is going to make your file size a lot bigger, make it even harder to share.

bigger, make it even harder to share. Anyway, one thing that's going on here

Anyway, one thing that's going on here which is really weird and I don't want

which is really weird and I don't want it to do is this job postings flat table

it to do is this job postings flat table has relationships with the job ID of the

has relationships with the job ID of the job posting fact and then it has this

job posting fact and then it has this secondary one with the company ID of

secondary one with the company ID of this company dim. This dotted line means

this company dim. This dotted line means that it's not the primary relationship.

that it's not the primary relationship. Anyway, I don't want any of these. I'm

Anyway, I don't want any of these. I'm going to delete both these relationships

going to delete both these relationships in here because we don't want that. I'm

in here because we don't want that. I'm going to create a new page called merge.

going to create a new page called merge. And we're just going to demo real quick.

And we're just going to demo real quick. Make sure we have the correct data in

Make sure we have the correct data in here. Specifically, what are the top

here. Specifically, what are the top skills and data? I'm going to uh copy

skills and data? I'm going to uh copy that. And I'm going to paste that in

that. And I'm going to paste that in here. And I put some text boxes in here

here. And I put some text boxes in here just to keep track. Right. This side

just to keep track. Right. This side over here, we're going to be analyzing

over here, we're going to be analyzing the star schema data that we use from

the star schema data that we use from the job postings fact table and the

the job postings fact table and the skills down. We now want to see what our

skills down. We now want to see what our data looks like in job skills flat. So

data looks like in job skills flat. So I'll remove both the Y and the X- axis.

I'll remove both the Y and the X- axis. Navigate into job skills flat. For the

Navigate into job skills flat. For the Y-axis, we'll drive drop in skills. And

Y-axis, we'll drive drop in skills. And for the X-axis, we're going to do a

for the X-axis, we're going to do a count. We want to do a count of the job

count. We want to do a count of the job IDs. So I need to change this over to

IDs. So I need to change this over to count. Okay. The purpose of this was to

count. Okay. The purpose of this was to well verify. Is it the same? So skills

well verify. Is it the same? So skills are at 244,416

are at 244,416 for Python and 244,416.

for Python and 244,416. So this flat table is good. The only

So this flat table is good. The only thing we have to remember, right, is

thing we have to remember, right, is navigating to this table view with job

navigating to this table view with job skills flat selected. There are, as you

skills flat selected. There are, as you can see, multiple different job IDs

can see, multiple different job IDs because some jobs have multiple

because some jobs have multiple different skills. So whoever we give

different skills. So whoever we give this data set to, they have to be sure

this data set to, they have to be sure that they know how they're analyzing it.

that they know how they're analyzing it. Now, what is if we need to send this to

Now, what is if we need to send this to our boss? Well, the easiest way is

our boss? Well, the easiest way is inside of this table view underneath

inside of this table view underneath data. We can write uh we can select

data. We can write uh we can select those three dots. And from there, you're

those three dots. And from there, you're going to select copy table. And in my

going to select copy table. And in my case, it took a couple minutes to copy

case, it took a couple minutes to copy it into the clipboard. Now, I'm going to

it into the clipboard. Now, I'm going to go ahead and just paste it right here

go ahead and just paste it right here into a blank Excel spreadsheet. And

into a blank Excel spreadsheet. And silly me, it says the data set's too

silly me, it says the data set's too large, right? Cuz we had 2.2 million

large, right? Cuz we had 2.2 million rows of data. There's only one million

rows of data. There's only one million about 1 million rows in Excel. So, not

about 1 million rows in Excel. So, not all of it's going to go into here, but

all of it's going to go into here, but you get the idea. Scroll on down. Almost

you get the idea. Scroll on down. Almost a million rows of data inside of here.

All right, so moving into our second and really bonus round of using group eye.

really bonus round of using group eye. Let's say we get back from our boss that

Let's say we get back from our boss that hey, he didn't like that you had to send

hey, he didn't like that you had to send him multiple different Excel files to

him multiple different Excel files to get those 2.2 million job postings with

get those 2.2 million job postings with all those skills. instead he just wants

all those skills. instead he just wants you to aggregate it all together and

you to aggregate it all together and then give him those results. That way

then give him those results. That way the table's much smaller. Well, we can

the table's much smaller. Well, we can use group by for this. So, let's

use group by for this. So, let's navigate back into the power query

navigate back into the power query editor. And we don't necessarily need to

editor. And we don't necessarily need to load this job skills flat table anymore

load this job skills flat table anymore because like I said, my boss doesn't

because like I said, my boss doesn't really want it. So, we're going to

really want it. So, we're going to uncheck enable load. It'll say, hey,

uncheck enable load. It'll say, hey, there's potential possible data loss

there's potential possible data loss warning. Not worried about that. It

warning. Not worried about that. It ain't going to happen. But seriously,

ain't going to happen. But seriously, it's not. And we want to end up using

it's not. And we want to end up using this table to get an aggregation of

this table to get an aggregation of things. We're going to end using under

things. We're going to end using under the transform tab group by. I could

the transform tab group by. I could start by doing a group by here, but this

start by doing a group by here, but this is going to do it within the current

is going to do it within the current query. I don't want to do that. So what

query. I don't want to do that. So what we're going to do is we're going to

we're going to do is we're going to create a new not duplicate, but

create a new not duplicate, but reference query. Now, with this new

reference query. Now, with this new query that I've renamed to job skills

query that I've renamed to job skills group by, we're going to perform group

group by, we're going to perform group by. And we're going to keep it simple at

by. And we're going to keep it simple at first. I'm going to just we want to look

first. I'm going to just we want to look at specifically that skills column. And

at specifically that skills column. And with this, we just want a skill count.

with this, we just want a skill count. And so, this is going to count the rows.

And so, this is going to count the rows. And we don't select the column because

And we don't select the column because we're just counting the rows. We'll go

we're just counting the rows. We'll go ahead and click okay. And we can

ahead and click okay. And we can basically see that the data was now

basically see that the data was now group by or if you will pivoted in that

group by or if you will pivoted in that we're getting skill counts for all the

we're getting skill counts for all the different skills that we have. Now we're

different skills that we have. Now we're not limited just doing one column. I can

not limited just doing one column. I can go back into this group rows and I

go back into this group rows and I actually know that he wants more

actually know that he wants more information more than just the skills

information more than just the skills specifically. He's curious of breaking

specifically. He's curious of breaking down the count of skills based on not

down the count of skills based on not only the skills but also on something

only the skills but also on something like the job title short column. And we

like the job title short column. And we can still do a skill count for this. I'm

can still do a skill count for this. I'm going go ahead and click okay. And now

going go ahead and click okay. And now it's breaking down by skills and that

it's breaking down by skills and that job title short column. So this is going

job title short column. So this is going to be the final table we're export. But

to be the final table we're export. But I want to show and demonstrate one thing

I want to show and demonstrate one thing specifically. If we were to go back into

specifically. If we were to go back into that group rows, we have to be very

that group rows, we have to be very careful about how we're grouping and

careful about how we're grouping and what we're aggregating by. Specifically,

what we're aggregating by. Specifically, let's say we were trying to do the

let's say we were trying to do the median value of something like the

median value of something like the yearly salary. And we'll say this is a

yearly salary. And we'll say this is a median salary. So this can be done and

median salary. So this can be done and this is like I said showing that median

this is like I said showing that median salary. But what my boss is going to

salary. But what my boss is going to take and do is he's going to end up

take and do is he's going to end up trying to most likely find out what is

trying to most likely find out what is overall SQL. What is the median salary

overall SQL. What is the median salary overall? And then you'd be taking a

overall? And then you'd be taking a median of this median salary column. So

median of this median salary column. So whenever you start getting to other

whenever you start getting to other aggregations with group eyes, I don't

aggregations with group eyes, I don't recommend it because you're not going to

recommend it because you're not going to get the actual results unless you look

get the actual results unless you look at the median for the entire data set.

at the median for the entire data set. And this can be applied similarly if you

And this can be applied similarly if you were doing like an average salary and

were doing like an average salary and then try to take an average of an

then try to take an average of an average. It's not going to work out to

average. It's not going to work out to what you want. Long story short, if

what you want. Long story short, if you're doing any type of aggregation or

you're doing any type of aggregation or group by, I would leave it to things

group by, I would leave it to things like count and also count distinct rows.

like count and also count distinct rows. Besides that, that's about it. So now we

Besides that, that's about it. So now we have the final table that we want. I

have the final table that we want. I have it loading in. We disenbled the

have it loading in. We disenbled the load a job skills flat. I'm going to go

load a job skills flat. I'm going to go in and close and apply this. We can see

in and close and apply this. We can see now that that job skills flat table's

now that that job skills flat table's gone and we only have that job skills

gone and we only have that job skills group by. And let's just see real quick.

group by. And let's just see real quick. Remember we haven't saved just yet. So

Remember we haven't saved just yet. So this file size is 76 megabytes as we're

this file size is 76 megabytes as we're shown right here. Whenever I actually

shown right here. Whenever I actually save this now with only the group by and

save this now with only the group by and removing that big old flat table, we get

removing that big old flat table, we get back down to basically 39 megabytes,

back down to basically 39 megabytes, which much smaller. Group eyes is a lot

which much smaller. Group eyes is a lot better. So once again, let's make sure

better. So once again, let's make sure that we're getting the correct results

that we're getting the correct results for our group by. And for this, we're

for our group by. And for this, we're going to be once again comparing it to

going to be once again comparing it to that star schema data. So I can go into

that star schema data. So I can go into that skill stat, copy this bad boy, and

that skill stat, copy this bad boy, and then paste it right into here. I'm going

then paste it right into here. I'm going to copy and also drag it over here.

to copy and also drag it over here. Remove the values inside of it. Add in

Remove the values inside of it. Add in skills to the y-axis and skill count to

skills to the y-axis and skill count to the x-axis and are we going to get the

the x-axis and are we going to get the same results? Python 244,416

same results? Python 244,416 Python 2444,46416.

Python 2444,46416. Now, what's also neat about this is

Now, what's also neat about this is because we kept that job title short in

because we kept that job title short in there. I can then even drag job title

there. I can then even drag job title short into the small multitudes and it's

short into the small multitudes and it's showing a 4x4 grid or a 2 x two grid.

showing a 4x4 grid or a 2 x two grid. I'm not really liking this. Under small

I'm not really liking this. Under small multitudes under format visual, I can go

multitudes under format visual, I can go to the number of columns and change it

to the number of columns and change it to one. And then for this visual, I just

to one. And then for this visual, I just want to have a filter on the skills

want to have a filter on the skills itself to only just show the top five

itself to only just show the top five filter or top five skills. We'll say top

filter or top five skills. We'll say top five. And I'll drag skill count into

five. And I'll drag skill count into there to do sum of skill count. All

there to do sum of skill count. All right. Now, boom. Top five skills are

right. Now, boom. Top five skills are AWS, Azure, Python, SQL, and Tableau.

AWS, Azure, Python, SQL, and Tableau. And we can actually scroll through and

And we can actually scroll through and see it for all the other different job

see it for all the other different job titles as well. One quick note, because

titles as well. One quick note, because we deleted that job skills flat table,

we deleted that job skills flat table, this visualization that we built

this visualization that we built previously is not going to work for the

previously is not going to work for the flat table. So, we're going to have to

flat table. So, we're going to have to go ahead and just delete this page. All

go ahead and just delete this page. All right, you now have some practice

right, you now have some practice problems to go through and get more

problems to go through and get more familiar with using appends, merges, and

familiar with using appends, merges, and also group buys. After that, we'll be

also group buys. After that, we'll be jumping into our last lesson to do a

jumping into our last lesson to do a deeper dive on the M language, which is

deeper dive on the M language, which is what is powering Power Query. With that,

what is powering Power Query. With that, I'll see you in the next one.

All right, this is the last lesson on Power Query and we're jumping into a

Power Query and we're jumping into a topic on the M language. Little sad that

topic on the M language. Little sad that is the last one on Power Query cuz Power

is the last one on Power Query cuz Power Quer is one of my favorite topics.

Quer is one of my favorite topics. Anyway, for this M language, don't be

Anyway, for this M language, don't be int uh intimidated because we're not

int uh intimidated because we're not going to be uh like programming experts

going to be uh like programming experts by the end of this. Instead, I just want

by the end of this. Instead, I just want you to have a basic understanding of

you to have a basic understanding of what is the purpose of this language

what is the purpose of this language that powers Power Query. And also, by

that powers Power Query. And also, by the end of this, I'm going to give you

the end of this, I'm going to give you some techniques with AI bots like

some techniques with AI bots like ChachiBT in order to help you out if you

ChachiBT in order to help you out if you ever find yourself nearing needing to

ever find yourself nearing needing to modify an M language query.

So what the heck is M language? Well, M language is Power Query's query language

language is Power Query's query language for basically data manipulation and

for basically data manipulation and transformation. It's what's under the

transformation. It's what's under the hood in the Power Query editor that's

hood in the Power Query editor that's actually doing all of our different ETL

actually doing all of our different ETL process or extract, transform, load.

process or extract, transform, load. Now, it's important to understand we can

Now, it's important to understand we can interact with the guey of the Power

interact with the guey of the Power Query editor and it just generates the M

Query editor and it just generates the M language itself. We don't necessarily

language itself. We don't necessarily need to actually do the M language or do

need to actually do the M language or do the M language. We don't need

the M language. We don't need necessarily type out the M language. And

necessarily type out the M language. And as we demonstrated, right? So in the

as we demonstrated, right? So in the case of our job postings fact table that

case of our job postings fact table that we've been manipulating through this,

we've been manipulating through this, right? We've gone through and applied in

right? We've gone through and applied in this case 1 2 3 4 five different steps.

this case 1 2 3 4 five different steps. And we can see each of these steps. One,

And we can see each of these steps. One, we can see it inside of the formula bar

we can see it inside of the formula bar itself. So in this case, this change

itself. So in this case, this change type step, it includes this entire step

type step, it includes this entire step right here. Or we can even see all of

right here. Or we can even see all of the steps inside the advanced editor.

the steps inside the advanced editor. And looking at this change type, this

And looking at this change type, this entire thing, this whole formula right

entire thing, this whole formula right here is the same thing that was in the

here is the same thing that was in the formula bar on this single row. And we

formula bar on this single row. And we can see 1 2 3 4 five different rows in

can see 1 2 3 4 five different rows in here for the five different steps. At

here for the five different steps. At the end of this video, we're going to be

the end of this video, we're going to be demonstrating how to actually move like

demonstrating how to actually move like query like this into another query. Say

query like this into another query. Say we have a new PowerBI file we're

we have a new PowerBI file we're starting with and we just want this

starting with and we just want this table alone. We're going to show you how

table alone. We're going to show you how to actually do this cuz I find it pretty

to actually do this cuz I find it pretty common, but we'll save that for later.

All right, for this lesson, we're going to be working in the same file as last

to be working in the same file as last time. Specifically, we're going to be

time. Specifically, we're going to be starting off from that 3.5 merge. You

starting off from that 3.5 merge. You can also just continue to work in that

can also just continue to work in that same PowerBI file. Doesn't really

same PowerBI file. Doesn't really matter. Anyway, here I have it loaded. I

matter. Anyway, here I have it loaded. I do want to clean it up because remember

do want to clean it up because remember in the last lesson we created this flat

in the last lesson we created this flat table and then also this group eye. We

table and then also this group eye. We don't need any of that and we don't need

don't need any of that and we don't need any of these pages associated with it.

any of these pages associated with it. First, I'm going get rid of those pages.

First, I'm going get rid of those pages. So I'm just going to get rid of this job

So I'm just going to get rid of this job skills flat verify that I named job

skills flat verify that I named job skills query verify that I named and

skills query verify that I named and then the group by analysis. From here in

then the group by analysis. From here in the home tab I'm going to navigate into

the home tab I'm going to navigate into transform data and we're going to get

transform data and we're going to get rid of both of these queries of the

rid of both of these queries of the group by by deleting it selecting delete

group by by deleting it selecting delete and then the flat table also of deleting

and then the flat table also of deleting that as well. So now let's get into one

that as well. So now let's get into one of my favorite features inside of Power

of my favorite features inside of Power Query and that's column from example.

Query and that's column from example. We're going to be doing an example.

We're going to be doing an example. We're going to be modifying our job

We're going to be modifying our job postings fact table even further. We're

postings fact table even further. We're going to start simple first. We're going

going to start simple first. We're going to navigate over here to the job posted

to navigate over here to the job posted date column which is actually

date column which is actually incorrectly labeled, right? Should be

incorrectly labeled, right? Should be job posted date time. So say we wanted

job posted date time. So say we wanted to extract the date out of this. You

to extract the date out of this. You already know you can use the transform

already know you can use the transform tab and you could extract out the date

tab and you could extract out the date using this method. We could also use add

using this method. We could also use add column to extract out the date. But the

column to extract out the date. But the main purpose of showing this is under

main purpose of showing this is under add column we also have this of column

add column we also have this of column from example which we're going to be

from example which we're going to be going over in this anyway we want to do

going over in this anyway we want to do this from the selection or in our case

this from the selection or in our case we want to extract out the date and so

we want to extract out the date and so what I can do is just start typing and

what I can do is just start typing and in this case I just put one and a bunch

in this case I just put one and a bunch of different options popped up even more

of different options popped up even more options I'll say than are available in

options I'll say than are available in the popup in the transform and the add

the popup in the transform and the add column tab for just date itself. So

column tab for just date itself. So that's why I recommend this because you

that's why I recommend this because you can get a lot more options out of this.

can get a lot more options out of this. Anyway, I just want to get the date

Anyway, I just want to get the date only. So I'm going to navigate and

only. So I'm going to navigate and select this one right here of 111 2024

select this one right here of 111 2024 and select it. And then whenever I press

and select it. And then whenever I press enter, everything else is going to

enter, everything else is going to autofill in. So what's happened is this

autofill in. So what's happened is this is a light gray because I didn't do an

is a light gray because I didn't do an an example here. And so I know that I

an example here. And so I know that I entered it here. We're going to have

entered it here. We're going to have other ones where we have to enter more

other ones where we have to enter more than one example. Anyway, the reason why

than one example. Anyway, the reason why the name of this lesson is M language is

the name of this lesson is M language is because, well, we need to inspect to

because, well, we need to inspect to make sure that we're doing this

make sure that we're doing this correctly. So, anytime you're doing

correctly. So, anytime you're doing this, you need to look up here under

this, you need to look up here under transform and make sure that this M

transform and make sure that this M language here makes sense of what we're

language here makes sense of what we're trying to do. You don't need to be an

trying to do. You don't need to be an expert at reading it, but we can

expert at reading it, but we can basically see that we're extracting the

basically see that we're extracting the date from job posted date. Okay, that

date from job posted date. Okay, that sounds about right. Sometimes this will

sounds about right. Sometimes this will give you bogus results that aren't what

give you bogus results that aren't what you want. and looking yep up here in

you want. and looking yep up here in this section is going to help you out.

this section is going to help you out. So, I'm good with this. I'm going to go

So, I'm good with this. I'm going to go ahead and click okay. I do want to

ahead and click okay. I do want to change the name of this, but I want it

change the name of this, but I want it to be job posted date. I can't name it

to be job posted date. I can't name it to the same thing. We've seen that

to the same thing. We've seen that before. It gives an error. So, I'm just

before. It gives an error. So, I'm just going to click okay. I want to now take

going to click okay. I want to now take this and I want it next to job posted

this and I want it next to job posted date. So, I'm just going to drag it over

date. So, I'm just going to drag it over here. Now, I'm going to rename this one

here. Now, I'm going to rename this one to job posted date time and then this

to job posted date time and then this one that's date to job posted date. All

one that's date to job posted date. All right, let's do another demo. Maybe this

right, let's do another demo. Maybe this will work out, maybe it won't. This one

will work out, maybe it won't. This one has to do with the job via column.

has to do with the job via column. Remember before we were doing replace

Remember before we were doing replace values to remove that via space. Let's

values to remove that via space. Let's see if we can do this from column from

see if we can do this from column from example. With job via selected, I'm

example. With job via selected, I'm going to go to column from example and

going to go to column from example and then from selection. I only wanted to

then from selection. I only wanted to use this column. Anyway, if I try to

use this column. Anyway, if I try to type in in this case voting reveal.com,

type in in this case voting reveal.com, pretty neat name, and press enter.

pretty neat name, and press enter. Actually surprised. It looks like it

Actually surprised. It looks like it actually did get it correct. Previously

actually did get it correct. Previously it wasn't getting this right and not too

it wasn't getting this right and not too bad. Anyway, inspecting the M language.

bad. Anyway, inspecting the M language. This is doing a transformation and what

This is doing a transformation and what it says is text after delimiter in job

it says is text after delimiter in job via it's looking for a space. So

via it's looking for a space. So basically it's looking for that space in

basically it's looking for that space in there and it's keeping all the values

there and it's keeping all the values after that. And so the case of BB

after that. And so the case of BB Singapore which is multiple values, it

Singapore which is multiple values, it just takes all of it after that first

just takes all of it after that first space. We'll say this is good enough for

space. We'll say this is good enough for our case. I'm going to go ahead and

our case. I'm going to go ahead and click okay. Now that I'm thinking about

click okay. Now that I'm thinking about it, this one I'm going to have to move

it, this one I'm going to have to move over and then also rename. So, we're not

over and then also rename. So, we're not going to end up using this one.

going to end up using this one. Actually, I'm going to go ahead and X

Actually, I'm going to go ahead and X out of that. Instead, I'm just going to

out of that. Instead, I'm just going to select job via, go to transform, replace

select job via, go to transform, replace values, and specifically replace the via

values, and specifically replace the via space with a blank value. And this

space with a blank value. And this cleans it up in only one step. A little

cleans it up in only one step. A little bit easier. All right. Next example I

bit easier. All right. Next example I want to get to. I want to actually be

want to get to. I want to actually be able to combine the salary year average

able to combine the salary year average column with an adjusted value for the

column with an adjusted value for the salary hour average column. Right now

salary hour average column. Right now there's all null values right here.

there's all null values right here. We're going to filter for the time being

We're going to filter for the time being removing blanks while we're doing this

removing blanks while we're doing this just to make it easier for ourel, but

just to make it easier for ourel, but I'm going to delete this step

I'm going to delete this step afterwards. Hope I don't forget. Anyway,

afterwards. Hope I don't forget. Anyway, we can't just combine these columns

we can't just combine these columns because like this case, this is 25 and

because like this case, this is 25 and this is 120,000. we need to adjust this

this is 120,000. we need to adjust this 25 uh first. Now, unfortunately, we're

25 uh first. Now, unfortunately, we're not going to be able to do column from

not going to be able to do column from example for this. We want to multiply

example for this. We want to multiply the salary hour average by a value. So,

the salary hour average by a value. So, that's why thankfully we're smart

that's why thankfully we're smart enough. We've done this already. We can

enough. We've done this already. We can just add a column using multiply.

just add a column using multiply. Specifically, remember we want to

Specifically, remember we want to multiply times 52 weeks in a year times

multiply times 52 weeks in a year times 40 hours in a week, which is 2080. I'm

40 hours in a week, which is 2080. I'm going to click okay. And then for this

going to click okay. And then for this one, right, we don't want to keep the

one, right, we don't want to keep the name multiplication. So inside the

name multiplication. So inside the formula bar, right, we're going to

formula bar, right, we're going to change this name of multiplication to

change this name of multiplication to salary hour adjusted. Clicking enter, we

salary hour adjusted. Clicking enter, we can see that it got updated there. Okay.

can see that it got updated there. Okay. But now that we actually have this

But now that we actually have this column, let's now use as we can see that

column, let's now use as we can see that 25 is transferred into 52,000. We can

25 is transferred into 52,000. We can now combine these two columns because if

now combine these two columns because if you will, they're the same value of the

you will, they're the same value of the same magnitude. Now, so for this one, we

same magnitude. Now, so for this one, we want to have multiple column selections.

want to have multiple column selections. So, holding control, I do salary year

So, holding control, I do salary year average and salary hour average. And I

average and salary hour average. And I come up here to column from example and

come up here to column from example and I select from selection. So, what I'm

I select from selection. So, what I'm going to do is just enter in for this

going to do is just enter in for this one, I want to enter in the 52,000.

one, I want to enter in the 52,000. Pressing enter. It looks like it's

Pressing enter. It looks like it's saying, oh, hey, we want to copy these.

saying, oh, hey, we want to copy these. Oh, but we don't want null for these

Oh, but we don't want null for these values. So, instead, what I'll do is

values. So, instead, what I'll do is I'll click inside of here and I'll start

I'll click inside of here and I'll start entering 120,000. Press enter. See if it

entering 120,000. Press enter. See if it can figure it out. Oh, okay. It did

can figure it out. Oh, okay. It did figure it out. Looking at the M language

figure it out. Looking at the M language for this, we can see that it says, "Hey,

for this, we can see that it says, "Hey, we're just going to combine the text,

we're just going to combine the text, the text from the salary, year average

the text from the salary, year average column and the text from the salary hour

column and the text from the salary hour average column." Notice though that it

average column." Notice though that it is using a text function, which we're

is using a text function, which we're going to have to deal with. Anyway, I'm

going to have to deal with. Anyway, I'm going to go ahead and first change the

going to go ahead and first change the name of this to salary year and hour.

name of this to salary year and hour. Press enter and then press okay. Now,

Press enter and then press okay. Now, like I said, look at these different

like I said, look at these different values. These are italics and to the

values. These are italics and to the right. So I know these are actual number

right. So I know these are actual number values. However, this is to the left

values. However, this is to the left hand side because it converted it to a

hand side because it converted it to a text and I can confirm this underneath

text and I can confirm this underneath the home tab because it says that it's

the home tab because it says that it's text. In our case, we want it to be

text. In our case, we want it to be standard. So we're just going to

standard. So we're just going to transfer it to a decimal number. So what

transfer it to a decimal number. So what I'm going to do is going to close and

I'm going to do is going to close and apply this in a new page called salary

apply this in a new page called salary stats. Let's inspect this new column

stats. Let's inspect this new column that we created. I'm going to draw draw

that we created. I'm going to draw draw in a stack bar chart for this. And from

in a stack bar chart for this. And from that job postings fact table for this

that job postings fact table for this we're going to drag in job title short

we're going to drag in job title short into the y-axis and then our newly

into the y-axis and then our newly created column. Let me expand this over

created column. Let me expand this over salary year and hour aggregating this by

salary year and hour aggregating this by median. Now we can also see in the tool

median. Now we can also see in the tool tips what is the count of all these. So

tips what is the count of all these. So I'm going to drag this into here and

I'm going to drag this into here and select this to count. So with this

select this to count. So with this making this into focus mode, we can now

making this into focus mode, we can now see we're having even more values based

see we're having even more values based on this count and we have more inputs

on this count and we have more inputs into what these different salaries are.

into what these different salaries are. Not just based on only the yearly

Not just based on only the yearly salary, but also the hourly salary. Now,

salary, but also the hourly salary. Now, one more example to use for this column

one more example to use for this column from example. We're going to go back

from example. We're going to go back into transform data. And I did forget a

into transform data. And I did forget a step if you can't remember. It didn't

step if you can't remember. It didn't affect that last visualization, but

affect that last visualization, but remember we did do this filtered rows to

remember we did do this filtered rows to remove those values that didn't have or

remove those values that didn't have or had null values for the salary. We we do

had null values for the salary. We we do want to remove that step. So, I'm going

want to remove that step. So, I'm going to go ahead and delete that. All right.

to go ahead and delete that. All right. And then go move to the last step. All

And then go move to the last step. All right. There's last one last thing I

right. There's last one last thing I want to do and it deals with the salary

want to do and it deals with the salary hour average column. Once again, uh no

hour average column. Once again, uh no uh but I'm only going to filter this

uh but I'm only going to filter this again, but I only want to filter it for

again, but I only want to filter it for hours. So, we can actually see all the

hours. So, we can actually see all the things in here. Once again, this is

things in here. Once again, this is something we'll need to remove with this

something we'll need to remove with this salary hour average column. I want to be

salary hour average column. I want to be able to bucket data. What do I mean by

able to bucket data. What do I mean by that? Well, let's select salary hour

that? Well, let's select salary hour average. Go into add column and do

average. Go into add column and do column from example. I want to put these

column from example. I want to put these salaries into $10 increments. So

salaries into $10 increments. So something like 25. I want it to be 2230.

something like 25. I want it to be 2230. I'm going to press enter. And we can see

I'm going to press enter. And we can see that it filled it in. So in this case,

that it filled it in. So in this case, just going down to make sure we have it.

just going down to make sure we have it. So this case 68.24, it does a bucket of

So this case 68.24, it does a bucket of 60 to 70. Pretty cool. And looking at

60 to 70. Pretty cool. And looking at the M language for this, it actually

the M language for this, it actually goes off screen. It does some sort of

goes off screen. It does some sort of offset and then basically increments by

offset and then basically increments by tens based on the value. Overall,

tens based on the value. Overall, inspecting these different values, it is

inspecting these different values, it is working correctly. So I'm okay with it.

working correctly. So I'm okay with it. I am going to change this name of this

I am going to change this name of this from range to salary hour bucket and

from range to salary hour bucket and then I'm going to click okay. All right.

then I'm going to click okay. All right. So now we have this new one. Once again

So now we have this new one. Once again remember we did filter for just the

remember we did filter for just the hourly data only. I don't want to keep

hourly data only. I don't want to keep that or this step of filtering. So I'm

that or this step of filtering. So I'm going to remove it. All the other steps

going to remove it. All the other steps will update for this. And then I can go

will update for this. And then I can go to home and close it and load it in. And

to home and close it and load it in. And now with this one, I can come in here,

now with this one, I can come in here, insert in a stacked column chart, drag

insert in a stacked column chart, drag salary bucket into the x-axis, and then

salary bucket into the x-axis, and then we want to count that as well. So I'll

we want to count that as well. So I'll drag it into the count of here. And

drag it into the count of here. And entering into focus mode, we can see

entering into focus mode, we can see that some of the most popular salaries

that some of the most popular salaries are between 20 to 30, 67, 40, 50, 50,

are between 20 to 30, 67, 40, 50, 50, and whatnot. So some pretty interesting

and whatnot. So some pretty interesting insights out of bucketing this together.

insights out of bucketing this together. And it basically creates what is known

And it basically creates what is known as like a histogram. Now, you may be

as like a histogram. Now, you may be like, Luke, looking at this, this is out

like, Luke, looking at this, this is out of order because 0 to 10 is all the way

of order because 0 to 10 is all the way back here. I mean, yeah, it is out of

back here. I mean, yeah, it is out of order. The easiest way to fix this,

order. The easiest way to fix this, unfortunately, is using DAX, which

unfortunately, is using DAX, which conveniently is coming up in the next

conveniently is coming up in the next chapter. And so, we will talk about how

chapter. And so, we will talk about how to tackle this problem of sorting it

to tackle this problem of sorting it using that X-axis in this case.

using that X-axis in this case. Now, let's crank up a notch and let's

Now, let's crank up a notch and let's jump into custom columns. With this,

jump into custom columns. With this, we're going to be actually creating or

we're going to be actually creating or writing M language, if you will, in

writing M language, if you will, in order to build these custom columns. For

order to build these custom columns. For this, I wanted to since it's more

this, I wanted to since it's more advanced, I want to keep it familiar

advanced, I want to keep it familiar with what you've done already. So, we're

with what you've done already. So, we're going to be recreating how we created

going to be recreating how we created this salary hour adjusted column and the

this salary hour adjusted column and the salary year and hour column. So, let's

salary year and hour column. So, let's start with the salary hour adjusted

start with the salary hour adjusted first because that one's frankly the

first because that one's frankly the easiest. We're going to go into add

easiest. We're going to go into add column and we're going to select custom

column and we're going to select custom column. In this case, a new pop-up

column. In this case, a new pop-up window comes up called custom column.

window comes up called custom column. You can name the column. So remember,

You can name the column. So remember, we're doing salary, hour, adjusted, and

we're doing salary, hour, adjusted, and I'm going to name this V1. Then

I'm going to name this V1. Then underneath here, we have our custom

underneath here, we have our custom column formula. And we have all our

column formula. And we have all our available columns on the right hand

available columns on the right hand side. So I can take something like

side. So I can take something like salary, hour, average, and insert that

salary, hour, average, and insert that in over here. In this case, if I were to

in over here. In this case, if I were to click okay and load this in, it's going

click okay and load this in, it's going to then load in those values. As I can

to then load in those values. As I can see, 25 here, 25 over here in that

see, 25 here, 25 over here in that salary hour average column. So, that's

salary hour average column. So, that's just a simple way. But navigating back

just a simple way. But navigating back into it by clicking that settings icon,

into it by clicking that settings icon, we can actually do manipulations with

we can actually do manipulations with this. We're not going to do anything

this. We're not going to do anything advanced. Remember before we did

advanced. Remember before we did multiplication, we did 2080. Well, with

multiplication, we did 2080. Well, with 2080, right, is 52 weeks in a year times

2080, right, is 52 weeks in a year times 40 hours in a week. So, whenever I do

40 hours in a week. So, whenever I do this for salary hour adjusted V1, click

this for salary hour adjusted V1, click okay. It now updates from that 25 to

okay. It now updates from that 25 to 52,000, which we can see from this other

52,000, which we can see from this other column that we created already. That is

column that we created already. That is correct. All right. Next task. We want

correct. All right. Next task. We want to recreate this salary, year, and hour

to recreate this salary, year, and hour column. Once again, we're going to go

column. Once again, we're going to go and select custom column and call this

and select custom column and call this salary year and our V1. Now, for this

salary year and our V1. Now, for this one, we need an if statement. Basically,

one, we need an if statement. Basically, we're going to write an if statement and

we're going to write an if statement and then have things below it. I'll be

then have things below it. I'll be honest, I don't have the M language

honest, I don't have the M language memorized for that, and I don't think

memorized for that, and I don't think you need to necessarily, too. Instead,

you need to necessarily, too. Instead, I'd recommend using your favorite

I'd recommend using your favorite chatbot, ChatGBT, or even Google Gemini.

chatbot, ChatGBT, or even Google Gemini. In it, I'm going to provide this prompt.

In it, I'm going to provide this prompt. Give me the custom column code for Power

Give me the custom column code for Power Query 2. Combine the salary year average

Query 2. Combine the salary year average column and salary hour adjusted column.

column and salary hour adjusted column. There's either a value in either one or

There's either a value in either one or the other column. Go ahead and click

the other column. Go ahead and click enter. It gives me this bad boy, which

enter. It gives me this bad boy, which looks pretty simple. I'm going to go

looks pretty simple. I'm going to go ahead and copy this. So, I can just

ahead and copy this. So, I can just select copy. I'm going to go ahead and

select copy. I'm going to go ahead and paste it. I press Ctrl +V. Notice there

paste it. I press Ctrl +V. Notice there are two question marks or sorry, two

are two question marks or sorry, two equal signs. I'm going to remove this.

equal signs. I'm going to remove this. Everything looks like it's okay. It says

Everything looks like it's okay. It says no syntax errors have been detected.

no syntax errors have been detected. I'll click okay. And scrolling here

I'll click okay. And scrolling here through here, we can see that it worked.

through here, we can see that it worked. We have 52,000 here for salary hour

We have 52,000 here for salary hour average. And then for this row that has

average. And then for this row that has salary year average in it, we have a

salary year average in it, we have a value for it. So it's working out. Now,

value for it. So it's working out. Now, if you're not comfortable using the M

if you're not comfortable using the M language for this or using CHBT for it,

language for this or using CHBT for it, no big deal. If we actually inspect this

no big deal. If we actually inspect this current step right here and go into it,

current step right here and go into it, custom code does not actually pop up. It

custom code does not actually pop up. It actually directs us to this of adding a

actually directs us to this of adding a conditional column which we've gone

conditional column which we've gone through and demonstrated before and you

through and demonstrated before and you could have built it similarly using this

could have built it similarly using this instead. I just wanted to demonstrate

instead. I just wanted to demonstrate how to actually use them language.

So now what happens if we want to create a completely new report but I don't want

a completely new report but I don't want frankly I don't want all of these

frankly I don't want all of these different queries in here and I don't

different queries in here and I don't want all these different pages in here.

want all these different pages in here. So, we just want to create a new report

So, we just want to create a new report and specifically I want this job

and specifically I want this job postings fact query as we demonstrate.

postings fact query as we demonstrate. We can go into advanced editor and we

We can go into advanced editor and we can get all this code right here. Well,

can get all this code right here. Well, that's what we're going to use for this.

that's what we're going to use for this. So, let's first start by creating a new

So, let's first start by creating a new report. So, I'm going to start a blank

report. So, I'm going to start a blank report. And in this, I'm going to just

report. And in this, I'm going to just go into transform data. Transform data.

go into transform data. Transform data. And we're going to say this is from a

And we're going to say this is from a new source. Specifically, this is going

new source. Specifically, this is going to be a blank query. We're going to

to be a blank query. We're going to rename this to what we want to move over

rename this to what we want to move over of job postings fact. All right, so

of job postings fact. All right, so let's try this. Navigating back into our

let's try this. Navigating back into our other file. I have the code here that I

other file. I have the code here that I want. I'm going to go ahead and select

want. I'm going to go ahead and select it all and I'm going to press Ctrl C.

it all and I'm going to press Ctrl C. Now, real quick before I paste it, just

Now, real quick before I paste it, just a reminder, right, these are all the

a reminder, right, these are all the different steps, right? We talked about

different steps, right? We talked about earlier. I'm going to go to done. These

earlier. I'm going to go to done. These are all the different steps that are in

are all the different steps that are in the applied step and they're all within

the applied step and they're all within this let statement right there. The last

this let statement right there. The last portion, this in is what's then output

portion, this in is what's then output and it's always basically this last step

and it's always basically this last step right here. So nothing really special

right here. So nothing really special with the syntax further that we need to

with the syntax further that we need to understand about it. Anyway, I did go

understand about it. Anyway, I did go through and actually copy it. Crl + C.

through and actually copy it. Crl + C. Now in inside of our new report going

Now in inside of our new report going into the advanced editor for this one,

into the advanced editor for this one, right? We still have that let there's

right? We still have that let there's nothing for the source and then that

nothing for the source and then that source is that last step. Anyway, I

source is that last step. Anyway, I don't really care about that. I'm going

don't really care about that. I'm going to delete it all and I'm going to paste

to delete it all and I'm going to paste in all of the different code in here.

in all of the different code in here. Now it does say here no syntax errors

Now it does say here no syntax errors have been detected. We are going to run

have been detected. We are going to run into an issue. We'll get to it. But I'm

into an issue. We'll get to it. But I'm going to go ahead and click done. Okay.

going to go ahead and click done. Okay. So in here I am on the last step and it

So in here I am on the last step and it says there's an expression error. the

says there's an expression error. the import star schema file matches no

import star schema file matches no exports. Did you miss a module

exports. Did you miss a module reference? I'm going to say hey go to

reference? I'm going to say hey go to that error and it's going to immediately

that error and it's going to immediately take me to that source step which

take me to that source step which basically says it is this and we're

basically says it is this and we're referencing remember we're referencing

referencing remember we're referencing the source step is referencing the star

the source step is referencing the star schema files query the one that we

schema files query the one that we created about three lessons ago but we

created about three lessons ago but we have no query of star schema files so

have no query of star schema files so let's navigate back to our original file

let's navigate back to our original file here I can see the star schema files is

here I can see the star schema files is right here we can inspect it with

right here we can inspect it with advanced editor and for this one, we're

advanced editor and for this one, we're going to take a little bit of a leap,

going to take a little bit of a leap, but this is the source and it tells us

but this is the source and it tells us the folder and the files that we need to

the folder and the files that we need to connect to. It's just one step. I'm

connect to. It's just one step. I'm going to go ahead and just copy the

going to go ahead and just copy the steps only for this portion and

steps only for this portion and navigating back to our new report. Going

navigating back to our new report. Going into the advanced editor, look at this.

into the advanced editor, look at this. This source up here is referencing that

This source up here is referencing that star schema files. Instead, what I'm

star schema files. Instead, what I'm going to do, I'm going to leave make

going to do, I'm going to leave make sure that that comma is not selected.

sure that that comma is not selected. I'm going to delete it to there. And I'm

I'm going to delete it to there. And I'm going to paste in source, which

going to paste in source, which navigates to the files or folder of this

navigates to the files or folder of this here. And I'm going to click done. And

here. And I'm going to click done. And voila, it loaded in. Now, let's actually

voila, it loaded in. Now, let's actually change this back to what it was, cuz

change this back to what it was, cuz most likely you're not going to know to

most likely you're not going to know to actually change this to this. What's

actually change this to this. What's going to happen is you're going to and

going to happen is you're going to and you're going to run this. This is

you're going to run this. This is probably be the scenario that you're in.

probably be the scenario that you're in. You're going to say, "Hey, go to error."

You're going to say, "Hey, go to error." And you're going to be like, "What the

And you're going to be like, "What the heck is going on here?" Well, this is

heck is going on here?" Well, this is what I recommend doing. or copy this

what I recommend doing. or copy this error message inside your favorite

error message inside your favorite chatbot. Paste this in. Go down and then

chatbot. Paste this in. Go down and then from there, get the actual code itself

from there, get the actual code itself so it knows what's going on. And then

so it knows what's going on. And then pasting that code in there. That's all

pasting that code in there. That's all I'm going to put in here. I'm going to

I'm going to put in here. I'm going to see what it can say. And interesting

see what it can say. And interesting enough, it says the fix for this is we

enough, it says the fix for this is we need to replace this line with the

need to replace this line with the correct folder files function.

correct folder files function. Specifically, this one right here. So,

Specifically, this one right here. So, I'm going to go ahead and copy it, paste

I'm going to go ahead and copy it, paste it into here, and then click done, and

it into here, and then click done, and bam, it got it. So, make sure you're

bam, it got it. So, make sure you're taking advantage of something like chatb

taking advantage of something like chatb and these chatbots anytime you need to

and these chatbots anytime you need to get into some coding, you're not

get into some coding, you're not comfortable with it. Now, one note on

comfortable with it. Now, one note on error troubleshooting. Some of you may

error troubleshooting. Some of you may have executed that previous query and

have executed that previous query and have gotten this error right here where

have gotten this error right here where it says the key didn't match rows in the

it says the key didn't match rows in the table and it specifies well one I'm

table and it specifies well one I'm going to click go to error and I know

going to click go to error and I know it's that second step of navigation

it's that second step of navigation basically says the key isn't right

basically says the key isn't right specifically this folder path most

specifically this folder path most likely this folder path is not correct

likely this folder path is not correct and I know that from troubleshooting

and I know that from troubleshooting this and being experienced with power

this and being experienced with power query but you may not. So, I'm just

query but you may not. So, I'm just going to copy this and then paste it

going to copy this and then paste it into chat GPT to see what it says. And

into chat GPT to see what it says. And it's saying, "Hey, you're getting this

it's saying, "Hey, you're getting this error because either the folder path is

error because either the folder path is incorrect or the file name is

incorrect or the file name is incorrect." So, it's hitting you towards

incorrect." So, it's hitting you towards it. Now, some others of you may be

it. Now, some others of you may be getting this error message. Once again,

getting this error message. Once again, I'll go to error. And this one's with

I'll go to error. And this one's with the second step as well, but this one's

the second step as well, but this one's different. It says, "Hey, data source

different. It says, "Hey, data source not found." It's actually queuing in

not found." It's actually queuing in more to what's wrong with this. And it

more to what's wrong with this. And it has this location here. Anyway, the

has this location here. Anyway, the problem is you probably don't have the

problem is you probably don't have the correct file path of where job postings

correct file path of where job postings fact.csv is. So all you have to do is

fact.csv is. So all you have to do is navigate to where it is in file

navigate to where it is in file explorer. Here I'm in the project folder

explorer. Here I'm in the project folder and navigate into data and then the star

and navigate into data and then the star schema folder. Up here I can just

schema folder. Up here I can just rightclick and say hey I want to copy

rightclick and say hey I want to copy this address. Then inside of chat GBT I

this address. Then inside of chat GBT I can say I found the issue. It was a file

can say I found the issue. It was a file path issue. The file is here. update my

path issue. The file is here. update my code and it update all the different

code and it update all the different code. It has it all here. All I'm going

code. It has it all here. All I'm going to do is just copy it, remove all this

to do is just copy it, remove all this old code in here, paste it in, cross my

old code in here, paste it in, cross my fingers, press done, and bam, it works.

fingers, press done, and bam, it works. So, the moral of the story is don't

So, the moral of the story is don't undervalue using chat GBT and helping

undervalue using chat GBT and helping you troubleshoot things like this. All

you troubleshoot things like this. All right, so that wraps up Power Query. You

right, so that wraps up Power Query. You now have some practice problems to go

now have some practice problems to go through to get more familiar with custom

through to get more familiar with custom columns and also column from example. In

columns and also column from example. In the next lesson, which is going to be

the next lesson, which is going to be the next chapter, we're going to be

the next chapter, we're going to be jumping into DAX. I'm super excited

jumping into DAX. I'm super excited about that. I'll see you there.

Welcome to this fourth and final chapter in this PowerBI course, and we're going

in this PowerBI course, and we're going to be covering DAX or data analysis

to be covering DAX or data analysis expressions. Now, we just got done with

expressions. Now, we just got done with the M language. So, it's very important

the M language. So, it's very important that we distinguish between the two. The

that we distinguish between the two. The M language, which is a more of a

M language, which is a more of a programming language, is used in Power

programming language, is used in Power Query in the process of actually loading

Query in the process of actually loading and transforming the data to get it into

and transforming the data to get it into PowerBI. Whereas, DAX data analysis

PowerBI. Whereas, DAX data analysis expressions is a formula language and

expressions is a formula language and it's used in the front end in PowerBI

it's used in the front end in PowerBI after the data is already loaded in. And

after the data is already loaded in. And in this video, we're going to have an

in this video, we're going to have an intro diving deeper into what exactly

intro diving deeper into what exactly DAX is, but also how we can use it in

DAX is, but also how we can use it in different use cases, specifically with

different use cases, specifically with calculated columns, calculated tables,

calculated columns, calculated tables, and even explicit measures. And then for

and even explicit measures. And then for the remaining two lessons, we're going

the remaining two lessons, we're going to dive deeper into measures, and then

to dive deeper into measures, and then also into parameters. But we're getting

also into parameters. But we're getting ahead of oursel, let's actually look

ahead of oursel, let's actually look into what exactly is DAX.

So what exactly is this formula language? Well, it's a method of

language? Well, it's a method of actually adding calculations to our data

actually adding calculations to our data model that we've loaded into PowerBI.

model that we've loaded into PowerBI. We're going to be focusing only on this

We're going to be focusing only on this tool, but you can actually use DAX and

tool, but you can actually use DAX and other tools as well like Microsoft

other tools as well like Microsoft Excel, Microsoft Fabric, SQL Service

Excel, Microsoft Fabric, SQL Service Analysis, and also Azure Analysis

Analysis, and also Azure Analysis Services. It's basically great at

Services. It's basically great at performing calculations on large sets of

performing calculations on large sets of data. It's super powerful. Now, anytime

data. It's super powerful. Now, anytime you're using some sort of formula

you're using some sort of formula language, I'm going to recommend that

language, I'm going to recommend that you go directly to the data source

you go directly to the data source anytime you have questions. So, I'll put

anytime you have questions. So, I'll put a link up on the screen and feel free to

a link up on the screen and feel free to keep this in a separate window to look

keep this in a separate window to look up any different functions from DAX you

up any different functions from DAX you want to learn more about. Anyway, it has

want to learn more about. Anyway, it has a host of different features. You could

a host of different features. You could use it for aggregation functions such as

use it for aggregation functions such as average, count, max, min, and sum. It

average, count, max, min, and sum. It has date and time functions. Some of

has date and time functions. Some of which we'll demonstrate in this and

which we'll demonstrate in this and actually building a calendar, but more

actually building a calendar, but more ones that you may be familiar with are

ones that you may be familiar with are things like extracting day, minute,

things like extracting day, minute, month. Then they even have things like

month. Then they even have things like logical functions that you can do if

logical functions that you can do if statements and or or. And then finally,

statements and or or. And then finally, other common one that I find myself

other common one that I find myself using is math and trig functions. Now,

using is math and trig functions. Now, if you have familiarity with Excel

if you have familiarity with Excel functions, DAX functions are very

functions, DAX functions are very similar, especially in their syntax they

similar, especially in their syntax they use. The one main thing to get around

use. The one main thing to get around between Excel and DAX is that Excel

between Excel and DAX is that Excel operates in a single cell. Whereas with

operates in a single cell. Whereas with DAX, we can run a calculation that can

DAX, we can run a calculation that can be run not only on a single row within a

be run not only on a single row within a cell, but also entire columns or even

cell, but also entire columns or even tables. I put together this table that

tables. I put together this table that goes through and compares all the

goes through and compares all the different functions. You can access it

different functions. You can access it inside of my course notes. Anyway, the

inside of my course notes. Anyway, the point of it is not to actually have you

point of it is not to actually have you memorize all these different things.

memorize all these different things. Instead, if we were to take a look up

Instead, if we were to take a look up here at the top, we can see that for

here at the top, we can see that for Excel, the sum function that you use

Excel, the sum function that you use here, it's very similar in DAX on sum.

here, it's very similar in DAX on sum. Just instead of an Excel like you'd

Just instead of an Excel like you'd insert in a cell or a range, here in

insert in a cell or a range, here in DAX, you're going to be inserting in

DAX, you're going to be inserting in probably a column name instead. Now I am

probably a column name instead. Now I am making the assumption with building this

making the assumption with building this DAX portion that you have some

DAX portion that you have some familiarity with writing formulas in

familiarity with writing formulas in Excel such as the ones listed here.

Excel such as the ones listed here. Nothing too complex but at least

Nothing too complex but at least understanding the concept of writing

understanding the concept of writing formulas. We're not going to necessarily

formulas. We're not going to necessarily go into the basics of writing formulas.

go into the basics of writing formulas. So unfortunately I'm assuming that you

So unfortunately I'm assuming that you have that kind of knowledge. Now I do

have that kind of knowledge. Now I do want to briefly call out that there is a

want to briefly call out that there is a difference between DAX and M language.

difference between DAX and M language. Like I spoke previously, DAX is a

Like I spoke previously, DAX is a formula language such as sum, average,

formula language such as sum, average, and whatnot. Where the M language is

and whatnot. Where the M language is more of a programming language. It's

more of a programming language. It's much more verbose. What you apply on the

much more verbose. What you apply on the other is not interchangeable with the

other is not interchangeable with the other like Excel functions were. Once

other like Excel functions were. Once again, I put together a table compare

again, I put together a table compare comparing DAX to the M language on

comparing DAX to the M language on certain things. And we can see that it's

certain things. And we can see that it's very much a different type of language

very much a different type of language used for M language. even something like

used for M language. even something like concatenate. Instead, we're going to be

concatenate. Instead, we're going to be using text combined. So, it's not even

using text combined. So, it's not even the same word. It's a different

the same word. It's a different structure. Once again, you don't need to

structure. Once again, you don't need to memorize this list. This is just more

memorize this list. This is just more meant for demonstration purposes. Now,

meant for demonstration purposes. Now, in this chapter, we're going to be

in this chapter, we're going to be focusing on four methods of applying or

focusing on four methods of applying or using DAX inside of PowerBI. And that's

using DAX inside of PowerBI. And that's with measures, specifically explicit

with measures, specifically explicit measures, calculated columns, calculated

measures, calculated columns, calculated tables, and also parameters. Parameters,

tables, and also parameters. Parameters, we're not going to get into an example

we're not going to get into an example of that until the third lesson. Now,

of that until the third lesson. Now, what we're going to be covering during

what we're going to be covering during this chapter is not exclusive of all the

this chapter is not exclusive of all the different DAX locations. You can

different DAX locations. You can actually use it in some other concepts

actually use it in some other concepts as well, such as rowle security, dynamic

as well, such as rowle security, dynamic format strings, and whatnot. Anyway, all

format strings, and whatnot. Anyway, all these concepts listed here are beyond

these concepts listed here are beyond the scope of this course. They're more

the scope of this course. They're more advanced concepts, but once you

advanced concepts, but once you understand the basics of DAX, you'll be

understand the basics of DAX, you'll be able to go in and apply it into these if

able to go in and apply it into these if you decide to learn any of these topics.

So, let's get into our first example with calculated columns. And for this

with calculated columns. And for this and all the examples in this, we're

and all the examples in this, we're going to be continue working from that

going to be continue working from that same file you were working in in the

same file you were working in in the last lesson on the with the M language.

last lesson on the with the M language. But if you didn't happen to keep it, you

But if you didn't happen to keep it, you can just navigate into the project file

can just navigate into the project file and just open up that 3.6 M language

and just open up that 3.6 M language PowerBI file. Inside of here, you should

PowerBI file. Inside of here, you should have that data model with our job

have that data model with our job postings fact table and then our

postings fact table and then our different dimensional tables as shown

different dimensional tables as shown here. Now, inside of PowerBI, we can

here. Now, inside of PowerBI, we can access our use DAX specifically from

access our use DAX specifically from this modeling tab. We're going to be

this modeling tab. We're going to be using all these different features of

using all these different features of new measures, new columns, and new

new measures, new columns, and new table. In this lesson, we'll be using

table. In this lesson, we'll be using new parameter in the third lesson of

new parameter in the third lesson of this. And like I said, rowle security is

this. And like I said, rowle security is beyond the scope of this course. We're

beyond the scope of this course. We're not going to be covering it for this,

not going to be covering it for this, but this is where you'd enter it in. So,

but this is where you'd enter it in. So, here's what I'm thinking for the

here's what I'm thinking for the calculated column. We're going to be

calculated column. We're going to be doing something similar to what we did

doing something similar to what we did in Power Query. Mainly just to

in Power Query. Mainly just to demonstrate how you can do both in each

demonstrate how you can do both in each inside of our job postings fact table.

inside of our job postings fact table. Previously in the last chapter, we use

Previously in the last chapter, we use Power Query to create the salary salary

Power Query to create the salary salary hour adjusted and also salary hour

hour adjusted and also salary hour adjusted V1 column. Both of them just

adjusted V1 column. Both of them just use different methods. Anyway, in this

use different methods. Anyway, in this one, we're going to recreate this column

one, we're going to recreate this column once again, but now using DAX. And then

once again, but now using DAX. And then we'll take it obviously a step further

we'll take it obviously a step further and also build the salary year and hour

and also build the salary year and hour column. But this one's going to be

column. But this one's going to be slightly more complex. Anyway, let's

slightly more complex. Anyway, let's create a new column in this data set. As

create a new column in this data set. As I mentioned, from the report view, you

I mentioned, from the report view, you can get it to it from modeling and

can get it to it from modeling and select new column. The one thing though

select new column. The one thing though is you have to make sure that the

is you have to make sure that the correct table is selected, right? We

correct table is selected, right? We wanted to do job postings fact table.

wanted to do job postings fact table. It's inserting it into company dim. Not

It's inserting it into company dim. Not what I want. I'm just going to press

what I want. I'm just going to press escape. It's going to undo it. So, if

escape. It's going to undo it. So, if you did it from here, make sure you

you did it from here, make sure you select job postings fact and then select

select job postings fact and then select new column to be inserted in here. The

new column to be inserted in here. The other method where you can get to it is

other method where you can get to it is in table view with the appropriate table

in table view with the appropriate table selected. You can see that it appears up

selected. You can see that it appears up at the top as well or you can just

at the top as well or you can just rightclick it and inside the table

rightclick it and inside the table itself select new column. And I like

itself select new column. And I like doing it here from here because it's

doing it here from here because it's showing me visibly what's happening in

showing me visibly what's happening in here. So this is my recommended way of

here. So this is my recommended way of doing it. Anyway, we want to make this

doing it. Anyway, we want to make this salary hour adjusted column. And we can

salary hour adjusted column. And we can see that we start by writing first the

see that we start by writing first the column name before the equal sign. And

column name before the equal sign. And we call the salary hour adjusted V2.

we call the salary hour adjusted V2. Now, as a reminder, we're going to be

Now, as a reminder, we're going to be taking that salary hour average column

taking that salary hour average column and multiplying it times 52 weeks in a

and multiplying it times 52 weeks in a year and 40 hours per week. So, what I

year and 40 hours per week. So, what I can start doing is just typing in this

can start doing is just typing in this column name. And you're noticing that we

column name. And you're noticing that we have this syntax here. It has the table

have this syntax here. It has the table name and then the column name. We want

name and then the column name. We want this one right here of job postings fact

this one right here of job postings fact salary hour average. It's highlighted

salary hour average. It's highlighted blue so I know it's working correctly.

blue so I know it's working correctly. We're just going to do that for the time

We're just going to do that for the time being. Press enter. Make sure that it

being. Press enter. Make sure that it loads in. Okay, we have those values in.

loads in. Okay, we have those values in. Now all we want to do is do some simple

Now all we want to do is do some simple multiplication. So I'll do a time symbol

multiplication. So I'll do a time symbol of 52 weeks in a year and then 40 hours

of 52 weeks in a year and then 40 hours in a week. Press enter and we get that

in a week. Press enter and we get that 520,000. I'm going to go ahead and just

520,000. I'm going to go ahead and just format it as currency. One thing to note

format it as currency. One thing to note over here in the data pane, we can see

over here in the data pane, we can see scrolling on down salary hour adjusted

scrolling on down salary hour adjusted V2 has this special symbol in front of

V2 has this special symbol in front of it symbolizing that it is a calculated

it symbolizing that it is a calculated column. So it cues you into that. Now

column. So it cues you into that. Now let's create this salary year and hour

let's create this salary year and hour V1. I'm going to go ahead and select new

V1. I'm going to go ahead and select new column. Give it the name salary year and

column. Give it the name salary year and hour V2. And now remember this one is

hour V2. And now remember this one is taking whether they have a value in that

taking whether they have a value in that salary year average column right here.

salary year average column right here. It's going to take either this value or

It's going to take either this value or if this is null it's going to end up

if this is null it's going to end up taking our salary hour adjusted. So this

taking our salary hour adjusted. So this value right here. Now I'll be honest

value right here. Now I'll be honest this is more of an advanced technique in

this is more of an advanced technique in order to combine these two columns. So I

order to combine these two columns. So I wouldn't expect you to know this off the

wouldn't expect you to know this off the top of your head. Instead, I'd recommend

top of your head. Instead, I'd recommend you use something like chatgbt and I

you use something like chatgbt and I provide a prompt like this. I'm using

provide a prompt like this. I'm using DAX for calculated columns. I want the

DAX for calculated columns. I want the value in it to be either the the salary

value in it to be either the the salary or average or the salary hour adjusted

or average or the salary hour adjusted V2. Make sure you give the full table

V2. Make sure you give the full table and column name to help it out. And it

and column name to help it out. And it says assume the other well column is

says assume the other well column is null. Go ahead and click enter. It gives

null. Go ahead and click enter. It gives me this formula which is an if formula.

me this formula which is an if formula. I'm going to go ahead and copy this.

I'm going to go ahead and copy this. Notice it has the column name of

Notice it has the column name of preferred salary. I don't want to copy

preferred salary. I don't want to copy that. That's why I didn't copy that.

that. That's why I didn't copy that. Anyway, inside of here, I'm going to go

Anyway, inside of here, I'm going to go ahead and paste that in. And apparently

ahead and paste that in. And apparently I did copy that, so I lied to you. But

I did copy that, so I lied to you. But notice that all of the different there's

notice that all of the different there's no errors or anything like that. I'm

no errors or anything like that. I'm going to go ahead and press enter. See

going to go ahead and press enter. See if it works. And bam, it does. Now, if

if it works. And bam, it does. Now, if you've taken like my Excel course, this

you've taken like my Excel course, this is probably looking very familiar to

is probably looking very familiar to what you've done in my Excel course for

what you've done in my Excel course for making if statements. Once again, we're

making if statements. Once again, we're not going to be going into a lot of this

not going to be going into a lot of this because I just want to stick to the

because I just want to stick to the basics and show you how you can use

basics and show you how you can use something like Chad GBT to help out.

something like Chad GBT to help out. Now, I want to pause real quick and

Now, I want to pause real quick and evaluate because we just showed how to

evaluate because we just showed how to create a calculated column and

create a calculated column and previously we were using custom columns

previously we were using custom columns inside of Power Queries. So, you may be

inside of Power Queries. So, you may be like, Luke, which one should I use? You

like, Luke, which one should I use? You just ran through that calculated columns

just ran through that calculated columns one and did some advanced calculations

one and did some advanced calculations and I have no idea how to do that. Well,

and I have no idea how to do that. Well, the good news is in most cases I'm going

the good news is in most cases I'm going to recommend don't use calculated

to recommend don't use calculated columns. Instead, use your vast

columns. Instead, use your vast knowledge you already have on custom

knowledge you already have on custom columns. Specifically, Power Query is

columns. Specifically, Power Query is much better at data transformations and

much better at data transformations and preparations, and it does this before

preparations, and it does this before the data is even loaded into the model.

the data is even loaded into the model. So, you don't have to do it in the front

So, you don't have to do it in the front end after all this is done. Also, I like

end after all this is done. Also, I like to keep all of my data cleaning in one

to keep all of my data cleaning in one spot, specifically in Power Query. And

spot, specifically in Power Query. And if I start doing in the front end adding

if I start doing in the front end adding these calculators and columns, it gets

these calculators and columns, it gets sort of out of control of understanding

sort of out of control of understanding what was my process to get the data

what was my process to get the data clean and makes it harder to replicate

clean and makes it harder to replicate later. The other thing to note is around

later. The other thing to note is around compression and file sizes. Whenever you

compression and file sizes. Whenever you do this in Power Query, this data is

do this in Power Query, this data is compressed more efficiently and your

compressed more efficiently and your file size is going to be much smaller

file size is going to be much smaller and therefore your data models are going

and therefore your data models are going to load and operate much more quickly.

to load and operate much more quickly. So, long story short, I just showed you

So, long story short, I just showed you calculated columns, so you understood

calculated columns, so you understood you could do them, but I'm going to

you could do them, but I'm going to recommend don't do them.

recommend don't do them. Moving on to the second or third item

Moving on to the second or third item we're going to cover this, and that's

we're going to cover this, and that's calculated tables. It's found under the

calculated tables. It's found under the button of new table. These type of

button of new table. These type of tables are great for creating things

tables are great for creating things like lookup tables, date tables, which

like lookup tables, date tables, which we're going to demonstrate, and also

we're going to demonstrate, and also transforming existing tables. Like I

transforming existing tables. Like I mentioned before, DAX very much not like

mentioned before, DAX very much not like Excel where it operates only a cell. DAX

Excel where it operates only a cell. DAX can operate on column names or a

can operate on column names or a complete table. Now, I will give this

complete table. Now, I will give this caveat to start with because you've

caveat to start with because you've already seen calculated columns. Just

already seen calculated columns. Just like calculated columns and custom

like calculated columns and custom columns, it's much more beneficial to do

columns, it's much more beneficial to do data cleaning and also creating tables

data cleaning and also creating tables in Power Query instead. But I'd be

in Power Query instead. But I'd be remiss if I didn't show you how to

remiss if I didn't show you how to actually do this with DAX in here

actually do this with DAX in here because I think it's a good learning

because I think it's a good learning opportunity. So let's insert a new

opportunity. So let's insert a new table. We can do that by going to the

table. We can do that by going to the table tools here and inserting new

table tools here and inserting new table. Also underneath the report view,

table. Also underneath the report view, we can go into modeling and select new

we can go into modeling and select new table here as well. I like that data

table here as well. I like that data view because I can keep track of what

view because I can keep track of what I'm doing. So I typically like to do it

I'm doing. So I typically like to do it from here and select new table. And it's

from here and select new table. And it's going to show me what I have below here.

going to show me what I have below here. Anyway, let's say for this we want to

Anyway, let's say for this we want to get a table from the job postings fact

get a table from the job postings fact table. Specifically, we want to get a

table. Specifically, we want to get a list of all the different job title

list of all the different job title shorts in here. Remember, we have 10

shorts in here. Remember, we have 10 unique values. This would be more of a

unique values. This would be more of a lookup table example that we're going to

lookup table example that we're going to do. So, I can go to table tools, insert

do. So, I can go to table tools, insert in that new table. For this, we'll call

in that new table. For this, we'll call it job title dim. And then for this,

it job title dim. And then for this, we're going to use a function called

we're going to use a function called distinct. distinct returns a one column

distinct. distinct returns a one column table that contains the distinct values

table that contains the distinct values in a column. So now all we need to do is

in a column. So now all we need to do is specify that column and it's this one

specify that column and it's this one here inside the job postings fact table.

here inside the job postings fact table. I need to make sure that I close off the

I need to make sure that I close off the parentheses and then press enter and

parentheses and then press enter and then bam it generates below. Also I can

then bam it generates below. Also I can see this table inside of here over here

see this table inside of here over here on job title dim. If you notice, this

on job title dim. If you notice, this has a little calculator in front of the

has a little calculator in front of the table icon to show that it's a

table icon to show that it's a calculated table and it shows the one

calculated table and it shows the one icon. Now, let's create a date table, a

icon. Now, let's create a date table, a date dimensional table. For this, we're

date dimensional table. For this, we're going to call this date dim. And we're

going to call this date dim. And we're going to use the calendar function. And

going to use the calendar function. And in this, it returns a table with one

in this, it returns a table with one column of all dates between the start

column of all dates between the start date and end date. I can see the syntax

date and end date. I can see the syntax up here. It's prompting me to put in the

up here. It's prompting me to put in the start date first and then the end date.

start date first and then the end date. Now, these dates do have to be in a

Now, these dates do have to be in a certain format. So, I'm going to use the

certain format. So, I'm going to use the date function specifying 2024 January

date function specifying 2024 January 1st for this. Then putting a comma, we

1st for this. Then putting a comma, we can now see we're in the end date. We're

can now see we're in the end date. We're going to do date as well. And we want to

going to do date as well. And we want to go to the 31st of December for that

go to the 31st of December for that year. And then we need to put a closing

year. And then we need to put a closing parenthesis on all that. Okay, I'm going

parenthesis on all that. Okay, I'm going to go ahead and press enter. All right,

to go ahead and press enter. All right, so pretty neat. This goes through and

so pretty neat. This goes through and creates a date automatically to the end.

creates a date automatically to the end. And I'm realizing now that I have a

And I'm realizing now that I have a typo. I put in 32. I didn't even know

typo. I put in 32. I didn't even know that was possible. Uh, going ahead and

that was possible. Uh, going ahead and run enter. It did go ahead and clean

run enter. It did go ahead and clean that up. That's why it's always good

that up. That's why it's always good that you inspect your data. Now, because

that you inspect your data. Now, because this is going to be we're actually we're

this is going to be we're actually we're going to keep this date dim for the

going to keep this date dim for the remainder of the course. And this is

remainder of the course. And this is going to be our reference date table

going to be our reference date table that we can use. We want to go ahead and

that we can use. We want to go ahead and mark this as a date table up here under

mark this as a date table up here under tables tools. This will enable the

tables tools. This will enable the creation of date related visuals, tables

creation of date related visuals, tables and quick measures using the tables date

and quick measures using the tables date data. So this is really powerful to make

data. So this is really powerful to make some automatic features actually happen

some automatic features actually happen in the back end. For this we need to

in the back end. For this we need to choose the correct column and there's

choose the correct column and there's only one column in here. It's date. It's

only one column in here. It's date. It's validated successfully. We'll go ahead

validated successfully. We'll go ahead and click save. With this I'm going to

and click save. With this I'm going to now ma uh navigate over to the model

now ma uh navigate over to the model view and we can see over here we have

view and we can see over here we have our job title dim table and our date

our job title dim table and our date dim. We're not going to use our job

dim. We're not going to use our job title dim anymore. So, I'm not going to

title dim anymore. So, I'm not going to connect it in, but I will drag date dim

connect it in, but I will drag date dim over here. And for this, I'll drag date

over here. And for this, I'll drag date onto job posted date. So, we can create

onto job posted date. So, we can create this relationship. And it's picking up

this relationship. And it's picking up that. Okay. The date, job posted date.

that. Okay. The date, job posted date. It's a one to many relationship, right?

It's a one to many relationship, right? There's only one unique value in the

There's only one unique value in the date table. And there's going to be

date table. And there's going to be multiple in the job postings fact.

multiple in the job postings fact. Crossfit direction we'll leave as single

Crossfit direction we'll leave as single right now. Click save. And this

right now. Click save. And this relationship is established. Now, let's

relationship is established. Now, let's get into modifying this date table even

get into modifying this date table even further. I lied to you a little bit in

further. I lied to you a little bit in the fact that we typed this out and I

the fact that we typed this out and I did that so that way you understand

did that so that way you understand understood that that could be a way of

understood that that could be a way of doing it. But if I type out calendar,

doing it. But if I type out calendar, there's actually this other one called

there's actually this other one called calendar auto. And inside of here, I

calendar auto. And inside of here, I notic in the the parameter is actually a

notic in the the parameter is actually a optional because it's in brackets. So,

optional because it's in brackets. So, you don't have to actually insert

you don't have to actually insert anything into here. and from it array it

anything into here. and from it array it returns a table with one column of dates

returns a table with one column of dates calculated from the model automatically

calculated from the model automatically and we're connected into the model. So

and we're connected into the model. So now when I run it it did I did run it

now when I run it it did I did run it there's no no difference because it

there's no no difference because it automatically picks up those dates from

automatically picks up those dates from January 1st all the way to December 31st

January 1st all the way to December 31st and I don't have to specify it. Also if

and I don't have to specify it. Also if we add more dates in this would be more

we add more dates in this would be more preferential because then we don't have

preferential because then we don't have to go in and try to update the dates and

to go in and try to update the dates and just using the calendar only function.

just using the calendar only function. Now, I do want to crank this up in a

Now, I do want to crank this up in a little bit more. Specifically, I want

little bit more. Specifically, I want more columns than just this. I don't

more columns than just this. I don't want just date. Maybe I want things like

want just date. Maybe I want things like year or day of week. So, what I'm going

year or day of week. So, what I'm going to do is press shift enter to move this

to do is press shift enter to move this on down. And I'm actually going to move

on down. And I'm actually going to move it down twice. And we're going to use

it down twice. And we're going to use the function of add columns. And it

the function of add columns. And it returns a table with new columns

returns a table with new columns specified by the DAX expression. The

specified by the DAX expression. The first expression that we need to put in

first expression that we need to put in is a table and calendar auto is that

is a table and calendar auto is that table. After calendar auto, we need to

table. After calendar auto, we need to insert in a name. It says name one,

insert in a name. It says name one, expression one. Name one is the column

expression one. Name one is the column name. Expression one is the column we

name. Expression one is the column we want to create. Now I'm going press

want to create. Now I'm going press shift and enter on down to the next line

shift and enter on down to the next line to actually do this. We're going to keep

to actually do this. We're going to keep it simple. We want a column called year.

it simple. We want a column called year. So, what function do you think we're

So, what function do you think we're going to use to get the year? Well, we

going to use to get the year? Well, we use year. And inside of here, we need to

use year. And inside of here, we need to specify a date. And so, we need to

specify a date. And so, we need to insert in the column. If I just do a

insert in the column. If I just do a square brackets open up, I can see that

square brackets open up, I can see that date pops up. It's already picking up

date pops up. It's already picking up date because that's the column. Date is

date because that's the column. Date is this column right here. All right. And

this column right here. All right. And then I'm going to close the parenthesis

then I'm going to close the parenthesis on this, right? Because we did the

on this, right? Because we did the table, we did name one, and we did

table, we did name one, and we did expression one. Going ahead and press

expression one. Going ahead and press enter. Now we have year. Now we can

enter. Now we have year. Now we can actually add in even additional things

actually add in even additional things inside of here. I can press comma. I'm

inside of here. I can press comma. I'm going to just shift down, enter down.

going to just shift down, enter down. I'm doing this all the shift enter stuff

I'm doing this all the shift enter stuff just to reformat it. It really doesn't

just to reformat it. It really doesn't matter. This is just so visually we can

matter. This is just so visually we can see the how things are aligned easier

see the how things are aligned easier for me to read it. Anyway, now I'm

for me to read it. Anyway, now I'm seeing we have name two and expression

seeing we have name two and expression two. If I wanted to, I could put in

two. If I wanted to, I could put in something like month number. And as you

something like month number. And as you guessed, I'd probably use a function

guessed, I'd probably use a function called month for this. It takes the

called month for this. It takes the argument of date. So I'll do that square

argument of date. So I'll do that square bracket, insert in date, and then that

bracket, insert in date, and then that is our second expression. So I'll shift

is our second expression. So I'll shift on down and put a closing parenthesis on

on down and put a closing parenthesis on here. Press enter. Bam. We got month

here. Press enter. Bam. We got month number. Since we have month number, you

number. Since we have month number, you know, we're going to need month name. So

know, we're going to need month name. So let's add this in. I'm going to insert

let's add this in. I'm going to insert in a comma and shift enter down. Write

in a comma and shift enter down. Write the month name for this. And for this,

the month name for this. And for this, there's not a month function for this.

there's not a month function for this. For this one, going to the source

For this one, going to the source documentation, we can use the format

documentation, we can use the format function. In it, it takes a value. So,

function. In it, it takes a value. So, in our case, we're going to take date

in our case, we're going to take date and then how we want to format it or the

and then how we want to format it or the format string. We can see that it works

format string. We can see that it works in calculated columns and calculated

in calculated columns and calculated tables as we're doing. Now, if we scroll

tables as we're doing. Now, if we scroll on down, we can get to this section on

on down, we can get to this section on custom datetime formats. And scrolling

custom datetime formats. And scrolling through this as well, I can see that

through this as well, I can see that right here. If we do four lowercase M's

right here. If we do four lowercase M's or uppercase M's, it displays the month

or uppercase M's, it displays the month as a full month name. So inside of here,

as a full month name. So inside of here, I'm going to do format because that's

I'm going to do format because that's the function we want to use. For the

the function we want to use. For the value, we're going to insert in the

value, we're going to insert in the date. And then for the format, inside of

date. And then for the format, inside of quotes, we're going to insert in those

quotes, we're going to insert in those M's. This looks good to me. I'm going to

M's. This looks good to me. I'm going to go ahead and press enter. Bam. We got a

go ahead and press enter. Bam. We got a month name now. So now that we know that

month name now. So now that we know that tactic, if I wanted to do something like

tactic, if I wanted to do something like in the weekday name, I could do similar

in the weekday name, I could do similar with the format and just do four

with the format and just do four lowercase D's running enter. I get the

lowercase D's running enter. I get the weekday name. Now I'm going to enter in

weekday name. Now I'm going to enter in a few more different columns that I want

a few more different columns that I want in there and then we're going to go

in there and then we're going to go through it briefly. First up is date

through it briefly. First up is date key. And all this is doing is just

key. And all this is doing is just getting it into a numerical way. If I

getting it into a numerical way. If I want to manipulate or reference it

want to manipulate or reference it later, I can. Pretty common to use a

later, I can. Pretty common to use a date key in a date dimension table. As

date key in a date dimension table. As we had previously, we're going to keep

we had previously, we're going to keep the year, also the month number, and the

the year, also the month number, and the month name. We'll take it a step further

month name. We'll take it a step further by adding in the year month, which all

by adding in the year month, which all it is in this case is a dash between the

it is in this case is a dash between the two. And then also adding in the quarter

two. And then also adding in the quarter itself. If you notice these, I used all

itself. If you notice these, I used all uppercase. You can use uppercase or

uppercase. You can use uppercase or lowercase when specifying inside of that

lowercase when specifying inside of that format function. Now, jumping to the

format function. Now, jumping to the last one, we did have the weekday name.

last one, we did have the weekday name. And because of that, I included these

And because of that, I included these two extra functions of week number and

two extra functions of week number and weekday number. All they take is the fun

weekday number. All they take is the fun function for this one, week number of

function for this one, week number of actual week num specifying the date and

actual week num specifying the date and then the number two. Same thing for

then the number two. Same thing for weekday. It takes the date and then the

weekday. It takes the date and then the number two. for both of these options

number two. for both of these options where we're specifying two. In this

where we're specifying two. In this case, I'm at the weak gnome

case, I'm at the weak gnome documentation syntax is date and then in

documentation syntax is date and then in these brackets here, it's saying it's an

these brackets here, it's saying it's an optional parameter, the return type. And

optional parameter, the return type. And if we go scroll down to this table, we

if we go scroll down to this table, we can see that by default, it's one and

can see that by default, it's one and that means the week begins on Sunday.

that means the week begins on Sunday. I'm fancymancy and I changed two so that

I'm fancymancy and I changed two so that way the week begins on a Monday cuz

way the week begins on a Monday cuz that's when the work week begins. So, I

that's when the work week begins. So, I could easily take this two out if I

could easily take this two out if I wanted to, as I mentioned, and run this

wanted to, as I mentioned, and run this again. And this doesn't really change

again. And this doesn't really change any of our different values. It's going

any of our different values. It's going to change it later on when we go to plot

to change it later on when we go to plot it. Anyway, this is our date dimensional

it. Anyway, this is our date dimensional table. Feel free to pause the screen and

table. Feel free to pause the screen and make sure that you get this down into

make sure that you get this down into your report so you have this available.

your report so you have this available. Now, I love having this dateimensional

Now, I love having this dateimensional table because not only now I can do

table because not only now I can do something like this where I can take the

something like this where I can take the line chart and drag our date into the

line chart and drag our date into the x-axis and then job ID into the yaxis

x-axis and then job ID into the yaxis for the count. Okay, so we get that.

for the count. Okay, so we get that. We've seen that before, but we can now

We've seen that before, but we can now take it a step for f further with our

take it a step for f further with our date dimensional table. In this, I'm

date dimensional table. In this, I'm going to create a stacked column chart.

going to create a stacked column chart. And I can drag the weekday name into the

And I can drag the weekday name into the X axis and drag the job ID into the

X axis and drag the job ID into the Yaxis. And we can see out of this, we'll

Yaxis. And we can see out of this, we'll go into focus mode. Tuesday is by far

go into focus mode. Tuesday is by far the most postings that when they happen

the most postings that when they happen and they happen the least on the

and they happen the least on the weekend. Now, you may be like, Luke,

weekend. Now, you may be like, Luke, this is great and all, but these names,

this is great and all, but these names, these weekday names are out of order.

these weekday names are out of order. What the heck is going on here? I want

What the heck is going on here? I want them to be in order. Well, navigating

them to be in order. Well, navigating back into that table view for our column

back into that table view for our column or for our date table itself. What we

or for our date table itself. What we can do is you can just select your

can do is you can just select your column of choice. In this case, weekday

column of choice. In this case, weekday name, we want it to we want to organize

name, we want it to we want to organize it. And inside this column tools tab

it. And inside this column tools tab that's going to pop up when we have this

that's going to pop up when we have this selected, they have this option all the

selected, they have this option all the way to the right of sort by column. and

way to the right of sort by column. and you sort one column by the contents of

you sort one column by the contents of another. Luckily, we built this this

another. Luckily, we built this this weekday number which we can see that the

weekday number which we can see that the one value is associated with Sunday, two

one value is associated with Sunday, two with Monday and so on. Anyway, what we

with Monday and so on. Anyway, what we can do is select sort by column with

can do is select sort by column with weekday name selected. We can say sort

weekday name selected. We can say sort by the week number. Now, you may get

by the week number. Now, you may get this popup right here that says sort by

this popup right here that says sort by another column. We can't sort the

another column. We can't sort the weekday name column by week number. This

weekday name column by week number. This I feel is a little bit of a glitch right

I feel is a little bit of a glitch right now in PowerBI. I'm going to click

now in PowerBI. I'm going to click close. And what you may need to do is

close. And what you may need to do is just recclick it, go to sort by column,

just recclick it, go to sort by column, and just do it again. Select it's a

and just do it again. Select it's a weekday number. And then this popup

weekday number. And then this popup doesn't happen anymore. I don't know.

doesn't happen anymore. I don't know. It's just a weird little glitch that's

It's just a weird little glitch that's going on with PowerBI. Anyway, I go back

going on with PowerBI. Anyway, I go back to report view. I need to actually

to report view. I need to actually refresh this and go back here. I'm going

refresh this and go back here. I'm going to remove weekday name and drag weekday

to remove weekday name and drag weekday name back into the X-axis. All right.

name back into the X-axis. All right. Bam. It has now updated to be in order

Bam. It has now updated to be in order from Sunday down to Saturday. Now, as I

from Sunday down to Saturday. Now, as I mentioned, I like my work week to start

mentioned, I like my work week to start on Monday. So, actually, I'm going to go

on Monday. So, actually, I'm going to go back into that table view, and we're

back into that table view, and we're going to change that week number for the

going to change that week number for the weekday function to specify that we want

weekday function to specify that we want it to begin on Monday. And I'll do the

it to begin on Monday. And I'll do the same thing for week number itself.

same thing for week number itself. Specifying this is two. Then running

Specifying this is two. Then running this all, pressing enter. I can see now

this all, pressing enter. I can see now that it's updated because Monday up here

that it's updated because Monday up here is now one. And navigating back to my

is now one. And navigating back to my visual, my visual now updates for that.

visual, my visual now updates for that. Okay, this date dimensional table that

Okay, this date dimensional table that we created is going to be continued to

we created is going to be continued to use for the remainder of the course. So

use for the remainder of the course. So very important that you have this down

very important that you have this down correct. If you need to pause the screen

correct. If you need to pause the screen right now and get this updated formula

right now and get this updated formula for you to actually use.

Now let's jump into the last use case of DAX and it's going to be the m the

DAX and it's going to be the m the primary focus for the next lesson and

primary focus for the next lesson and that's on measures. As you recall from

that's on measures. As you recall from previously anytime we were creating some

previously anytime we were creating some sort of bar chart or count I would drag

sort of bar chart or count I would drag the job title short into that Y-axis and

the job title short into that Y-axis and then anytime I want to do a count of the

then anytime I want to do a count of the jobs to drag that into the X-axis and

jobs to drag that into the X-axis and get the count of this. We also did this

get the count of this. We also did this with job ID as well getting the count

with job ID as well getting the count values are still the same. Anyway, every

values are still the same. Anyway, every single time I had to go through and then

single time I had to go through and then update this title to job count, it's a

update this title to job count, it's a mess. This is what we call an implicit

mess. This is what we call an implicit measure. It's implied. And we're limited

measure. It's implied. And we're limited with these implicit measures to only the

with these implicit measures to only the selections through this drop down arrow

selections through this drop down arrow right here. But we can use explicit

right here. But we can use explicit measures with DAX to make even more

measures with DAX to make even more complex type of measures. But let's keep

complex type of measures. But let's keep it simple for the time being. I'm going

it simple for the time being. I'm going to go ahead and just copy this

to go ahead and just copy this visualization and then paste it on over

visualization and then paste it on over here. And we'll get rid of this job

here. And we'll get rid of this job count. That's what we're going to be

count. That's what we're going to be creating, a count of the jobs. Now, to

creating, a count of the jobs. Now, to create a measure, we can do it a number

create a measure, we can do it a number of different ways. I can rightclick the

of different ways. I can rightclick the job postings fact table and then say,

job postings fact table and then say, hey, I want to create a new measure. We

hey, I want to create a new measure. We can navigate to it under the modeling

can navigate to it under the modeling tab, getting to this new measure. Also,

tab, getting to this new measure. Also, I can just select the table itself and

I can just select the table itself and table tools pops up. And then from there

table tools pops up. And then from there create new measure. So we first start by

create new measure. So we first start by giving this measure a name. We're going

giving this measure a name. We're going to keep it simple of job count. And we

to keep it simple of job count. And we want to do a count. So there's probably

want to do a count. So there's probably a formula called count. And in it we

a formula called count. And in it we need to count the numbers in a column.

need to count the numbers in a column. So we can just specify the job ID. Put a

So we can just specify the job ID. Put a closing parenthesis on this. Press

closing parenthesis on this. Press enter. And now inside of our job

enter. And now inside of our job postings fact table we have this

postings fact table we have this measure. We can see this by this little

measure. We can see this by this little calculator icon. And I can take job

calculator icon. And I can take job count which has that name written. And

count which has that name written. And so the column name is also updated for

so the column name is also updated for job count. And we're getting that same

job count. And we're getting that same value of 128994 as we got with the

value of 128994 as we got with the implicit measure. So their explicit

implicit measure. So their explicit measure a lot easier. So every time now

measure a lot easier. So every time now we want job count. All we got to do is

we want job count. All we got to do is drag in that explicit measure. Now job

drag in that explicit measure. Now job count just because it's in the job

count just because it's in the job postings f fact table doesn't mean it

postings f fact table doesn't mean it can only be used with the job postings

can only be used with the job postings fact table. Remember we do have

fact table. Remember we do have relationships throughout the tables. So,

relationships throughout the tables. So, we could technically use this for skills

we could technically use this for skills to figure out what are the counts of

to figure out what are the counts of jobs for a certain skill. Back in our

jobs for a certain skill. Back in our report view, I'm going to get rid of

report view, I'm going to get rid of this table right here. We don't need

this table right here. We don't need this one anymore since we have an

this one anymore since we have an explicit measure. Now, I'm going to copy

explicit measure. Now, I'm going to copy it and then paste it on over here. And

it and then paste it on over here. And instead of job title short in the yaxis,

instead of job title short in the yaxis, I'm going to navigate to that skills dim

I'm going to navigate to that skills dim and I'm going to drag skills over to the

and I'm going to drag skills over to the Yaxis. Going into focus mode because we

Yaxis. Going into focus mode because we can see that the visualization built.

can see that the visualization built. Bam. We're now getting this job count

Bam. We're now getting this job count based on the skill. Let's just create

based on the skill. Let's just create one more measure for funsies and that's

one more measure for funsies and that's going to be I'm going to rightclick this

going to be I'm going to rightclick this and select new measure for this.

and select new measure for this. Remember we were doing the median yearly

Remember we were doing the median yearly salary all the time with this. Well, we

salary all the time with this. Well, we can create a measure for this. I'm going

can create a measure for this. I'm going to create median yearly salary. Set it

to create median yearly salary. Set it to equal. And then in this we're going

to equal. And then in this we're going to use the median function and we're

to use the median function and we're going to specify that salary year

going to specify that salary year average column. Going to close the

average column. Going to close the parenthesis. Press enter. And then how

parenthesis. Press enter. And then how we did that job count for job title

we did that job count for job title short. Well, I can just trade out those

short. Well, I can just trade out those values removing job count. And now we

values removing job count. And now we have the median yearly salary in here.

have the median yearly salary in here. Oh, and if remember right, we had to

Oh, and if remember right, we had to format this every single time with this.

format this every single time with this. If I select median yearly salary uh from

If I select median yearly salary uh from the data pane here and then this measure

the data pane here and then this measure tools pops up. What I can do is I can

tools pops up. What I can do is I can not only change the name but also I can

not only change the name but also I can change what is the format. Specifically,

change what is the format. Specifically, I'm going to change it to currency and

I'm going to change it to currency and zero. And so now, every time I'm using

zero. And so now, every time I'm using this, so let's uh it's going to use that

this, so let's uh it's going to use that same value. Specifically, going to that

same value. Specifically, going to that skills, I'm going to get rid of that job

skills, I'm going to get rid of that job count and drag median yearly salary into

count and drag median yearly salary into here. And whenever I scroll over it,

here. And whenever I scroll over it, it's actually formatted correctly. So,

it's actually formatted correctly. So, not only do I get the correct name, I

not only do I get the correct name, I don't have to update, but it keeps the

don't have to update, but it keeps the data format that I'll want. I love

data format that I'll want. I love explicit measures.

Now, one last thing, I promise, and this has to do with measures versus

has to do with measures versus calculated columns and tables. This is

calculated columns and tables. This is definitely a new concept, especially to

definitely a new concept, especially to those that haven't dealt with measures

those that haven't dealt with measures before. So, it's hard to wrap your head

before. So, it's hard to wrap your head around what's the difference between a

around what's the difference between a calculated column and what is a measure.

calculated column and what is a measure. It's important to understand that for

It's important to understand that for calculated columns and also tables

calculated columns and also tables they're calculated immediately upon data

they're calculated immediately upon data import and they're visible in the data

import and they're visible in the data and also those report views. Now

and also those report views. Now measures on the other hand are not done

measures on the other hand are not done on the data import instead they're done

on the data import instead they're done at query runtime is is when that

at query runtime is is when that basically the visualization is getting

basically the visualization is getting built. So in this case there's a table

built. So in this case there's a table that we're doing on job count. So these

that we're doing on job count. So these job titles it's getting calculated then

job titles it's getting calculated then and it's getting calculated down to that

and it's getting calculated down to that job title level. That's basically the

job title level. That's basically the filter for it. It's available in report

filter for it. It's available in report views like we can do on a canvas, but

views like we can do on a canvas, but it's also available in a DAX view as

it's also available in a DAX view as well. And DAX query view, we'll cover

well. And DAX query view, we'll cover more in the next lesson. I feel like

more in the next lesson. I feel like we've covered enough for the time being.

we've covered enough for the time being. Anyway, it's just an important concept

Anyway, it's just an important concept to understand because many people when

to understand because many people when they go to the table view specifically

they go to the table view specifically for job postings, the fact table that we

for job postings, the fact table that we have here, I can see inside of here

have here, I can see inside of here these calculated columns that we created

these calculated columns that we created of salary hour adjusted and salary year

of salary hour adjusted and salary year and hour as designated by these two

and hour as designated by these two icons in front of it. But if I try to

icons in front of it. But if I try to look for things like the job count or

look for things like the job count or the median yearly salary, that's not

the median yearly salary, that's not going to be anywhere on this table

going to be anywhere on this table because that's not calculated to show in

because that's not calculated to show in this view immediately on the data load.

this view immediately on the data load. Instead, it's not calculated until let's

Instead, it's not calculated until let's say we put a M matrix in here and I draw

say we put a M matrix in here and I draw job title short into the rows and then

job title short into the rows and then job count into the values. It's not

job count into the values. It's not calculated until this time. And it

calculated until this time. And it allows us to get this aggregation at

allows us to get this aggregation at this type of level which we're going to

this type of level which we're going to dive into more in the next lesson. All

dive into more in the next lesson. All right, you have some practice problems

right, you have some practice problems now. Go through and get familiar with

now. Go through and get familiar with calculated columns, calculated tables,

calculated columns, calculated tables, and also these explicit measures. In the

and also these explicit measures. In the next lesson, once you have that all

next lesson, once you have that all down, we're going to be diving even

down, we're going to be diving even deeper into explicit measures because

deeper into explicit measures because they're the primary one that I'm using

they're the primary one that I'm using with DAX in here. With that, I'll see

with DAX in here. With that, I'll see you there.

Welcome to the second of three lessons on DAX. This one is going to be diving

on DAX. This one is going to be diving even deeper into explicit measures. With

even deeper into explicit measures. With this, we're going to dive into building

this, we're going to dive into building more complex measures, but also better

more complex measures, but also better understanding how to use them.

understanding how to use them. Specifically, we're going to be able to

Specifically, we're going to be able to do calculations that we haven't been

do calculations that we haven't been able to do before, like calculating how

able to do before, like calculating how many skills or an allen are associated

many skills or an allen are associated with a particular job title. And because

with a particular job title. And because of this measure, we'll also be able to

of this measure, we'll also be able to look into see do jobs that request more

look into see do jobs that request more skills, do they actually pay more? So,

skills, do they actually pay more? So, some really unique insights that we

some really unique insights that we wouldn't be able to do without DAX and

wouldn't be able to do without DAX and specifically explicit measures. All

specifically explicit measures. All right, let's jump into it.

For this, you can either start with working with that report that you had

working with that report that you had from the last lesson, or if you didn't

from the last lesson, or if you didn't follow along and didn't keep up, feel

follow along and didn't keep up, feel free to just open up the one from the

free to just open up the one from the last lesson on 4.1 DAX intro. I did a

last lesson on 4.1 DAX intro. I did a few more calculations in here for the

few more calculations in here for the column check, the table check, and the

column check, the table check, and the measure check. Anyway, there's um

measure check. Anyway, there's um unnecessary things that we created that

unnecessary things that we created that we're not going to be using later on.

we're not going to be using later on. So, I want to go ahead and clean this

So, I want to go ahead and clean this report up to make sure that it's as

report up to make sure that it's as minimal as necessary so it doesn't cause

minimal as necessary so it doesn't cause issues later on when we're trying to

issues later on when we're trying to build our project. Specifically, I'm

build our project. Specifically, I'm going to delete these two columns. This

going to delete these two columns. This one here on new column check and delete

one here on new column check and delete this one here on date table check. What

this one here on date table check. What I am keeping are the visualizations that

I am keeping are the visualizations that we created in the last one. You may have

we created in the last one. You may have to if you keeping if you're working on

to if you keeping if you're working on from the last lesson, you may have to

from the last lesson, you may have to recreate some of these. But basically we

recreate some of these. But basically we have on the left hand side what are the

have on the left hand side what are the counts of different jobs and what are

counts of different jobs and what are the salaries for different jobs and then

the salaries for different jobs and then conversely we have what are the counts

conversely we have what are the counts of different skills along with what are

of different skills along with what are the different pays for the top paying

the different pays for the top paying skills. So the report itself is looking

skills. So the report itself is looking good. Now let's move over to the data

good. Now let's move over to the data model itself. We did create this job

model itself. We did create this job title dim table which we can view here

title dim table which we can view here inside of our table view. We're not

inside of our table view. We're not going to be using this any further. So,

going to be using this any further. So, I'm going to go ahead and rightclick

I'm going to go ahead and rightclick this and say delete from model. It'll

this and say delete from model. It'll prompt you if you want to do it. Yep.

prompt you if you want to do it. Yep. Next up, after the calculated table we

Next up, after the calculated table we created, I also want to get rid of these

created, I also want to get rid of these calculated columns that we created. So,

calculated columns that we created. So, I want to remove these two columns right

I want to remove these two columns right here. Conveniently, I can see them with

here. Conveniently, I can see them with their icon that there were the

their icon that there were the calculated columns. All you have to do

calculated columns. All you have to do is just rightclick them and say delete

is just rightclick them and say delete from model and confirm it. I'll do it

from model and confirm it. I'll do it again for salary, hour, year. There's a

again for salary, hour, year. There's a error right now cuz I deleted them out

error right now cuz I deleted them out of order. Anyway, both of them gone now.

of order. Anyway, both of them gone now. Now I also want to get rid of salary

Now I also want to get rid of salary hour adjusted and salary year and hour.

hour adjusted and salary year and hour. You can rightclick it and delete it this

You can rightclick it and delete it this way. I want to control it though in

way. I want to control it though in Power Query because remember we did

Power Query because remember we did create these in Power Query. So for this

create these in Power Query. So for this I'm going to go into Power Query by

I'm going to go into Power Query by going to transform data and then inside

going to transform data and then inside of here I'm going to remove these last

of here I'm going to remove these last two steps of adding the different

two steps of adding the different columns into here. And we'll keep

columns into here. And we'll keep everything else in there. Looks good. Go

everything else in there. Looks good. Go ahead and close and apply it. All right.

ahead and close and apply it. All right. Not too bad. We've now cleaned up not

Not too bad. We've now cleaned up not only our report canvas, but also cleaned

only our report canvas, but also cleaned up our data model itself. That's in a

up our data model itself. That's in a good spot to continue on. This is good

good spot to continue on. This is good practice always to get rid of any

practice always to get rid of any measures, calculated columns, or whatnot

measures, calculated columns, or whatnot that you're not using and that aren't

that you're not using and that aren't useful because it's just going to cause

useful because it's just going to cause confusion.

On the same note of doing a model and report cleanup, we also need to make

report cleanup, we also need to make sure that we're implementing best

sure that we're implementing best practices for DAX and measures

practices for DAX and measures specifically in order to organize these

specifically in order to organize these measures that we're creating.

measures that we're creating. Previously, we created these measures

Previously, we created these measures under this job posting fact. And I can

under this job posting fact. And I can see have job count here and median

see have job count here and median yearly salary down below it. It's best

yearly salary down below it. It's best practice to create a table to just keep

practice to create a table to just keep all of your measures in. And we can do

all of your measures in. And we can do this with calculated tables. going to

this with calculated tables. going to modeling, inserting in a new table.

modeling, inserting in a new table. Inside of here, I'm going to rename

Inside of here, I'm going to rename table, and I'm going to do underscore

table, and I'm going to do underscore measures. This is for a few reasons.

measures. This is for a few reasons. One, measures, the actual name is

One, measures, the actual name is reserved. You can't use that. But two,

reserved. You can't use that. But two, since we have an underscore at the

since we have an underscore at the front, this allows it to be up at the

front, this allows it to be up at the top and so we can quickly access any

top and so we can quickly access any measures that are inside of here. Now,

measures that are inside of here. Now, we want to get our two measures that we

we want to get our two measures that we created inside of here. You

created inside of here. You unfortunately you can't just drag and

unfortunately you can't just drag and drop into here. But what I can do is I

drop into here. But what I can do is I can select job count and measure tools

can select job count and measure tools tab comes up and it says the name of it

tab comes up and it says the name of it but also what's the home table and we

but also what's the home table and we can change this to measures. We can also

can change this to measures. We can also do this the same for median yearly

do this the same for median yearly salary. I'm going to change this one to

salary. I'm going to change this one to measures as well. Now inside of here in

measures as well. Now inside of here in our measures table we have our measures.

our measures table we have our measures. Now you will notice it does have this

Now you will notice it does have this column. If I click on this and then go

column. If I click on this and then go into the table view, you have to have a

into the table view, you have to have a column which is blank. You can't get rid

column which is blank. You can't get rid of that. That just has to be there. And

of that. That just has to be there. And just a reminder from last lesson, it

just a reminder from last lesson, it doesn't matter where these measures are

doesn't matter where these measures are as we moved them. The measures, as we

as we moved them. The measures, as we can see here, um we're using this job

can see here, um we're using this job count in the x-axis right here. The

count in the x-axis right here. The measures are still going to work the

measures are still going to work the same. Now, besides keeping them

same. Now, besides keeping them organized in a certain location, the

organized in a certain location, the next thing that is widely done is

next thing that is widely done is commenting to make sure that you're

commenting to make sure that you're documenting what this measure actually

documenting what this measure actually does. What do I mean by this? I'm going

does. What do I mean by this? I'm going to expand this down to full full view so

to expand this down to full full view so that way we can see it. I'm going to

that way we can see it. I'm going to press shift enter so that way we can

press shift enter so that way we can navigate down and we can insert in

navigate down and we can insert in what's called a comment. How I do this

what's called a comment. How I do this is I can do two forward slashes as shown

is I can do two forward slashes as shown here. And everything after this on this

here. And everything after this on this line is not going to get interpreted.

line is not going to get interpreted. And I can put in something like this of

And I can put in something like this of that calculates the median yearly salary

that calculates the median yearly salary across all job postings. If I wanted to

across all job postings. If I wanted to put another line down, I press shift

put another line down, I press shift enter, put in two forward slashes, and

enter, put in two forward slashes, and then I could put in something like this

then I could put in something like this of uses median over average to account

of uses median over average to account for high salary outliers. Also,

for high salary outliers. Also, typically what we're going to see is

typically what we're going to see is that the name of the measure is on the

that the name of the measure is on the first line and then pressing shift enter

first line and then pressing shift enter after median. The function or the next

after median. The function or the next function begins on the next line. And

function begins on the next line. And from time to time, you may see me, this

from time to time, you may see me, this one only has one variable, so this is

one only has one variable, so this is actually fine. But from time to time,

actually fine. But from time to time, you may see me putting variables on

you may see me putting variables on their own separate line and then having

their own separate line and then having it in this manner. Regardless, even if I

it in this manner. Regardless, even if I press enter right here, close out of

press enter right here, close out of this formula bar, everything underneath

this formula bar, everything underneath this is working just fine. That is using

this is working just fine. That is using this measure. So, I can verify this is

this measure. So, I can verify this is using that median yearly salary measure.

using that median yearly salary measure. Now, let's clean up this job count as

Now, let's clean up this job count as well. I'm going to bring this one on

well. I'm going to bring this one on down here. And then we're going to

down here. And then we're going to insert in a multi-line comma. There's

insert in a multi-line comma. There's going to be multiple lines in here. And

going to be multiple lines in here. And how we can do this is we do a forward

how we can do this is we do a forward slash asterisk. And then I'm going to

slash asterisk. And then I'm going to shift enter, shift enter, shift enter.

shift enter, shift enter, shift enter. And then we can close off this comment

And then we can close off this comment by doing an asterisk and then forward

by doing an asterisk and then forward slash. Now, anything we put inside of

slash. Now, anything we put inside of here is going to be commented off. And

here is going to be commented off. And we can see that it's all commented by

we can see that it's all commented by this new text that I just stuck in here

this new text that I just stuck in here as it's all green. The syntax

as it's all green. The syntax highlighting is pretty helpful. And I

highlighting is pretty helpful. And I just put that it calculates the total

just put that it calculates the total count of jobs and it's used as a

count of jobs and it's used as a denominator as we're going to show in

denominator as we're going to show in various per job calculations. Now I want

various per job calculations. Now I want to switch this actually and we're going

to switch this actually and we're going to be using now instead count rows and

to be using now instead count rows and we're using count rows because every row

we're using count rows because every row in this table of job postings fact is a

in this table of job postings fact is a single job posting or the count of a

single job posting or the count of a job. So we're going to do that. Anyway,

job. So we're going to do that. Anyway, I just want to show before this. This is

I just want to show before this. This is what's really neat about measures and

what's really neat about measures and what I really love about them. Right? So

what I really love about them. Right? So we have this used inside of here. These

we have this used inside of here. These top two tables here that are using that

top two tables here that are using that job count. If for some reason I need to

job count. If for some reason I need to update a measure, all I have to do is

update a measure, all I have to do is just come in here, insert in the new

just come in here, insert in the new formula that I want to use for this. I

formula that I want to use for this. I want to use count rows. In this case,

want to use count rows. In this case, for count rows, it counts the number of

for count rows, it counts the number of rows in a table. So I only need to list

rows in a table. So I only need to list a table of job postings fact. Close the

a table of job postings fact. Close the parenthesis. Press enter. And then

parenthesis. Press enter. And then navigating back in here, these values

navigating back in here, these values didn't change because both of them were

didn't change because both of them were basically calculating the same thing. I

basically calculating the same thing. I just prefer count uh rows over this. But

just prefer count uh rows over this. But all of the different reports and

all of the different reports and canvases that I'm using to use this

canvases that I'm using to use this measure are updated as well. Measures

measure are updated as well. Measures are great because they allow us to have

are great because they allow us to have a single source of truth. So you can

a single source of truth. So you can catch yourself from inadvertently doing

catch yourself from inadvertently doing wrong calculations with implicit

wrong calculations with implicit measures. Explicit measures help prevent

measures. Explicit measures help prevent that.

So we've been doing previously a lot of job count and median yearly salary.

job count and median yearly salary. Let's actually change this up a little

Let's actually change this up a little bit and let's start putting to use those

bit and let's start putting to use those skills. What we're going to be

skills. What we're going to be calculating are these two measures.

calculating are these two measures. First, the easier one is that we want to

First, the easier one is that we want to calculate the skill count. So, how many

calculate the skill count. So, how many skills are associated for a certain job

skills are associated for a certain job title. Granted, this number doesn't

title. Granted, this number doesn't really help us that much because if

really help us that much because if there's more job postings, the higher

there's more job postings, the higher the job count, the higher the skill

the job count, the higher the skill count. So there's nothing we can do with

count. So there's nothing we can do with this until we turn it into a ratio of

this until we turn it into a ratio of skills per job. And in that case, we can

skills per job. And in that case, we can then see uh what is the amount of skills

then see uh what is the amount of skills per a job actually normalized out. So

per a job actually normalized out. So let's get into building this. I'm going

let's get into building this. I'm going to start a new page for us to visualize

to start a new page for us to visualize this on. And we're going to do our first

this on. And we're going to do our first measure. We want to do it right inside

measure. We want to do it right inside of that measures table. We want to do

of that measures table. We want to do that skill count. So I'm going to

that skill count. So I'm going to rightclick it and select new measure.

rightclick it and select new measure. Now for this we want to calculate

Now for this we want to calculate obviously skill count. Keep the name

obviously skill count. Keep the name real original. Use shift enter to go

real original. Use shift enter to go down to the next line. And we're going

down to the next line. And we're going to follow a similar approach that we did

to follow a similar approach that we did for job count in that we're going to use

for job count in that we're going to use count rows. And we want to use the

count rows. And we want to use the skills job dim. Then we'll go ahead and

skills job dim. Then we'll go ahead and close this function. Now, it's important

close this function. Now, it's important to note we don't want to do the skills

to note we don't want to do the skills dim because remember the skills dim

dim because remember the skills dim table is only a list of a single skill

table is only a list of a single skill in there. We want to look at the skills

in there. We want to look at the skills job dim specifically the skill skill IDs

job dim specifically the skill skill IDs cuz this has the list of each every

cuz this has the list of each every individual skill and we can see it from

individual skill and we can see it from the table view as well. Hey, there's

the table view as well. Hey, there's multiple different jobs that can have

multiple different jobs that can have multiple different skills. Anyway,

multiple different skills. Anyway, navigating back into our report view.

navigating back into our report view. Looks like skill count. Skill count

Looks like skill count. Skill count disappeared. I'm going to go ahead and

disappeared. I'm going to go ahead and select it again because I do want to add

select it again because I do want to add a comment on here. Shift enter down. And

a comment on here. Shift enter down. And I'm just going to put in it's used to

I'm just going to put in it's used to find the total count of skills for a job

find the total count of skills for a job posting. Now, anytime I'm making any of

posting. Now, anytime I'm making any of these skills, I typically like to use

these skills, I typically like to use either a matrix or a table to make sure

either a matrix or a table to make sure that it's calculating correctly as I go.

that it's calculating correctly as I go. Feel is a little bit easier than using

Feel is a little bit easier than using any other charts or visualizations. And

any other charts or visualizations. And for this, we want to look at the job

for this, we want to look at the job title short level. So, I can put that

title short level. So, I can put that into the rows. Putting this into focus

into the rows. Putting this into focus mode so we can actually see it a little

mode so we can actually see it a little better. I can then do things like drag

better. I can then do things like drag the skill count into the values column.

the skill count into the values column. And I'm noticing right now it's not

And I'm noticing right now it's not really formatted how I want it. I can

really formatted how I want it. I can select it. This measure tools come up.

select it. This measure tools come up. I'm going to put in a comma. All right,

I'm going to put in a comma. All right, looking good. Not too bad. I want to get

looking good. Not too bad. I want to get out of this. Anytime I want the formula

out of this. Anytime I want the formula bar to disappear, I can just click over

bar to disappear, I can just click over here in the data pane and select like

here in the data pane and select like another table just to get rid of it.

another table just to get rid of it. Granted, it can't be a calculated table.

Granted, it can't be a calculated table. Anyway, now we want to get skills per

Anyway, now we want to get skills per job. And conveniently, we've already

job. And conveniently, we've already done the job count. So, I'm going to

done the job count. So, I'm going to drag it down here. What we can do is we

drag it down here. What we can do is we can divide the skill count measure by

can divide the skill count measure by the job count measure. So, let's create

the job count measure. So, let's create this measure. I'm going to rightclick

this measure. I'm going to rightclick measure, select new measure. We're going

measure, select new measure. We're going to call this skill per job. And for this

to call this skill per job. And for this one, we could do skill count, which the

one, we could do skill count, which the measure pops up, and then job count

measure pops up, and then job count divided by in this type of syntax. It's

divided by in this type of syntax. It's going to be perfectly fine. I can go

going to be perfectly fine. I can go skills per job, drag it into here. It's

skills per job, drag it into here. It's calculating correctly. This is good, but

calculating correctly. This is good, but not necessarily best practice. Instead,

not necessarily best practice. Instead, what I would like to see is I would use

what I would like to see is I would use the divide function. And it's a safe

the divide function. And it's a safe divide function with the ability to

divide function with the ability to handle divide by zero cases. So you

handle divide by zero cases. So you don't have to deal with errors and stuff

don't have to deal with errors and stuff like that. In there, we just specify the

like that. In there, we just specify the nu numerator of skill count and then

nu numerator of skill count and then also the denominator of job count. Press

also the denominator of job count. Press enter. None of the values change. And

enter. None of the values change. And then as best practice inside of here,

then as best practice inside of here, I'm going to just enter in a comment

I'm going to just enter in a comment that's used to find the ratio of skills

that's used to find the ratio of skills required for job postings. Looks good.

required for job postings. Looks good. One thing is I don't like that it has

One thing is I don't like that it has two decimal places. So I'll select it

two decimal places. So I'll select it and we're just going to put it down to

and we're just going to put it down to one decimal place. It's just tmi. We

one decimal place. It's just tmi. We don't need all that information. All

don't need all that information. All right. Now sorting this table, we can

right. Now sorting this table, we can see that things like senior data

see that things like senior data engineer, data engineers, senior data

engineer, data engineers, senior data scientists, they're requiring more

scientists, they're requiring more skills, whereas data analysts, business

skills, whereas data analysts, business analysts are requiring less skills. This

analysts are requiring less skills. This looks like it honestly correlates to

looks like it honestly correlates to something else that we've calculated

something else that we've calculated previously. If I were to drag the median

previously. If I were to drag the median yearly salary also into here, we can see

yearly salary also into here, we can see that there's a very strange correlation

that there's a very strange correlation going on here. Almost like we could put

going on here. Almost like we could put this into a scatter plot or something.

this into a scatter plot or something. So, I'm going to insert in a scatter

So, I'm going to insert in a scatter plot. Go into focus mode. I'm going to

plot. Go into focus mode. I'm going to drag the median yearly salary into the

drag the median yearly salary into the x-axis. So, nice. It actually has the

x-axis. So, nice. It actually has the right column title and is formatted to

right column title and is formatted to the right currency. And then I'm going

the right currency. And then I'm going to drag skills per job into the yaxis.

to drag skills per job into the yaxis. Now there's only one value. We want to

Now there's only one value. We want to break this up by job titles. Right? So

break this up by job titles. Right? So I'm going to take the job title short

I'm going to take the job title short and drag it into the values portion.

and drag it into the values portion. I'll rename this as job title. And bam.

I'll rename this as job title. And bam. Look at this correlation. I can even go

Look at this correlation. I can even go in and insert in a trend line. Turn it

in and insert in a trend line. Turn it on. This thing is definitely looking

on. This thing is definitely looking like there's some correlation between

like there's some correlation between the number of skills and what is the

the number of skills and what is the median yearly salary. I probably would

median yearly salary. I probably would take this one step further under format

take this one step further under format your visual and I would turn on category

your visual and I would turn on category labels. So now that we can actually see

labels. So now that we can actually see where the different job titles are and

where the different job titles are and actually read it face on.

We're going to shift gears a little bit and get theoretical specifically with

and get theoretical specifically with these measures and with using DAX.

these measures and with using DAX. There's different contexts that can

There's different contexts that can actually happen. We're going to go

actually happen. We're going to go through one by one and see how these

through one by one and see how these different contexts can affect a in our

different contexts can affect a in our case measure or even calculated columns.

case measure or even calculated columns. Overall, it's important to understand

Overall, it's important to understand row context takes less priority than

row context takes less priority than query context and takes less priority

query context and takes less priority than filter context. That's getting an

than filter context. That's getting an error of herself. Let's actually just

error of herself. Let's actually just look at what the heck is row context

look at what the heck is row context first. So, what the heck is row context?

first. So, what the heck is row context? Well, as shown by this visual, row

Well, as shown by this visual, row context refers to in this table the

context refers to in this table the current row that a calculation is being

current row that a calculation is being applied. Specifically, we do we're doing

applied. Specifically, we do we're doing a day of the week calculation and it's

a day of the week calculation and it's looking at only that current row in

looking at only that current row in order to get that final answer of five

order to get that final answer of five for the day of week of January 4th for

for the day of week of January 4th for that Thursday. And we can demonstrate

that Thursday. And we can demonstrate this by creating a new column and for

this by creating a new column and for the day of week setting this equal to

the day of week setting this equal to the function of week day and specifying

the function of week day and specifying job posted date. So these DAX

job posted date. So these DAX calculations inside of this calculated

calculations inside of this calculated column are evaluating on a row context

column are evaluating on a row context label. I say label I mean level.

label. I say label I mean level. Now let's actually take this

Now let's actually take this calculation. We don't need to calculate

calculation. We don't need to calculate day of week in here. I'm not going to

day of week in here. I'm not going to keep this one. Let's actually calculate

keep this one. Let's actually calculate something useful. Specifically, we did

something useful. Specifically, we did this explicit measure of skill per job.

this explicit measure of skill per job. Is there a way we could use row context

Is there a way we could use row context and provide how many skills are

and provide how many skills are associated with a certain job? What we

associated with a certain job? What we would need to do is do a calculated

would need to do is do a calculated column in this job postings fact table

column in this job postings fact table and query the skills job dim table to

and query the skills job dim table to get for every job posting what is the

get for every job posting what is the count of the associated rows for that

count of the associated rows for that particular job posting. So how will we

particular job posting. So how will we do this? Well, I'm going to call this a

do this? Well, I'm going to call this a the skill count and we're going to be

the skill count and we're going to be doing count rows. And then it says, hey,

doing count rows. And then it says, hey, insert a table. So like I said, we're

insert a table. So like I said, we're going to insert in skills job dim and

going to insert in skills job dim and then go ahead and press enter. Now the

then go ahead and press enter. Now the problem is this is as you can see it's

problem is this is as you can see it's 2.2 million which if we remember or we

2.2 million which if we remember or we can just actually navigate to it. We

can just actually navigate to it. We don't have to remember skills job dim is

don't have to remember skills job dim is 2.2 million rows long. So this

2.2 million rows long. So this calculation that's going on inside of

calculation that's going on inside of our fact table it's not right.

our fact table it's not right. Specifically I'm going to get rid of

Specifically I'm going to get rid of that value. We need to use a function

that value. We need to use a function called related table and it returns the

called related table and it returns the related table filtered so that it only

related table filtered so that it only includes the related rows. And then from

includes the related rows. And then from there we can insert in skill job dim.

there we can insert in skill job dim. Put two closing parentheses on here.

Put two closing parentheses on here. Press enter. Wa bing bang. We have an

Press enter. Wa bing bang. We have an answer. This is pretty neat. And it says

answer. This is pretty neat. And it says what skills or how many skills are

what skills or how many skills are associated for each of these different

associated for each of these different jobs. This is once again demonstrating

jobs. This is once again demonstrating that row context analysis.

Now after row context, the next thing that's evaluated is the query context.

that's evaluated is the query context. And this determines which rows from a

And this determines which rows from a table are included in a calculation

table are included in a calculation based on the filtered selection and

based on the filtered selection and visuals, relationships between tables,

visuals, relationships between tables, and then slicers and cross filtering.

and then slicers and cross filtering. So, let's demonstrate query context that

So, let's demonstrate query context that can be used to filter these visuals. We

can be used to filter these visuals. We can do something like drag a slicer into

can do something like drag a slicer into here. And I'm going to drag job tile

here. And I'm going to drag job tile shorten here. Anyway, I can adjust the

shorten here. Anyway, I can adjust the query context by changing which values

query context by changing which values we want to see. So, we can see business

we want to see. So, we can see business analyst or data analyst. And this query

analyst or data analyst. And this query context will filter us down to in our

context will filter us down to in our case, we select the data analyst. It

case, we select the data analyst. It modifies the what this visual is going

modifies the what this visual is going to show. Other ways we could do this of

to show. Other ways we could do this of adjusting the query context is actually

adjusting the query context is actually going into the filters and selecting

going into the filters and selecting what we want it to show here on the

what we want it to show here on the page. This applies this query context

page. This applies this query context plays the filters on this visual filters

plays the filters on this visual filters on this page and filters on all pages.

on this page and filters on all pages. And the other thing that query context

And the other thing that query context is controlled by is cross filtering. So

is controlled by is cross filtering. So if I select something like a machine

if I select something like a machine learning engineer, it will cross filter

learning engineer, it will cross filter there. This has applies a query context

there. This has applies a query context of machine learning engineer. Query

of machine learning engineer. Query context in my opinion is a little bit

context in my opinion is a little bit more abstract in that this is the

more abstract in that this is the specification sent by the visual itself

specification sent by the visual itself whether doing that cross filtering

whether doing that cross filtering filtering or even using slicers.

The last one to discuss is filter context and filter context is applied on

context and filter context is applied on top of query context and on top of row

top of query context and on top of row context. We're going to demonstrate this

context. We're going to demonstrate this shortly in that we can explicitly modify

shortly in that we can explicitly modify using DAX functions like calculate in

using DAX functions like calculate in order to control this filter context and

order to control this filter context and thus undo things that are within the

thus undo things that are within the query or even row context. That's why

query or even row context. That's why we're just covering this cuz you need to

we're just covering this cuz you need to understand there's different contexts.

understand there's different contexts. So let's demonstrate this filter

So let's demonstrate this filter context. And for this we're going to be

context. And for this we're going to be using our skill count column that we

using our skill count column that we just created. Remember, skill count is

just created. Remember, skill count is calculated at the row context level

calculated at the row context level because it's a calculated column. And

because it's a calculated column. And what we're going to be building with is

what we're going to be building with is this visualization here where we can get

this visualization here where we can get a sum of that skill count calculated

a sum of that skill count calculated column based on a different job title.

column based on a different job title. But we can modify the filter context. If

But we can modify the filter context. If you notice here, we get 2.2 million

you notice here, we get 2.2 million grand total skill count. It's basically

grand total skill count. It's basically undoing that query context and modifying

undoing that query context and modifying that filter context. Anyway, let's jump

that filter context. Anyway, let's jump into it. Anyway, inside of here, I'm

into it. Anyway, inside of here, I'm going to insert another matrix down at

going to insert another matrix down at the bottom. Remember, we want to do this

the bottom. Remember, we want to do this by the job title short on the rows. And

by the job title short on the rows. And previously, right, we created that skill

previously, right, we created that skill count calculated column. And I'm going

count calculated column. And I'm going to drag that into the values. I'm going

to drag that into the values. I'm going to put this into focus mode so we can

to put this into focus mode so we can see it. I'm not liking how this

see it. I'm not liking how this formatted, so I'm going to select skill

formatted, so I'm going to select skill count. We're going to change this to put

count. We're going to change this to put a comma there. We're also going to sort

a comma there. We're also going to sort this from high to low. Okay, so this is

this from high to low. Okay, so this is showing us the counts of the skills

showing us the counts of the skills based on the different job title. We can

based on the different job title. We can also confirm this. I'm going to remove

also confirm this. I'm going to remove this, but I can drag in skill count from

this, but I can drag in skill count from our measures and they are again the same

our measures and they are again the same values. So we are confirming that our

values. So we are confirming that our calculated column is correct. Anyway,

calculated column is correct. Anyway, let's say we want a column that has the

let's say we want a column that has the total counts of skills in there. For

total counts of skills in there. For this one, we're going to create a new

this one, we're going to create a new measure and we call this grand total

measure and we call this grand total skill count. And I'm going shift enter

skill count. And I'm going shift enter down. And so normally we would do

down. And so normally we would do something like this. We would do sum of.

something like this. We would do sum of. Remember we created that calculated

Remember we created that calculated column skill count. So I can go ahead

column skill count. So I can go ahead and insert that in here. Close it. Press

and insert that in here. Close it. Press enter. I'm going to just drag it in.

enter. I'm going to just drag it in. This is not what we want just yet. We

This is not what we want just yet. We still have to do modified. I'm going to

still have to do modified. I'm going to drag it into the values. But it's doing

drag it into the values. But it's doing that calculation here. I'm not liking

that calculation here. I'm not liking how it's formatted. I'm going to format

how it's formatted. I'm going to format it correctly real quick. Anyway, that's

it correctly real quick. Anyway, that's not what we want, right? We want to get

not what we want, right? We want to get it to this where we actually see the

it to this where we actually see the grand total skill count popping up.

grand total skill count popping up. Basically modifying that filter context.

Basically modifying that filter context. Well, for this we're going to use a very

Well, for this we're going to use a very popular function that you need to get

popular function that you need to get down and that is called calculate. This

down and that is called calculate. This evaluates an expression within a

evaluates an expression within a modified filtered context. Hence, we can

modified filtered context. Hence, we can modify our filter context. So, this

modify our filter context. So, this allows us to do it. Now, calculate is

allows us to do it. Now, calculate is pretty simple in how we're going to

pretty simple in how we're going to execute it. Let's actually type it out

execute it. Let's actually type it out instead of looking at it here. I don't

instead of looking at it here. I don't like how it's written. I'm going to

like how it's written. I'm going to shift enter down. Type in calculate and

shift enter down. Type in calculate and then open parenthesis. All right. The

then open parenthesis. All right. The first thing is it accepts an expression.

first thing is it accepts an expression. In our case, this formula that we put in

In our case, this formula that we put in right here, this is an expression. I'm

right here, this is an expression. I'm going to tab it over. So that is our

going to tab it over. So that is our expression. And then I'm going to put a

expression. And then I'm going to put a comma. And then we can apply a filter

comma. And then we can apply a filter after that. And that's actually optional

after that. And that's actually optional as noted in the square brackets. So,

as noted in the square brackets. So, what I'm going to do is I'm just going

what I'm going to do is I'm just going to shift enter down and put in another

to shift enter down and put in another parenthesis. Press enter. Anyway, if we

parenthesis. Press enter. Anyway, if we notice after running this, this grand

notice after running this, this grand total skill count did not change. It

total skill count did not change. It still correlates. So, the formula still

still correlates. So, the formula still works the same using calculate. But now

works the same using calculate. But now I want to put on a filter into it.

I want to put on a filter into it. Filters are pretty easy and you probably

Filters are pretty easy and you probably know how to write them yourself. I'm

know how to write them yourself. I'm going to do shift enter. I could do a

going to do shift enter. I could do a filter something like this where I want

filter something like this where I want to filter the job title short to be

to filter the job title short to be equal to data analyst and then running

equal to data analyst and then running this we can see from this that all these

this we can see from this that all these values here are equal to that of the

values here are equal to that of the data analyst so that filter context

data analyst so that filter context overwrites it but we want to get that

overwrites it but we want to get that grand total so we want to look at the

grand total so we want to look at the entire table if you will so we can use

entire table if you will so we can use this function all it's also a popular

this function all it's also a popular function that you should know it returns

function that you should know it returns all rows in a table or values in a

all rows in a table or values in a column, ignoring any filters that may

column, ignoring any filters that may have been applied. So, I'm going to

have been applied. So, I'm going to remove this filter that we just made.

remove this filter that we just made. I'm going to type in that all function,

I'm going to type in that all function, and it takes a table name or column

and it takes a table name or column name. Our calculated column is in that

name. Our calculated column is in that job postings fact table. So, we're going

job postings fact table. So, we're going to go ahead and put this. I'm going to

to go ahead and put this. I'm going to press enter. And bam, now we have that

press enter. And bam, now we have that value overriding and inputting it

value overriding and inputting it through our filter context. So, in

through our filter context. So, in recap, there are three different types

recap, there are three different types of context. filter context which we most

of context. filter context which we most recently covered has the highest

recently covered has the highest precedence. It overrides query and row

precedence. It overrides query and row context and you explicitly modify

context and you explicitly modify calculation environment using functions

calculation environment using functions like calculate like we did. Next up is

like calculate like we did. Next up is query context. It determines what subset

query context. It determines what subset of data to include based on the visual

of data to include based on the visual selection using things like cross filter

selection using things like cross filter filtering or even inside of a matrix how

filtering or even inside of a matrix how it can come out there. query context is

it can come out there. query context is going to override our row context level

going to override our row context level which has the lowest precedence and it

which has the lowest precedence and it operates at that individual row level

operates at that individual row level like we demonstrated with calculated

like we demonstrated with calculated columns. Now this is an advanced topic

columns. Now this is an advanced topic but it's important that you understand

but it's important that you understand what's going on here because you're

what's going on here because you're going to get yourself into trouble if

going to get yourself into trouble if you don't understand this precedence and

you don't understand this precedence and you start building more complex

you start building more complex calculations with DAX. Trust me, I've

calculations with DAX. Trust me, I've gotten myself into plenty of trouble

gotten myself into plenty of trouble with this.

with this. Now, let's put this knowledge of

Now, let's put this knowledge of different contexts to the test by

different contexts to the test by building this visual here, which we're

building this visual here, which we're going to be able to break down basically

going to be able to break down basically what is the median yearly salary for all

what is the median yearly salary for all job postings and what is using modifying

job postings and what is using modifying our filter context, what is the median

our filter context, what is the median yearly salary of only the US and we're

yearly salary of only the US and we're going to be able to evaluate this at a

going to be able to evaluate this at a skill count level. So, let's get into

skill count level. So, let's get into building it. For this, I'm going to

building it. For this, I'm going to remove some room and I'm going to remove

remove some room and I'm going to remove the slicer. So, we've already calculated

the slicer. So, we've already calculated this median yearly salary. I want to now

this median yearly salary. I want to now calculate what is the median year uh

calculate what is the median year uh yearly salary just for the United

yearly salary just for the United States. You can also just modify this to

States. You can also just modify this to any country that you that you live in.

any country that you that you live in. Um so, feel free to do that if you want

Um so, feel free to do that if you want to. Anyway, what I'm going to do to keep

to. Anyway, what I'm going to do to keep things simple, I'm just going to copy

things simple, I'm just going to copy all this here and inside of measures,

all this here and inside of measures, I'm going to create a new measure. I'm

I'm going to create a new measure. I'm going to paste it in. We want to do this

going to paste it in. We want to do this for the US. So this is my new measure

for the US. So this is my new measure and I'll modify the comment so that way

and I'll modify the comment so that way it says for the United States. Now

it says for the United States. Now remember in order to do this we going we

remember in order to do this we going we are going to use the calculate function.

are going to use the calculate function. So I'm going to type in calculate and

So I'm going to type in calculate and then shift enter down. We want to do the

then shift enter down. We want to do the median value of salary year average and

median value of salary year average and we want to put a filter on it. So I'm

we want to put a filter on it. So I'm going shift enter down for that. And for

going shift enter down for that. And for that filter, we want to make sure that

that filter, we want to make sure that the job country is equal to the United

the job country is equal to the United States. If you do a different country,

States. If you do a different country, you need to make sure that you spell it

you need to make sure that you spell it correctly. Okay, I'm going to go ahead

correctly. Okay, I'm going to go ahead and press shift enter and then close the

and press shift enter and then close the parenthesis and press enter. So, let's

parenthesis and press enter. So, let's actually see this bad boy in action. I'm

actually see this bad boy in action. I'm going to create a clustered bar chart

going to create a clustered bar chart down here. And I'm going drag the median

down here. And I'm going drag the median yearly salary into the x- axis and

yearly salary into the x- axis and median yearly salary for the US also in

median yearly salary for the US also in here. Making it slightly bigger. We can

here. Making it slightly bigger. We can see which is pretty interesting. The

see which is pretty interesting. The median salary for the US is slightly

median salary for the US is slightly lower. Also not liking how this number's

lower. Also not liking how this number's formatted. I'm going select this format

formatted. I'm going select this format as currency and change this to zero

as currency and change this to zero decimal places. Now, one thing to note

decimal places. Now, one thing to note with this calculation that we currently

with this calculation that we currently have, median yearly salary, I retyped in

have, median yearly salary, I retyped in this right here, which if we look at the

this right here, which if we look at the median yearly salary, it is the same

median yearly salary, it is the same calculation. And it looks like it does

calculation. And it looks like it does hint uh this red highlighting mainly

hint uh this red highlighting mainly because normally it's not written like

because normally it's not written like this with this extra spacing. I just did

this with this extra spacing. I just did that for demo purposes earlier. So, I'm

that for demo purposes earlier. So, I'm going to clean that up real quick. Press

going to clean that up real quick. Press enter. Anyway, this is the same thing.

enter. Anyway, this is the same thing. So inside of this median yearly salary

So inside of this median yearly salary what would actually be better practice

what would actually be better practice instead of using this is referencing

instead of using this is referencing directly that other measure itself it is

directly that other measure itself it is still an expression whenever I press

still an expression whenever I press enter the value is still the same um so

enter the value is still the same um so I know it's working and yeah and so this

I know it's working and yeah and so this case we're still using that filter

case we're still using that filter context to filter down to this now we

context to filter down to this now we want to get it by the count of skills so

want to get it by the count of skills so in that job postings t fact table I'm

in that job postings t fact table I'm going to drag in the skill count to the

going to drag in the skill count to the y-axis. Now, it goes all the way up to

y-axis. Now, it goes all the way up to this level, which looks like it's like

this level, which looks like it's like 35. But when we get start getting

35. But when we get start getting higher, right, there's less values

higher, right, there's less values because the likelihood that there's 34

because the likelihood that there's 34 skills in a job posting is pretty low.

skills in a job posting is pretty low. So, I'm going to filter it down. So,

So, I'm going to filter it down. So, we'll adjust the query context by going

we'll adjust the query context by going to filters. Know it's sort of

to filters. Know it's sort of counterintuitive, but we're adjusting

counterintuitive, but we're adjusting the query context in this case. We'll

the query context in this case. We'll leave it as advanced filter, and we'll

leave it as advanced filter, and we'll say when it's less than, we'll say 15

say when it's less than, we'll say 15 values. All right, so not bad. We can

values. All right, so not bad. We can clearly see with this that for less

clearly see with this that for less skills, you get paid less money. And as

skills, you get paid less money. And as the skills go up, the pay goes up. And

the skills go up, the pay goes up. And when comparing it to the United States

when comparing it to the United States in our case, honestly, we're not seeing

in our case, honestly, we're not seeing that big of a difference. And that's

that big of a difference. And that's mainly because the United States is such

mainly because the United States is such a large portion of this data set. Feel

a large portion of this data set. Feel free to try out different countries as

free to try out different countries as well, and let me know if you find any

well, and let me know if you find any characteristics about it in the

characteristics about it in the comments.

Now, one quick refresher that we covered all the way back in the first chapter,

all the way back in the first chapter, and that's on DAX query view. You can

and that's on DAX query view. You can also use this to evaluate your different

also use this to evaluate your different measures that you've come up with and

measures that you've come up with and are creating. I just find it's a little

are creating. I just find it's a little bit more difficult as it takes some more

bit more difficult as it takes some more DAX knowledge, but you could actually do

DAX knowledge, but you could actually do this without DAX. In the case of job

this without DAX. In the case of job count, I can rightclick this and they

count, I can rightclick this and they have this quick queries. If I do

have this quick queries. If I do evaluate in the upper portion right

evaluate in the upper portion right here, it writes out the DAX in order to

here, it writes out the DAX in order to get job count below this. So just put it

get job count below this. So just put it anyway, the values down here for job

anyway, the values down here for job count. Now another option is I can

count. Now another option is I can rightclick job count, go to quick

rightclick job count, go to quick queries, I can go to define and evaluate

queries, I can go to define and evaluate making a little bit bigger room with

making a little bit bigger room with this. Now with this one, because I did

this. Now with this one, because I did define and evaluate, it still has the

define and evaluate, it still has the same syntax you notice below. But what

same syntax you notice below. But what is nice about this is they have this

is nice about this is they have this option up here of update update model

option up here of update update model overwrite measures. You can if you want

overwrite measures. You can if you want if you wanted to create or update this

if you wanted to create or update this measure like I could make this back into

measure like I could make this back into using count of job ID and then I can run

using count of job ID and then I can run it. Okay, I'm getting the same value and

it. Okay, I'm getting the same value and then I can just update the model and it

then I can just update the model and it says hey do you want to update the

says hey do you want to update the model? Are you sure? and it updates the

model? Are you sure? and it updates the model that disappears. And whenever I go

model that disappears. And whenever I go into job count when looking at it from

into job count when looking at it from something like the query view, I can see

something like the query view, I can see that okay, it did update. But I don't

that okay, it did update. But I don't actually want to update it. I'm going to

actually want to update it. I'm going to change this back to this bad boy. I'm

change this back to this bad boy. I'm going to say update model and update it.

going to say update model and update it. Here this is a really good environment

Here this is a really good environment in the case like our median yearly

in the case like our median yearly salary. Going to define and evaluate. In

salary. Going to define and evaluate. In our case, right, we had multiple

our case, right, we had multiple different levels of the formula itself.

different levels of the formula itself. This is a good case if you were getting

This is a good case if you were getting complex queries to go in and actually

complex queries to go in and actually edit it and then update the values based

edit it and then update the values based on what you need to mainly just want to

on what you need to mainly just want to share as an option because this DAX

share as an option because this DAX query view is a newer feature inside of

query view is a newer feature inside of PowerBI so some people aren't familiar

PowerBI so some people aren't familiar with it. All right, it's now your turn

with it. All right, it's now your turn to give it a try and jump in creating

to give it a try and jump in creating some different explicit measures and

some different explicit measures and messing around with those different

messing around with those different context to better understand it. With

context to better understand it. With that, there's only one more lesson left

that, there's only one more lesson left and for that we're going to be jumping

and for that we're going to be jumping into parameters and that uses DAX. With

into parameters and that uses DAX. With that, I'll see you there.

Welcome to this last lesson in DAX. And this one, pretty fun one. We're going to

this one, pretty fun one. We're going to be doing it on parameters, which these

be doing it on parameters, which these allow our end users who may not be as

allow our end users who may not be as familiar with all the intricacies of

familiar with all the intricacies of PowerBI to actually change up what

PowerBI to actually change up what inputs they have inside of a chart and

inputs they have inside of a chart and get more of what they want and explore

get more of what they want and explore the data better. Let me show you what I

the data better. Let me show you what I mean. Here I have two charts. The one on

mean. Here I have two charts. The one on the left is showing based on the job

the left is showing based on the job title the median yearly salary. The one

title the median yearly salary. The one right is showing basically the same

right is showing basically the same thing for job count. Anyway, parameters

thing for job count. Anyway, parameters are what we're going to be creating in

are what we're going to be creating in this. And this slicer up in the top

this. And this slicer up in the top allow us to select different parameters.

allow us to select different parameters. So in this case, I can change the yaxis

So in this case, I can change the yaxis from something like the job title to

from something like the job title to skill to even country or even company

skill to even country or even company allowing me to change up this view. And

allowing me to change up this view. And it's not only limited to like in this

it's not only limited to like in this case the y-axis, we could change up the

case the y-axis, we could change up the x-axis. So right now I have selected job

x-axis. So right now I have selected job count for this uh visual below. I could

count for this uh visual below. I could change it to something like median

change it to something like median yearly salary. Now, both of these

yearly salary. Now, both of these examples are what is known as a field

examples are what is known as a field parameter. Now, both of these are known

parameter. Now, both of these are known as a field parameter as we're allowed to

as a field parameter as we're allowed to input into this different column names

input into this different column names or even different measures. Now, besides

or even different measures. Now, besides those, they also have numeric

those, they also have numeric parameters, and this allows us to

parameters, and this allows us to perform more of a whatif analysis.

perform more of a whatif analysis. Here's a scenario we're going to be

Here's a scenario we're going to be doing at the second half of this lesson.

doing at the second half of this lesson. And in it, we're trying to find out,

And in it, we're trying to find out, yeah, what are the top paying jobs, but

yeah, what are the top paying jobs, but more specifically, what is our take-home

more specifically, what is our take-home pay going to be? In the United States,

pay going to be? In the United States, tax rates can be up to 35%.

tax rates can be up to 35%. And this will allow us via a slider to

And this will allow us via a slider to adjust what our different rates are for

adjust what our different rates are for this. And as you can see, it adjusts our

this. And as you can see, it adjusts our final values in the visuals below. Now,

final values in the visuals below. Now, numeric parameters are more prevalent

numeric parameters are more prevalent within forecasting scenarios and even

within forecasting scenarios and even things like financial modeling. So,

things like financial modeling. So, personally, I find this that it comes

personally, I find this that it comes second to those field parameters. So,

second to those field parameters. So, that's why we're covering this second.

Let's jump into creating our first field parameter. And in this one, I want to be

parameter. And in this one, I want to be able to view based on the median yearly

able to view based on the median yearly salary it from different types of views,

salary it from different types of views, if you will. such as job tiles, country,

if you will. such as job tiles, country, company, and also skills. So, we're

company, and also skills. So, we're going to create a parameter to do this.

going to create a parameter to do this. For this, feel free to continue to work

For this, feel free to continue to work with the report from the last lesson, or

with the report from the last lesson, or if you lost, you can just use that

if you lost, you can just use that explicit measures, and we're going to be

explicit measures, and we're going to be taking it off from there. Inside of

taking it off from there. Inside of here, I'm going to create a new page,

here, I'm going to create a new page, and I'm going to name it parameters. So,

and I'm going to name it parameters. So, parameters are accessed underneath the

parameters are accessed underneath the modeling tab. And inside of here under

modeling tab. And inside of here under new parameters, we can make either a

new parameters, we can make either a numeric range or a field parameter. Now,

numeric range or a field parameter. Now, I do want to call out something real

I do want to call out something real quick cuz you've probably seen it

quick cuz you've probably seen it before. And that's with on the home tab

before. And that's with on the home tab under transform data. Transform data. We

under transform data. Transform data. We have a section. Right now, it's grayed

have a section. Right now, it's grayed out, but we have a section called edit

out, but we have a section called edit parameters and also edit variables.

parameters and also edit variables. These, although somewhat related, aren't

These, although somewhat related, aren't the same parameters that we're going to

the same parameters that we're going to create inside of here. Specifically,

create inside of here. Specifically, this is the parameters within Power

this is the parameters within Power Query, hence why it's in that same drop

Query, hence why it's in that same drop down for transform data, which opens up

down for transform data, which opens up the Power Query editor. And these

the Power Query editor. And these parameters are similar in that you can

parameters are similar in that you can use it to change up different fields,

use it to change up different fields, but usually use them to like change to

but usually use them to like change to different data sources very easily.

different data sources very easily. Anyway, that's beyond the scope of this.

Anyway, that's beyond the scope of this. I just wanted to point it out in case

I just wanted to point it out in case you had questions about it. So, back to

you had questions about it. So, back to the modeling tab under new parameters.

the modeling tab under new parameters. We're going to do the first one first of

We're going to do the first one first of fields. If you accidentally select the

fields. If you accidentally select the wrong one, you can change it inside of

wrong one, you can change it inside of here. Anyway, we're changing back to

here. Anyway, we're changing back to fields. And for the name of this, I know

fields. And for the name of this, I know I'm going to put into a slicer. So, I

I'm going to put into a slicer. So, I give it a name that basically cues the

give it a name that basically cues the user in that they can select with this.

user in that they can select with this. So, we can do something like either

So, we can do something like either select category or select value. We'll

select category or select value. We'll just do select category. Next up, we

just do select category. Next up, we need to start dragging the applicable

need to start dragging the applicable columns that we want into there. I know

columns that we want into there. I know I want job title short job country

I want job title short job country skills specifically from the skills

skills specifically from the skills dimensional table because we want that

dimensional table because we want that name and then finally we want company

name and then finally we want company which you actually have to input in name

which you actually have to input in name if you want to get that from company

if you want to get that from company dim. It asks at the bottom do I want to

dim. It asks at the bottom do I want to add the slice to the page? You bet I do.

add the slice to the page? You bet I do. Select create. I'm going to move this

Select create. I'm going to move this slicer down just to show but we have our

slicer down just to show but we have our name of select category and then look at

name of select category and then look at all these names. Especially this one of

all these names. Especially this one of name that's not really readable like

name that's not really readable like what is that? So luckily for us we know

what is that? So luckily for us we know DAX now and this is the formula behind

DAX now and this is the formula behind how this parameter is created. Also with

how this parameter is created. Also with that over here in the right hand side of

that over here in the right hand side of the data pane we have this select

the data pane we have this select category and if we view it within the

category and if we view it within the table view we can see that it has three

table view we can see that it has three different attributes about it or three

different attributes about it or three different columns. It has the select

different columns. It has the select category column, the select category

category column, the select category fields, which it tells it what are those

fields, which it tells it what are those different columns that I should be

different columns that I should be using, and the select category order.

using, and the select category order. What order should it put it specifically

What order should it put it specifically in a slicer? Obviously, you know, we

in a slicer? Obviously, you know, we can't edit it from right here. So, we're

can't edit it from right here. So, we're going to edit it from that formula bar.

going to edit it from that formula bar. Change job title short to just job

Change job title short to just job title, job country to country, skills to

title, job country to country, skills to just skills with a capital S, and then

just skills with a capital S, and then name to company. Pressing enter, see our

name to company. Pressing enter, see our slicer updates below. If I want to

slicer updates below. If I want to change the uh the order in this slicer,

change the uh the order in this slicer, say I wanted skills actually second,

say I wanted skills actually second, which we need to put it number one for

which we need to put it number one for this one and then country uh third. So

this one and then country uh third. So that one needs to be two. Got to love

that one needs to be two. Got to love the index of zero. I can do that and

the index of zero. I can do that and then it updates below as well. All

then it updates below as well. All right, so let's now use this in a

right, so let's now use this in a visualization. I'm going to put select

visualization. I'm going to put select category up in the top and then put a

category up in the top and then put a bar chart underneath it. We want to look

bar chart underneath it. We want to look at the median yearly salary. So, I'll

at the median yearly salary. So, I'll take that and drag that into the x-axis.

take that and drag that into the x-axis. And then normally remember we go for

And then normally remember we go for like the job postings fact table. Drag

like the job postings fact table. Drag in job title shortened to here. But that

in job title shortened to here. But that doesn't help us here, right? Because we

doesn't help us here, right? Because we want to be able to control the actual

want to be able to control the actual view within here. So, we have to add

view within here. So, we have to add this to this visualization. So, I'm

this to this visualization. So, I'm going uncheck this and we want to go to

going uncheck this and we want to go to that select category here. Specifically,

that select category here. Specifically, this of select category, put it into the

this of select category, put it into the yaxis and bam, here it is below. And now

yaxis and bam, here it is below. And now whenever I select different options. So

whenever I select different options. So skills it updates for the skills the

skills it updates for the skills the country and then also company. Now you

country and then also company. Now you can't do multiple values with this. So

can't do multiple values with this. So we need to change this visual to make

we need to change this visual to make sure that it our users work with this

sure that it our users work with this properly. So under format visual I'm

properly. So under format visual I'm going to go into our slicer settings and

going to go into our slicer settings and I'm going to change this to a title

I'm going to change this to a title along with under selection going to make

along with under selection going to make sure that only single select is allowed.

sure that only single select is allowed. Now, we've only demonstrated the y-axis

Now, we've only demonstrated the y-axis in this or the categories. What if we

in this or the categories. What if we want to change this one down here with

want to change this one down here with specifically with different measures we

specifically with different measures we have built? Well, let's create a

have built? Well, let's create a parameter for that. And for this, we're

parameter for that. And for this, we're going to be switching between median

going to be switching between median yearly salary and the total job count.

yearly salary and the total job count. So, under modeling, new parameters,

So, under modeling, new parameters, we'll go to fields. For the name, we'll

we'll go to fields. For the name, we'll use select measure. And inside of here,

use select measure. And inside of here, we're going to drag in that job count

we're going to drag in that job count and then also that median yearly salary

and then also that median yearly salary and click create. These names are

and click create. These names are already good enough for how I like it.

already good enough for how I like it. This is looking good. I do want to

This is looking good. I do want to format this visual in the fact that I

format this visual in the fact that I only want it to be a single select. And

only want it to be a single select. And then also let's make it look similar

then also let's make it look similar being a tile. So now we can switch

being a tile. So now we can switch between this median yearly salary and

between this median yearly salary and this job count. But it's not doing

this job count. But it's not doing anything on our visual. I can create

anything on our visual. I can create another visual. But I actually want to

another visual. But I actually want to demonstrate how we can use this on this

demonstrate how we can use this on this visual right here. So I'm going to

visual right here. So I'm going to remove median yearly salary. And then

remove median yearly salary. And then for select measure, I'm going to drag it

for select measure, I'm going to drag it into the x-axis. I'm going to make this

into the x-axis. I'm going to make this a little bit bigger now. But now I can

a little bit bigger now. But now I can switch up between median yearly salary

switch up between median yearly salary and job count. And we can do this for

and job count. And we can do this for country, company, median, yearly salary.

country, company, median, yearly salary. This is pretty neat and allows a dynamic

This is pretty neat and allows a dynamic access and manipulation of our

access and manipulation of our visualizations. Now, one quick note.

visualizations. Now, one quick note. This one here of select measures.

This one here of select measures. Remember, we're doing the aggregation

Remember, we're doing the aggregation with a measure, but I'm going to demo

with a measure, but I'm going to demo something real quick. You don't have to

something real quick. You don't have to follow along with this portion. I'm

follow along with this portion. I'm going to demo something that doesn't

going to demo something that doesn't work that you need to be aware of when

work that you need to be aware of when building this. Specifically, let's say

building this. Specifically, let's say we're creating a new parameter. We'll

we're creating a new parameter. We'll say it's a field as well, right? And we

say it's a field as well, right? And we wanted to select between yearly and

wanted to select between yearly and hourly salary. Should be yearly or

hourly salary. Should be yearly or hourly salary. Anyway, inside our job

hourly salary. Anyway, inside our job postings fact table, right, we do have

postings fact table, right, we do have this salary hour average and that salary

this salary hour average and that salary year average. I'm going to go ahead and

year average. I'm going to go ahead and just create this. Now, let's say with

just create this. Now, let's say with this slicer, let's say we were just

this slicer, let's say we were just replacing this one up here, and we want

replacing this one up here, and we want to put it inside of this visualization.

to put it inside of this visualization. So, I come to select salary yearly or

So, I come to select salary yearly or hourly, remove the select measures, and

hourly, remove the select measures, and I drag this into the x-axis. Okay, this

I drag this into the x-axis. Okay, this is not going to work. And even when I

is not going to work. And even when I select these different ones, it's not

select these different ones, it's not going to work. And this is because right

going to work. And this is because right these are columns and we're trying to do

these are columns and we're trying to do an aggregation on the xaxis. Previously,

an aggregation on the xaxis. Previously, we were doing either count of jobs or a

we were doing either count of jobs or a median of the salaries. It doesn't know

median of the salaries. It doesn't know what to do by default and so it's not

what to do by default and so it's not even going to make a visual and it's

even going to make a visual and it's going to end up breaking. So going to

going to end up breaking. So going to get rid of this and drag select measures

get rid of this and drag select measures back in like we liked. Additionally, I'm

back in like we liked. Additionally, I'm not keeping this cuz it's broken. So I'm

not keeping this cuz it's broken. So I'm going to remove it. And also I'm going

going to remove it. And also I'm going to delete this from our model because it

to delete this from our model because it doesn't work. All right, so bam back to

doesn't work. All right, so bam back to working properly.

Next up, let's get into a numeric parameter. And for this, we're going to

parameter. And for this, we're going to be building this one here on selecting

be building this one here on selecting the deduction rate. Now, this one we're

the deduction rate. Now, this one we're going to have to do a little bit more

going to have to do a little bit more works because yes, we can set up our

works because yes, we can set up our numeric parameter very similar to how we

numeric parameter very similar to how we set up our field parameter, but then we

set up our field parameter, but then we actually have to implement it into a DAX

actually have to implement it into a DAX calculation or an explicit measure in

calculation or an explicit measure in order to calculate what we're trying to

order to calculate what we're trying to do with this deduction rate. Basically,

do with this deduction rate. Basically, we're trying to take a deduction of the

we're trying to take a deduction of the median yearly salary. So, this one's

median yearly salary. So, this one's going to take multiple steps. So, I

going to take multiple steps. So, I created a new page and then under new

created a new page and then under new parameters, we're going to create that

parameters, we're going to create that numeric range. We'll call this select

numeric range. We'll call this select deduction rate. For the minimum, we're

deduction rate. For the minimum, we're going to be doing zero. Maximum, we'll

going to be doing zero. Maximum, we'll go to we'll say 50%, but in this case,

go to we'll say 50%, but in this case, we need to put a decimal. So 0.5. And

we need to put a decimal. So 0.5. And then for the increments, we're going to

then for the increments, we're going to do that of 0.01

do that of 0.01 increments. For this, the typical tax

increments. For this, the typical tax rate, at least in the United States, is

rate, at least in the United States, is 20%, so we'll do 0.2 for this. So me,

20%, so we'll do 0.2 for this. So me, I'm noticing the red values. I skipped

I'm noticing the red values. I skipped over this. The data type, we have to

over this. The data type, we have to make sure that this is a decimal number.

make sure that this is a decimal number. Anyway, all those red boxes are cleared.

Anyway, all those red boxes are cleared. We want to add a slicer to this page.

We want to add a slicer to this page. Create. Okay. I'm going to drag that

Create. Okay. I'm going to drag that across the top here. Next up, let's

across the top here. Next up, let's create a visualization that we can

create a visualization that we can actually use this on. So, I'm going to

actually use this on. So, I'm going to insert in a stack bar chart and we'll

insert in a stack bar chart and we'll have median yearly salary on the x-axis

have median yearly salary on the x-axis and then a job title short on the yaxis.

and then a job title short on the yaxis. I'm going to change this name to job

I'm going to change this name to job title. Now we need to go into

title. Now we need to go into implementing this into where we want to

implementing this into where we want to take whatever deduction rate we select

take whatever deduction rate we select from here from our slicer to basically

from here from our slicer to basically deduct from our median yearly salary

deduct from our median yearly salary here. One quick note, notice I made an

here. One quick note, notice I made an error. I have the stack bar chart. We

error. I have the stack bar chart. We want these the new measure that we

want these the new measure that we created right next to it. So I'm going

created right next to it. So I'm going to change this to a clustered bar chart.

to change this to a clustered bar chart. Should be no change visually. So let's

Should be no change visually. So let's get into creating this measure using

get into creating this measure using median yearly salary. So I'm going to

median yearly salary. So I'm going to say I want a new measure. We'll call

say I want a new measure. We'll call this median yearly take-home pay. Press

this median yearly take-home pay. Press shift enter to get down. And for this,

shift enter to get down. And for this, we want to use that median yearly salary

we want to use that median yearly salary value. And then we want to subtract our

value. And then we want to subtract our deduction rate. So, we're going to do a

deduction rate. So, we're going to do a little bit of algebra here. We're going

little bit of algebra here. We're going to multiply times 1 minus that deduction

to multiply times 1 minus that deduction rate. But what the heck is that? How how

rate. But what the heck is that? How how do we get this deduction rate in here?

do we get this deduction rate in here? Well, if we go to that select deduction

Well, if we go to that select deduction rate right here, and it's giving me an

rate right here, and it's giving me an error right now because I exited out of

error right now because I exited out of that. It's fine. I have notice compared

that. It's fine. I have notice compared to the other ones of select category,

to the other ones of select category, they only have one value, but select

they only have one value, but select deduction rate has two values. And this

deduction rate has two values. And this second one right here is, we can see by

second one right here is, we can see by the icon, a measure, and that's what we

the icon, a measure, and that's what we want to use. We want to use that select

want to use. We want to use that select deduction rate. So, typing in select

deduction rate. So, typing in select deduction rate, it's popping right up.

deduction rate, it's popping right up. I'm going to do that and then close the

I'm going to do that and then close the parentheses. So, just so you're aware of

parentheses. So, just so you're aware of what's going on here, right? If I have a

what's going on here, right? If I have a deduction rate of 20 or 0.2, we're going

deduction rate of 20 or 0.2, we're going to do 1 minus.2 that gives8.8

to do 1 minus.2 that gives8.8 times our median yearly salary. Makes

times our median yearly salary. Makes sense. Let's press enter. Now, whenever

sense. Let's press enter. Now, whenever we do this and move this, nothing's

we do this and move this, nothing's going to happen because we haven't added

going to happen because we haven't added to our chart. So, with our visual

to our chart. So, with our visual selected, I'm going add median yearly

selected, I'm going add median yearly take-home pay underneath here. And now

take-home pay underneath here. And now whenever we adjust this we can see what

whenever we adjust this we can see what it needs to be. So at 0% just testing it

it needs to be. So at 0% just testing it out both of these are equal as expected

out both of these are equal as expected and then doing a 50% it is half of this.

and then doing a 50% it is half of this. So pretty neat implementation of numeric

So pretty neat implementation of numeric parameters.

Now DAX has a lot more features to cover more than I can cover in this video.

more than I can cover in this video. Frankly, I can make a whole course about

Frankly, I can make a whole course about it and I did actually twice on data camp

it and I did actually twice on data camp specifically. I have two other courses I

specifically. I have two other courses I would recommend after this if you want

would recommend after this if you want to learn more about DAX. The first one

to learn more about DAX. The first one is just DAX functions in PowerBI. I'm

is just DAX functions in PowerBI. I'm listed down here as the collaborator. I

listed down here as the collaborator. I was basically the brains behind the

was basically the brains behind the scenes putting this course together. And

scenes putting this course together. And this goes into all the basics of DAX.

this goes into all the basics of DAX. When we get into iterating functions in

When we get into iterating functions in chapter 4 of this module or of this

chapter 4 of this module or of this course, that's when we start covering

course, that's when we start covering more new stuff that we didn't cover in

more new stuff that we didn't cover in here. The second course that I helped

here. The second course that I helped create was intermediate DAX and PowerBI.

create was intermediate DAX and PowerBI. This one has a lot of things in here

This one has a lot of things in here that we didn't even cover in this course

that we didn't even cover in this course that are frankly intermediate. Here I am

that are frankly intermediate. Here I am listed as collaborator down here.

listed as collaborator down here. Anyway, this would also be a great

Anyway, this would also be a great option to dive into next if you want to

option to dive into next if you want to learn more with DAX. I may consider in

learn more with DAX. I may consider in the future building a second PowerBI

the future building a second PowerBI course, basically getting into advanced

course, basically getting into advanced DAXs and encompassing all these

DAXs and encompassing all these different other techniques. So, if

different other techniques. So, if you're interested that, let me know in

you're interested that, let me know in the comments below. Anyway, that was our

the comments below. Anyway, that was our last lesson of actually learning what to

last lesson of actually learning what to do with PowerBI. We're now going to be

do with PowerBI. We're now going to be jumping next into the final project,

jumping next into the final project, applying all those different concepts

applying all those different concepts we've learned, and building something

we've learned, and building something out super special. Oh, I forgot to

out super special. Oh, I forgot to mention you do have some practice

mention you do have some practice problems, your last set of practice

problems, your last set of practice problems to go through and test out

problems to go through and test out parameters with. Anyway, and with that,

parameters with. Anyway, and with that, actually, see you in the next one.

All right, welcome to this final section where we're going to be tackling our

where we're going to be tackling our last project. And this is going to be

last project. And this is going to be broken into two videos. First one is

broken into two videos. First one is actually building out our dashboard and

actually building out our dashboard and the second one is getting into my

the second one is getting into my recommended ways for sharing it. Now,

recommended ways for sharing it. Now, I'm going to start with this. You can

I'm going to start with this. You can feel free to follow along in this video

feel free to follow along in this video and build out the recommendations that

and build out the recommendations that I'm going to recommend for building out

I'm going to recommend for building out our second project, but I highly

our second project, but I highly recommend that instead you if you want

recommend that instead you if you want to, you can watch it, but I recommend

to, you can watch it, but I recommend just skipping it and you dive deep and

just skipping it and you dive deep and build out your own dashboard that you

build out your own dashboard that you find usable and beneficial to you. Truth

find usable and beneficial to you. Truth be told, I'm not going to be there

be told, I'm not going to be there holding your hand in the real world,

holding your hand in the real world, guiding you along on what you need to

guiding you along on what you need to do. So, you need to take the initiative

do. So, you need to take the initiative now and start coming up with ideas on

now and start coming up with ideas on how you can actually build effective

how you can actually build effective visualizations. So, with that, I'm going

visualizations. So, with that, I'm going to provide first some constructive

to provide first some constructive feedback on our last project. That way,

feedback on our last project. That way, it can maybe inspire some ideas on what

it can maybe inspire some ideas on what you could build out. Then, from there,

you could build out. Then, from there, I'm going to build out what I want to

I'm going to build out what I want to actually build.

So, let's get back into some feedback that I have on this first dashboard. And

that I have on this first dashboard. And as a reminder, remember it was two

as a reminder, remember it was two pages. We have our first actual landing

pages. We have our first actual landing page dashboard, and then in the second

page dashboard, and then in the second page, we're allowed to drill through

page, we're allowed to drill through into particular job titles so we can

into particular job titles so we can dive deeper into insights. Anyway, I've

dive deeper into insights. Anyway, I've been using this bad boy while going

been using this bad boy while going through and building this course, and I

through and building this course, and I have some thoughts on things I want to

have some thoughts on things I want to improve on it based on my real world

improve on it based on my real world experience with implementing dashboards.

experience with implementing dashboards. Anyway, the first thing is this. The

Anyway, the first thing is this. The KPIs, specifically those cards up at the

KPIs, specifically those cards up at the top on the main dashboard, are really

top on the main dashboard, are really beneficial. I really like them, and I

beneficial. I really like them, and I like that they provide value immediately

like that they provide value immediately to me on what I'm looking for. Now,

to me on what I'm looking for. Now, regarding all the other visualizations,

regarding all the other visualizations, they're great, but I particularly like

they're great, but I particularly like the visuals on hourly and yearly salary.

the visuals on hourly and yearly salary. This scatter plot, not going to lie,

This scatter plot, not going to lie, it's a little hard to read, but I do

it's a little hard to read, but I do like down here these different plots or

like down here these different plots or these different charts we've implemented

these different charts we've implemented into our matrix to show this. We don't

into our matrix to show this. We don't need to necessarily do it like this, but

need to necessarily do it like this, but maybe we could use parameters to build

maybe we could use parameters to build something with this. Here's some other

something with this. Here's some other areas that I want to tweak. The KPIs or

areas that I want to tweak. The KPIs or the cards I said I did like, but that

the cards I said I did like, but that average job rating, it's very unclear on

average job rating, it's very unclear on what actually is going on there. and

what actually is going on there. and frankly is biased to what I had set up

frankly is biased to what I had set up for this fivestar system. Also looking

for this fivestar system. Also looking at it holistically with the main page

at it holistically with the main page and also with that drill through page,

and also with that drill through page, there's just too many visuals for my

there's just too many visuals for my liking. I want to drill it down to only

liking. I want to drill it down to only the core visuals necessary. And although

the core visuals necessary. And although the drill through feature was really

the drill through feature was really nice and fancy, in my practice, I'm not

nice and fancy, in my practice, I'm not seeing it getting used as much unless

seeing it getting used as much unless you're actually training your users on

you're actually training your users on it. So here are my top features that I

it. So here are my top features that I definitely want to include on this. I

definitely want to include on this. I want to focus on two things mainly

want to focus on two things mainly skills and then salary of jobs. For the

skills and then salary of jobs. For the skill data I want to show pre like we

skill data I want to show pre like we showed previously the counts but also it

showed previously the counts but also it in a relative percentage like how many

in a relative percentage like how many jobs are requesting Python. And then for

jobs are requesting Python. And then for the salary I want to do this for job

the salary I want to do this for job titles and I want to be able to easily

titles and I want to be able to easily flip between analyzing for hourly or

flip between analyzing for hourly or yearly. Now, the last two things are

yearly. Now, the last two things are things that I've actually gotten

things that I've actually gotten insights from this website of my dad.te

insights from this website of my dad.te and that was this country filter was a

and that was this country filter was a really big demand in my subscribers. I

really big demand in my subscribers. I initially when I built this didn't have

initially when I built this didn't have this and everybody was commenting

this and everybody was commenting include a country filter. Also, I really

include a country filter. Also, I really like the well the aesthetics of this

like the well the aesthetics of this specifically how it's more of a dark

specifically how it's more of a dark theme with it. So, that's the last two

theme with it. So, that's the last two things. We're going to add a country

things. We're going to add a country slicer and also we're going to be using

slicer and also we're going to be using dark mode.

So, let's get into rough drafting out this dashboard. As a reminder, you're

this dashboard. As a reminder, you're following along with me. Completely

following along with me. Completely optional. However you want to do it,

optional. However you want to do it, feel free to do it and modify as

feel free to do it and modify as necessary. Anyway, in the first

necessary. Anyway, in the first dashboard that we built, I actually

dashboard that we built, I actually physically wrote this out. Yeah, I know

physically wrote this out. Yeah, I know this is my bad handwriting. And I

this is my bad handwriting. And I actually physically drew this out, but I

actually physically drew this out, but I don't always do this. Instead, I like to

don't always do this. Instead, I like to sometimes just play around in PowerBI

sometimes just play around in PowerBI shaping things and rough drafting it

shaping things and rough drafting it that way. So, that's what we're going to

that way. So, that's what we're going to do for this one. For this, you can start

do for this one. For this, you can start off with the file that we used in the

off with the file that we used in the last lesson, which if you got lost along

last lesson, which if you got lost along the way, just go into that DAX folder is

the way, just go into that DAX folder is that last one of parameters. We want to

that last one of parameters. We want to use that same data model that we created

use that same data model that we created previously. And so all of that different

previously. And so all of that different work we're going to keep. I'm just going

work we're going to keep. I'm just going to end up getting rid of all these

to end up getting rid of all these different sheets towards the end.

different sheets towards the end. Anyway, I'm going to start a new page

Anyway, I'm going to start a new page here and call it data jobs 2.0. Now,

here and call it data jobs 2.0. Now, we're going to be implementing a dark

we're going to be implementing a dark theme on this. So, I'm going to go ahead

theme on this. So, I'm going to go ahead actually right now into view and under

actually right now into view and under themes I'm going to change it to I

themes I'm going to change it to I really like this theme right here, this

really like this theme right here, this innovate. And I'm going to change it to

innovate. And I'm going to change it to this. But I'll be honest, this isn't

this. But I'll be honest, this isn't dark theme enough for me specifically. I

dark theme enough for me specifically. I want to work in a dark theme too, too.

want to work in a dark theme too, too. So, going into files and under options

So, going into files and under options and settings and options, we can go into

and settings and options, we can go into report settings and I'm going to

report settings and I'm going to actually change my layout from this

actually change my layout from this portion to a dark theme. Bam. All right.

portion to a dark theme. Bam. All right. Now we're ready to build with dark

Now we're ready to build with dark theme. So remember my two main areas

theme. So remember my two main areas that I want on this on features. I want

that I want on this on features. I want one area or one half focusing on skills

one area or one half focusing on skills and then the other half focusing on

and then the other half focusing on salary. So I'm going to put two visuals

salary. So I'm going to put two visuals on there to get started. Personally I

on there to get started. Personally I prioritize skills over salary because

prioritize skills over salary because you need the skills to get the salary.

you need the skills to get the salary. So stealing this from our skills stats

So stealing this from our skills stats page that we built. I'm gonna take this

page that we built. I'm gonna take this visual, control C, and then put this in

visual, control C, and then put this in here. I'm going to lower it some because

here. I'm going to lower it some because like you like before, we want a great

like you like before, we want a great design layout where we have the KPIs up

design layout where we have the KPIs up at top and then the visuals below. Other

at top and then the visuals below. Other thing is I want to make sure this is

thing is I want to make sure this is centered. All right, looking good. Next

centered. All right, looking good. Next thing I want is remember those salaries.

thing I want is remember those salaries. And for this one, I'm going to use that

And for this one, I'm going to use that new measure check page that we have

new measure check page that we have created right here. And I'm going to

created right here. And I'm going to copy this one down here that has all

copy this one down here that has all these different ones in here. And then

these different ones in here. And then paste it in. I'm not realizing I should

paste it in. I'm not realizing I should have got the skills one from there, too,

have got the skills one from there, too, because right now this job count isn't

because right now this job count isn't using our measure that we created. So,

using our measure that we created. So, I'm going to actually delete that out of

I'm going to actually delete that out of there and drag job count into here. And

there and drag job count into here. And then update that y-axis to say skill.

then update that y-axis to say skill. All right. So, remember, I'm just rough

All right. So, remember, I'm just rough drafting right now. I do want KPIs on

drafting right now. I do want KPIs on top of here. So, we're going to be using

top of here. So, we're going to be using that card new. Make sure you don't

that card new. Make sure you don't select the actual visual and do this.

select the actual visual and do this. I'm I'm going control Z this. I didn't

I'm I'm going control Z this. I didn't have the outside selected. I'm insert in

have the outside selected. I'm insert in card new and I'm going to drag this

card new and I'm going to drag this along this top area right here. We're

along this top area right here. We're going to put titles and slicers up at

going to put titles and slicers up at the top. So that's why I'm going to

the top. So that's why I'm going to leave that space. Now in this I'm going

leave that space. Now in this I'm going to have four different KPIs. I want the

to have four different KPIs. I want the leftmost to deal with well like counts

leftmost to deal with well like counts and skills. So I'm going to drag job

and skills. So I'm going to drag job count into here and then also skills per

count into here and then also skills per job. And then over on top of this one,

job. And then over on top of this one, don't worry, this is going to shift over

don't worry, this is going to shift over as soon as you add more. Over on top of

as soon as you add more. Over on top of this one, I want things like the yearly

this one, I want things like the yearly and then also hourly salary. So I'll

and then also hourly salary. So I'll drag median yearly salary in. I notice

drag median yearly salary in. I notice also that we don't have anything for

also that we don't have anything for hourly. So what I'm going to do is going

hourly. So what I'm going to do is going to copy the median yearly salary. Right

to copy the median yearly salary. Right click, create a new measure. Paste this

click, create a new measure. Paste this one in. Replace everything for hourly.

one in. Replace everything for hourly. And everything is updated. Press enter.

And everything is updated. Press enter. And now going and dragging in median

And now going and dragging in median hourly salary with that slicer selected.

hourly salary with that slicer selected. Only three things are showing. That's

Only three things are showing. That's because under format your visual the

because under format your visual the layout right now it says max card shown

layout right now it says max card shown three. We want to bump that up to four.

three. We want to bump that up to four. I also don't like this border around

I also don't like this border around each of those cards. So I'm going to

each of those cards. So I'm going to select border or type in border. And

select border or type in border. And then underneath cards I see that that's

then underneath cards I see that that's the border that I want. I'm going to

the border that I want. I'm going to undo it. I also don't like how these are

undo it. I also don't like how these are not centered. They're all left aligned.

not centered. They're all left aligned. So, inside of callout values, I'm going

So, inside of callout values, I'm going to go down here and just center those.

to go down here and just center those. All right, not too bad. Let's insert in

All right, not too bad. Let's insert in a title and some put put some slicers up

a title and some put put some slicers up at the top. Put in data jobs dashboard

at the top. Put in data jobs dashboard 2.0. Made it a 36 point font and then

2.0. Made it a 36 point font and then put it in bold. All right. Now, let's

put it in bold. All right. Now, let's add in some slicers. Remember, we want

add in some slicers. Remember, we want to slice two things. We want to slice

to slice two things. We want to slice that job title short and then also the

that job title short and then also the country. But let's actually format this

country. But let's actually format this one how we actually want it to look. and

one how we actually want it to look. and then we'll copy it. Specifically under

then we'll copy it. Specifically under format visual, the style, we're going to

format visual, the style, we're going to change this to a drop down. I'm also

change this to a drop down. I'm also going to make the values a little bit

going to make the values a little bit bigger so that way you can see it. Go

bigger so that way you can see it. Go size 14. Inside of here, I want a few

size 14. Inside of here, I want a few different options. Specifically, the

different options. Specifically, the selection, I want to show select all.

selection, I want to show select all. So, I'm going to enable that. And then

So, I'm going to enable that. And then in the slicer itself, clicking the three

in the slicer itself, clicking the three dots, I'm going to enable search. That

dots, I'm going to enable search. That way whenever they drop down here, they

way whenever they drop down here, they can actually have be able to search for

can actually have be able to search for something like data analyst. They can

something like data analyst. They can select it, it'll filter. All right, undo

select it, it'll filter. All right, undo it. The last thing is I like to cue

it. The last thing is I like to cue people in. So I'm just going to say

people in. So I'm just going to say select job title. Okay, looking good.

select job title. Okay, looking good. Let's actually copy this. And in this

Let's actually copy this. And in this one, instead of doing select job title,

one, instead of doing select job title, we're going to drag in job country. And

we're going to drag in job country. And we'll rename this to select country.

we'll rename this to select country. Now, I do want an easy way to clear the

Now, I do want an easy way to clear the slicers. So, I am going to insert a

slicers. So, I am going to insert a button. Specifically, we're going to

button. Specifically, we're going to insert a clear all slicers button. And

insert a clear all slicers button. And I'm going to put it over here to the

I'm going to put it over here to the right hand side. Under the button

right hand side. Under the button option, going to style. I'm just going

option, going to style. I'm just going to say instead of clear all slicers,

to say instead of clear all slicers, only two. I'm just say clear slicers.

only two. I'm just say clear slicers. Also, make that text a little bit

Also, make that text a little bit bigger. And I'm going to enable the

bigger. And I'm going to enable the shadow to make it look a little bit more

shadow to make it look a little bit more standout so people understand it is a

standout so people understand it is a button. I do have to format things a

button. I do have to format things a little bit to make a little bit more

little bit to make a little bit more space for it. All right. Looking good.

space for it. All right. Looking good. I'm liking this layout here. It's very

I'm liking this layout here. It's very simple. We got two visuals underneath.

simple. We got two visuals underneath. We got our main KPIs up at the top and

We got our main KPIs up at the top and then we have the appropriate slicers as

then we have the appropriate slicers as well.

All right. Now that we have the rough draft out of the way, there is some

draft out of the way, there is some refinement I want to get into with this.

refinement I want to get into with this. Specifically, as I mentioned in the

Specifically, as I mentioned in the beginning, for these skills, I want to

beginning, for these skills, I want to look at it from two perspectives. Job

look at it from two perspectives. Job count and percentage. And then for the

count and percentage. And then for the salary, I want to look at it from yearly

salary, I want to look at it from yearly and also hourly. I want to be able to

and also hourly. I want to be able to switch between them. We need to do

switch between them. We need to do parameters for this. Let's get into the

parameters for this. Let's get into the skill one first. Specifically, we want a

skill one first. Specifically, we want a percentage. What do I mean by

percentage. What do I mean by percentage? Well, in my app, I don't

percentage? Well, in my app, I don't present counts of a particular skill.

present counts of a particular skill. Instead, what a percent is the

Instead, what a percent is the percentage or likelihood that it's going

percentage or likelihood that it's going to be in a job posting. In this case,

to be in a job posting. In this case, Python is in 55% of all job postings.

Python is in 55% of all job postings. Whenever I filter for something like

Whenever I filter for something like data analyst in the United States, it

data analyst in the United States, it tells me something like PowerBI is in

tells me something like PowerBI is in almost 17% of job postings. So, we need

almost 17% of job postings. So, we need to create a measure inside of PowerBI to

to create a measure inside of PowerBI to do this skill percentage before we even

do this skill percentage before we even create that parameter. So, I'm going to

create that parameter. So, I'm going to rightclick our measures and select

rightclick our measures and select create new measure. In this, we're going

create new measure. In this, we're going to create a new one called job percent.

to create a new one called job percent. Now, in order to get this percentage, we

Now, in order to get this percentage, we have to take well, we have to divide. We

have to take well, we have to divide. We have to have a numerator and a

have to have a numerator and a denominator. So we're going to use the

denominator. So we're going to use the divide function for this. So the

divide function for this. So the numerator is the count of a particular

numerator is the count of a particular skill that has been filtered down into

skill that has been filtered down into the correct context. So in the case of

the correct context. So in the case of something like Python that's below here,

something like Python that's below here, that is the count of Python, which is

that is the count of Python, which is 244,000. Anyway, that's done by the

244,000. Anyway, that's done by the measure job count. So that's pretty

measure job count. So that's pretty easy, right? We just put in job count.

easy, right? We just put in job count. But now the denominator,

But now the denominator, this needs to be basically this job

this needs to be basically this job count right here, this 479,000.

count right here, this 479,000. But I can't just put something in like

But I can't just put something in like job count, right? Um cuz this isn't

job count, right? Um cuz this isn't going to show the right context. I'm

going to show the right context. I'm just doing this for demonstration

just doing this for demonstration purposes. I'm dividing job count by job

purposes. I'm dividing job count by job count. And then inside of here, I'm

count. And then inside of here, I'm going to drag job percentage in here and

going to drag job percentage in here and take off job count. Anyway, every single

take off job count. Anyway, every single one of them, right, is 100% not what we

one of them, right, is 100% not what we want. So, going to job percentage just

want. So, going to job percentage just to show what we do need in here. We need

to show what we do need in here. We need it to be basically that 479,000 of

it to be basically that 479,000 of 479,000. We're not going to keep this

479,000. We're not going to keep this cuz we could potentially put filters on

cuz we could potentially put filters on it. I'm going to show you why. Anyway,

it. I'm going to show you why. Anyway, pressing uh enter to do this. See, this

pressing uh enter to do this. See, this looks very similar to what we saw

looks very similar to what we saw previously. And I know this is correct

previously. And I know this is correct because it has the same around 51%.

because it has the same around 51%. That's what I expected to be see for

That's what I expected to be see for Python and SQL. Right now, it's not in

Python and SQL. Right now, it's not in the right format. So, I'm actually going

the right format. So, I'm actually going to select it. I'm going to change this

to select it. I'm going to change this to a percentage with zero decimal

to a percentage with zero decimal places. Anyway, I digress. So, with this

places. Anyway, I digress. So, with this job percentage, how are we going to get

job percentage, how are we going to get this correct value in here? Because

this correct value in here? Because right now, if I were to now filter for

right now, if I were to now filter for something like data analyst and then go

something like data analyst and then go down, right, this number has now changed

down, right, this number has now changed to 113,000. So, I can't hardcode it in.

to 113,000. So, I can't hardcode it in. These percentages are not correct.

These percentages are not correct. they're entirely too low. We need to

they're entirely too low. We need to basically use a certain function that

basically use a certain function that will remove that query context. This is

will remove that query context. This is why it's important that you know between

why it's important that you know between row query and filter context. Going into

row query and filter context. Going into job percent, you know, we're probably

job percent, you know, we're probably going to use something like the

going to use something like the calculate function for this. And for the

calculate function for this. And for the expression, we're still going to use job

expression, we're still going to use job count, but we want to do something in

count, but we want to do something in order to remove this skill context

order to remove this skill context filter or this skill query context

filter or this skill query context filter that's on there. Well, lucky for

filter that's on there. Well, lucky for us, there's a function for that called

us, there's a function for that called all selected. This removes the filters

all selected. This removes the filters from columns and rows in the current

from columns and rows in the current query. In our case, those column and

query. In our case, those column and rows would be the visualization.

rows would be the visualization. And with this, it retains all other

And with this, it retains all other context filters or explicit filters. So,

context filters or explicit filters. So, we need to specify what filters we don't

we need to specify what filters we don't want it to actually filter down for. And

want it to actually filter down for. And specifically, we don't want to filter

specifically, we don't want to filter down for this skills. So, I'm going to

down for this skills. So, I'm going to put in all selected. And we can put in a

put in all selected. And we can put in a table name or column. We need to put the

table name or column. We need to put the name of the column or name of the table

name of the column or name of the table where the skills are. So, skill dim. All

where the skills are. So, skill dim. All right. Now, pressing enter. Boom. This

right. Now, pressing enter. Boom. This data analyst actually updated and it's

data analyst actually updated and it's where I suspect, right? PowerBI here is

where I suspect, right? PowerBI here is up to 23%. Notice this is going to be

up to 23%. Notice this is going to be different from our dashboard or data

different from our dashboard or data nerd.te because data nerd.tech has

nerd.te because data nerd.tech has multiple different years. We're only

multiple different years. We're only doing 2024 within this dashboard, but

doing 2024 within this dashboard, but the percentages are very similar.

the percentages are very similar. Anyway, if I wanted to doublech check

Anyway, if I wanted to doublech check this, I could inside this visualization

this, I could inside this visualization in the tool tip drag in that job count

in the tool tip drag in that job count and then I could double check the math

and then I could double check the math by saying that hey, is 26500

by saying that hey, is 26500 / 113 23%. It is. I did the math just in

/ 113 23%. It is. I did the math just in case you don't believe me. All right,

case you don't believe me. All right, sweet. Let's go ahead and remove this

sweet. Let's go ahead and remove this data analyst filter. And let's now since

data analyst filter. And let's now since we have we have our job percentage and

we have we have our job percentage and job count for this one and our median,

job count for this one and our median, yearly, and median hourly, we need to

yearly, and median hourly, we need to create our parameters for this because

create our parameters for this because we're going to have slicers or basically

we're going to have slicers or basically tile buttons below this to select what

tile buttons below this to select what kind of view we want to see with each

kind of view we want to see with each within each of these. So, inside

within each of these. So, inside modeling, I'm going to go to new

modeling, I'm going to go to new parameters and we're going to do fields.

parameters and we're going to do fields. And for this, we're going to say select

And for this, we're going to say select skill measure. And I'm going to drag in

skill measure. And I'm going to drag in job percent first because I feel that's

job percent first because I feel that's more important. And then they can also,

more important. And then they can also, if they want to switch to job count, and

if they want to switch to job count, and I'm going to go ahead and select create

I'm going to go ahead and select create with add slicer to this page. I'm going

with add slicer to this page. I'm going to drag it down to this bottom portion

to drag it down to this bottom portion right here. I'm going to change this

right here. I'm going to change this slicer though. I don't want it to be a

slicer though. I don't want it to be a vertical list. I want it to be actual

vertical list. I want it to be actual tile buttons. And I don't like the title

tile buttons. And I don't like the title on there. I don't want it to have a

on there. I don't want it to have a title on there. So, I'm actually going

title on there. So, I'm actually going to click on here, just press space, and

to click on here, just press space, and then press enter to remove it. And then

then press enter to remove it. And then reformat this other visual to be right

reformat this other visual to be right above this. Okay. So, now I can select

above this. Okay. So, now I can select things like job percent or job count.

things like job percent or job count. And it's doing nothing because it's not

And it's doing nothing because it's not in our visual. So, with our visual

in our visual. So, with our visual selected, I'm go under select scale

selected, I'm go under select scale measure, and I'm going to drag that into

measure, and I'm going to drag that into the X-axis and remove this job percent.

the X-axis and remove this job percent. Okay. So, now I can switch between job

Okay. So, now I can switch between job percent and job count. and the

percent and job count. and the proportions of these should remain the

proportions of these should remain the same. So that's how you can also double

same. So that's how you can also double check that your math is right. Anyway,

check that your math is right. Anyway, let's do the same thing now for this

let's do the same thing now for this graph where we want to create a

graph where we want to create a parameter for hourly and yearly salary.

parameter for hourly and yearly salary. So I'm going to create a new fields

So I'm going to create a new fields parameter and I'll say select job

parameter and I'll say select job measure. I want yearly salary first

measure. I want yearly salary first followed by hourly. We're going to go

followed by hourly. We're going to go create this. It's going to add in a

create this. It's going to add in a slicer. Similarly, I'm going to remove

slicer. Similarly, I'm going to remove the title by selecting all of this in

the title by selecting all of this in the fields well pressing space and then

the fields well pressing space and then enter. And then under format your visual

enter. And then under format your visual under slicer settings. I'm going to

under slicer settings. I'm going to change this to tile. Once again, this

change this to tile. Once again, this isn't going to do anything till we

isn't going to do anything till we actually add it into our visualization

actually add it into our visualization itself. Just going to drag this in here

itself. Just going to drag this in here to the x-axis and get rid of that median

to the x-axis and get rid of that median yearly salary. And now we can select

yearly salary. And now we can select between the two. Pretty neat. Also, I'm

between the two. Pretty neat. Also, I'm noticing our median hourly salary is not

noticing our median hourly salary is not formatted correctly as currency. And

formatted correctly as currency. And we're just going to keep it simple at

we're just going to keep it simple at zero decimal places.

zero decimal places. All

All right, this is looking pretty sweet. I'm

right, this is looking pretty sweet. I'm liking it. But I do want to do some

liking it. But I do want to do some elements of improving the background to

elements of improving the background to show how this actually all works

show how this actually all works together cuz right now it's really

together cuz right now it's really unclear that the parameters work with

unclear that the parameters work with these graphs above here. So we can build

these graphs above here. So we can build in v visual cues in the background that

in v visual cues in the background that they are associated with each other

they are associated with each other along with these KPIs as well. We're

along with these KPIs as well. We're going to follow a similar similar

going to follow a similar similar approach that we did in the last

approach that we did in the last project. And I'm just going to use these

project. And I'm just going to use these rounded rectangles that we're going to

rounded rectangles that we're going to end up putting in the back. First thing

end up putting in the back. First thing I'm going to do is actually put these

I'm going to do is actually put these around the appropriate KPIs. So, we'll

around the appropriate KPIs. So, we'll do each of these. But let's actually I'm

do each of these. But let's actually I'm getting ahead of myself. We need to

getting ahead of myself. We need to adjust the coloring first. I don't want

adjust the coloring first. I don't want this blue color. So, under the format

this blue color. So, under the format shape, I'm going to the style. And then

shape, I'm going to the style. And then for the color first, I want to match the

for the color first, I want to match the background exactly. And I'm actually

background exactly. And I'm actually fine. I can't match the background

fine. I can't match the background exactly, but what I want is I want it to

exactly, but what I want is I want it to be slightly darker. So, I went with

be slightly darker. So, I went with black 20% lighter. And I don't know if

black 20% lighter. And I don't know if you can see, but there's like this blue

you can see, but there's like this blue line around here. Specifically, those

line around here. Specifically, those borders on. And for this one, I'm going

borders on. And for this one, I'm going to make it even slightly darker. To make

to make it even slightly darker. To make it stand out and pop and make it pop

it stand out and pop and make it pop even more, I'm going to turn on shadows.

even more, I'm going to turn on shadows. All right. So, this is good enough. I'm

All right. So, this is good enough. I'm going to now copy this and then paste

going to now copy this and then paste this for this KPI as well. And then I'm

this for this KPI as well. And then I'm going to create two more for these

going to create two more for these visualizations. Now, with these

visualizations. Now, with these visualizations, it's getting these extra

visualizations, it's getting these extra rounded curves. I don't really like

rounded curves. I don't really like that. So, I'm going to go under shape

that. So, I'm going to go under shape and the rounded corners. I'm going to

and the rounded corners. I'm going to just going to bring down until it

just going to bring down until it matches the rounded shape of that above

matches the rounded shape of that above it. It's around 10%. All right, looks

it. It's around 10%. All right, looks good. Going to copy this one. Going to

good. Going to copy this one. Going to paste it. Not bad. Like last time, I

paste it. Not bad. Like last time, I want to group all these and put them in

want to group all these and put them in the back. So, we need to go into that

the back. So, we need to go into that view, specifically showing the

view, specifically showing the selection. I'm going minimize this. So,

selection. I'm going minimize this. So, this is showing all of our different

this is showing all of our different shapes. I'm going to select all of our

shapes. I'm going to select all of our appropriate shapes by holding control,

appropriate shapes by holding control, selecting them all. Now, they're

selecting them all. Now, they're selected. Rightclick them, go to group,

selected. Rightclick them, go to group, name this group background, and then put

name this group background, and then put it all the way in the back. But we can't

it all the way in the back. But we can't see it, right? So, we need to remove for

see it, right? So, we need to remove for each one of these visuals, we need to

each one of these visuals, we need to remove their backgrounds or make it

remove their backgrounds or make it transparent. So, I'm going to turn off

transparent. So, I'm going to turn off the selection pane. We're done with that

the selection pane. We're done with that now. And get back into visualizations.

now. And get back into visualizations. We're going to go into these visuals and

We're going to go into these visuals and specifically under the format your

specifically under the format your visual under general under effects,

visual under general under effects, we're going to remove the background.

we're going to remove the background. And then for these cards is a little bit

And then for these cards is a little bit more complex. You actually have to go

more complex. You actually have to go into cards as well on top of what you

into cards as well on top of what you just did and then select background and

just did and then select background and turn this off. Anyway, now need to

turn this off. Anyway, now need to adjust these. Well, at least not the KPI

adjust these. Well, at least not the KPI cards. Those are fitting just fine

cards. Those are fitting just fine inside of there. But we need to adjust

inside of there. But we need to adjust now these visuals to make sure that

now these visuals to make sure that they're fitting inside of these

they're fitting inside of these appropriate blocks. All right, not

appropriate blocks. All right, not looking bad. This is now finalized. The

looking bad. This is now finalized. The next thing that I'd want to do is

next thing that I'd want to do is hopefully you've been saving this along

hopefully you've been saving this along the way. If you haven't, now is a good

the way. If you haven't, now is a good time to save it. But I'm going to

time to save it. But I'm going to actually save this with a new title so I

actually save this with a new title so I can get rid of all these different

can get rid of all these different pages. And then we can upload it to the

pages. And then we can upload it to the PowerBI service if you happen to want to

PowerBI service if you happen to want to do that option. Not required by any

do that option. Not required by any means. I'm going to call this data jobs

means. I'm going to call this data jobs dashboard 2.0 and then go through and

dashboard 2.0 and then go through and remove all these extra pages in here.

remove all these extra pages in here. Okay, everything's in here that I want.

Okay, everything's in here that I want. I'm going to save it again. And then

I'm going to save it again. And then under the home tag, I can go into

under the home tag, I can go into publish, select the dashboard I want to

publish, select the dashboard I want to go to, and click select and upload it.

go to, and click select and upload it. So, here's mine uploaded onto the

So, here's mine uploaded onto the PowerBI service. I can go through if I

PowerBI service. I can go through if I want to select things like data analyst

want to select things like data analyst in the United States and bam. This thing

in the United States and bam. This thing is good. I'm liking it. Remember if you

is good. I'm liking it. Remember if you did take this option or do this option

did take this option or do this option you can go to the option of under file

you can go to the option of under file embedded report and then publish to web

embedded report and then publish to web and then you'll get this link which you

and then you'll get this link which you can actually view mine at the link below

can actually view mine at the link below and this is accessible from any URL and

and this is accessible from any URL and completely interactive like we showed

completely interactive like we showed previously. Anyway, now I think we have

previously. Anyway, now I think we have the dashboard in a much better manner.

the dashboard in a much better manner. This meets all the things that I wanted

This meets all the things that I wanted out of this specifically. I wanted to

out of this specifically. I wanted to have insights on the skills. what were

have insights on the skills. what were the top skills and then also with the

the top skills and then also with the salaries, what were basically the

salaries, what were basically the highest paying jobs hourly and also

highest paying jobs hourly and also yearly. And for cases where we want to

yearly. And for cases where we want to filter our data down, such as looking at

filter our data down, such as looking at something like the data analyst, we can

something like the data analyst, we can get even more insights and value out of

get even more insights and value out of things like the KPIs above here.

things like the KPIs above here. Overall, I'm pretty in love with this

Overall, I'm pretty in love with this dashboard. Now, it's your turn to go

dashboard. Now, it's your turn to go through and finalize your project. Once

through and finalize your project. Once again, as a reminder, you're not

again, as a reminder, you're not required to follow my project exactly.

required to follow my project exactly. feel free to adapt it to your need.

feel free to adapt it to your need. Anyway, in the next lesson, the final

Anyway, in the next lesson, the final lesson, we're going to get into how I

lesson, we're going to get into how I would go about sharing this dashboard

would go about sharing this dashboard along with that previous dashboard that

along with that previous dashboard that we created halfway through this course.

we created halfway through this course. With that, see you in the next one.

All right, first of all, congratulations on wrapping up the second project and

on wrapping up the second project and now, if you will, this entire course.

now, if you will, this entire course. It's been nothing short of your hard

It's been nothing short of your hard work. In this video, we're going to be

work. In this video, we're going to be going through how to actually better

going through how to actually better share both of those different portfolio

share both of those different portfolio projects that we put together. But for

projects that we put together. But for our most recent dashboard, we still need

our most recent dashboard, we still need to go through and create a readme to

to go through and create a readme to document what we all did. And as we're

document what we all did. And as we're going to find out, we need to reorganize

going to find out, we need to reorganize our project. Anyway, what do I mean by

our project. Anyway, what do I mean by that? Let's jump in.

So, here I am inside of VS Code and we haven't changed anything since the last

haven't changed anything since the last left off. But specifically inside of our

left off. But specifically inside of our PowerBI dashboards, we have three

PowerBI dashboards, we have three different objects. Images folder, the

different objects. Images folder, the data jobs dashboard or actual PowerBI

data jobs dashboard or actual PowerBI file, and then our readme. I can even

file, and then our readme. I can even pull it up on VS Code just to show that

pull it up on VS Code just to show that okay it is these three things and that

okay it is these three things and that readme is getting put on that first page

readme is getting put on that first page and that's for this project or this repo

and that's for this project or this repo that we have of PowerBI dashboards but I

that we have of PowerBI dashboards but I want all of our projects in here or our

want all of our projects in here or our la or our current two projects in here.

la or our current two projects in here. So we need to restructure this in a way

So we need to restructure this in a way to accomplish that. So back inside of VS

to accomplish that. So back inside of VS Code, what I'm going to do is I'm going

Code, what I'm going to do is I'm going to create a folder for these projects

to create a folder for these projects right here. I'm going to call it data

right here. I'm going to call it data jobs v1. Press enter. Anyway, I'm going

jobs v1. Press enter. Anyway, I'm going to grab these both these items and put

to grab these both these items and put them inside of here. Now, with this

them inside of here. Now, with this restructuring,

restructuring, unfortunately, we don't have something

unfortunately, we don't have something to deh show on the front of our repo.

to deh show on the front of our repo. But now, with this restructuring, it's

But now, with this restructuring, it's going to affect how our repo looks.

going to affect how our repo looks. Specifically, I'm just going to show

Specifically, I'm just going to show this for demo. You don't need to do

this for demo. You don't need to do this. I'm going to push these changes up

this. I'm going to push these changes up to GitHub with this commit of move

to GitHub with this commit of move folders commit. and then I'm going to

folders commit. and then I'm going to sync the changes. Now, refreshing this

sync the changes. Now, refreshing this inside of GitHub, what we're going to

inside of GitHub, what we're going to notice is, okay, we got our folder, but

notice is, okay, we got our folder, but now we need a readme. So, what we're

now we need a readme. So, what we're going to do is we're going to create a

going to do is we're going to create a readme for the top of the repository and

readme for the top of the repository and then direct people to either data jobs

then direct people to either data jobs v1 and data jobs v2. And if you happen

v1 and data jobs v2. And if you happen to make other projects in PowerBI, you

to make other projects in PowerBI, you can just add them straight to this. So,

can just add them straight to this. So, super convenient. So, back in VS Code

super convenient. So, back in VS Code under explore, I'm going to go ahead and

under explore, I'm going to go ahead and add a new file. And this one is going to

add a new file. And this one is going to be called readme.md. Now, these readmes,

be called readme.md. Now, these readmes, right, they need to be named this

right, they need to be named this specifically because that's how GitHub

specifically because that's how GitHub knows to pick it up and display it. I'm

knows to pick it up and display it. I'm going to close this side pane and also

going to close this side pane and also zoom out a little bit. I'm pressing

zoom out a little bit. I'm pressing control and then minus. All right, so

control and then minus. All right, so let's go ahead and build out this front

let's go ahead and build out this front page. And we're going to keep it really

page. And we're going to keep it really simple. I'm also going to be opening up

simple. I'm also going to be opening up this side pane so we can see as we work

this side pane so we can see as we work as we go. I started off simple with my

as we go. I started off simple with my PowerBI dashboard portfolio. give a

PowerBI dashboard portfolio. give a little short intro into what I'm doing

little short intro into what I'm doing here. And then underneath this, I have a

here. And then underneath this, I have a featured dashboard section, which we can

featured dashboard section, which we can now list all of our different

now list all of our different dashboards. I'm going start by first

dashboards. I'm going start by first putting in that data jobs dashboard,

putting in that data jobs dashboard, this V1. We're going to just put an

this V1. We're going to just put an image in real quick. And we're going to

image in real quick. And we're going to use that same image we've been using

use that same image we've been using that we have in our image folders. And

that we have in our image folders. And for this I start by giving the

for this I start by giving the hypertext. So an exclamation point and

hypertext. So an exclamation point and then inside brackets a brackets the

then inside brackets a brackets the actual alt text and then in parentheses

actual alt text and then in parentheses the link to that specifically we want to

the link to that specifically we want to go in the images folder and we want that

go in the images folder and we want that project one page one. Now for me I'm

project one page one. Now for me I'm also going to list right underneath this

also going to list right underneath this the link to this so that way if they

the link to this so that way if they wanted to they can go to my interactive

wanted to they can go to my interactive dashboard that's hosted on the PowerBI

dashboard that's hosted on the PowerBI service. Now, the next section that I'm

service. Now, the next section that I'm going to add is optional, but highly

going to add is optional, but highly recommended. I'm going to capture in

recommended. I'm going to capture in bullet point, very succinctly, what are

bullet point, very succinctly, what are all the different PowerBI skills that I

all the different PowerBI skills that I used in order to build this

used in order to build this visualization. And then right under

visualization. And then right under this, I want to link them to my readme

this, I want to link them to my readme in the project one folder. So, they

in the project one folder. So, they start checking out this. So, we're going

start checking out this. So, we're going to create a link. In square brackets,

to create a link. In square brackets, I'm going to put what I want for the

I'm going to put what I want for the text. And then from there, I'm going to

text. And then from there, I'm going to put in the link. So whenever they go to

put in the link. So whenever they go to click on it, it will pop up and then

click on it, it will pop up and then they can go through that readme that we

they can go through that readme that we previously had. One note, I just put the

previously had. One note, I just put the skills that we previously had generated

skills that we previously had generated into CHBT, had it condensed down, and

into CHBT, had it condensed down, and that's how I got this list right here

that's how I got this list right here for our new readme. So we have the core

for our new readme. So we have the core features built out for this. I'm liking

features built out for this. I'm liking what's going on here. I'm going to now

what's going on here. I'm going to now save this and then close out of it all.

save this and then close out of it all. Go into source control and I'm going to

Go into source control and I'm going to push this up to my readme. Feel free to

push this up to my readme. Feel free to actually you do this now too as well. I

actually you do this now too as well. I gave it the commit message of update

gave it the commit message of update source readme and then I'm just going to

source readme and then I'm just going to sync the changes. And now inside of here

sync the changes. And now inside of here I'm going to click refresh for GitHub.

I'm going to click refresh for GitHub. And we have this now to where it shows

And we have this now to where it shows hey there's our feature dashboards.

hey there's our feature dashboards. There's our first dashboard. We can view

There's our first dashboard. We can view it on PowerBI service. Oh, I want to

it on PowerBI service. Oh, I want to view the full project one details. I can

view the full project one details. I can click on it and it navigates me to this

click on it and it navigates me to this page here where I can now see all of

page here where I can now see all of this.

Now that we got that out of the way for the restructuring, let's actually get

the restructuring, let's actually get into building out our readme for V2 or

into building out our readme for V2 or the most recent one that we just built.

the most recent one that we just built. So, I'm going to create a new folder,

So, I'm going to create a new folder, call it data jobs v2. Inside of here,

call it data jobs v2. Inside of here, I'm going to add a readme cuz we want a

I'm going to add a readme cuz we want a readme for this one. And then the last

readme for this one. And then the last thing we do is need to wherever you save

thing we do is need to wherever you save data jobs dashboard your 2.0, you need

data jobs dashboard your 2.0, you need to just go ahead and drag it in. But

to just go ahead and drag it in. But apparently that doesn't work. So

apparently that doesn't work. So navigate where it is in file explorer

navigate where it is in file explorer and then open v2. Then I'm going to go

and then open v2. Then I'm going to go ahead and just copy this and then paste

ahead and just copy this and then paste it right into here using crl +v. Now for

it right into here using crl +v. Now for the readme for project 2, we're going to

the readme for project 2, we're going to follow a very similar structure to that

follow a very similar structure to that of v1. So I'm going to just go ahead and

of v1. So I'm going to just go ahead and copy this bad boy. then go into here and

copy this bad boy. then go into here and paste it in. ControlV and so I don't get

paste it in. ControlV and so I don't get confused, I'm g close out of the other

confused, I'm g close out of the other one. Gonna minimize the explorer so I

one. Gonna minimize the explorer so I can see better and then open this view.

can see better and then open this view. All right, so this is data jobs

All right, so this is data jobs dashboard and it is V2. We need to get

dashboard and it is V2. We need to get first and update an image. For this,

first and update an image. For this, we're just going to use that snipp tool

we're just going to use that snipp tool like we learned to use previously, which

like we learned to use previously, which all we have to do is press Windows

all we have to do is press Windows shifts. So I'm going to snap a shot of

shifts. So I'm going to snap a shot of this bad boy. Go to markup and share.

this bad boy. Go to markup and share. And specifically, I want to save this.

And specifically, I want to save this. And I'm going to save this in our images

And I'm going to save this in our images folders. Project 2, page one. Save it.

folders. Project 2, page one. Save it. Now, inside of here, all I need to do is

Now, inside of here, all I need to do is just update that this is actually

just update that this is actually project 2_.

project 2_. Bam. It's appearing right here. I've

Bam. It's appearing right here. I've also updated my link for this that we

also updated my link for this that we created previously, which I can just

created previously, which I can just click on to verify that it is in fact

click on to verify that it is in fact working. It's up there. It's good to go.

working. It's up there. It's good to go. Next up, I'm going to update the

Next up, I'm going to update the introduction to make it more relative

introduction to make it more relative that we upgrade the last dashboard. And

that we upgrade the last dashboard. And I just highlighted this. This project

I just highlighted this. This project offers a streamlined single page uh page

offers a streamlined single page uh page interface to quickly explore crucial

interface to quickly explore crucial market trends. Now, scrolling on down to

market trends. Now, scrolling on down to the skills showcased. These skills were

the skills showcased. These skills were really dealing with the first half of

really dealing with the first half of the course, so they need to be updated

the course, so they need to be updated for the second half of the course. So,

for the second half of the course. So, I'm going to go ahead and delete them

I'm going to go ahead and delete them and start from scratch. With this one, I

and start from scratch. With this one, I focused on not only dashboard design,

focused on not only dashboard design, but how much we use Power Query, how we

but how much we use Power Query, how we use data modeling and also DAX. And then

use data modeling and also DAX. And then I did highlight again the visualizations

I did highlight again the visualizations we used of car charts, cards, tables,

we used of car charts, cards, tables, and whatnot. And then finally,

and whatnot. And then finally, highlighting those interactive features

highlighting those interactive features like slicers and button, buttons, and

like slicers and button, buttons, and books. For this bottom portion, we don't

books. For this bottom portion, we don't have uh two pages. So, I'm actually

have uh two pages. So, I'm actually going to get rid of all this portion

going to get rid of all this portion regarding the second portion. I'll

regarding the second portion. I'll update our image right here to be from

update our image right here to be from project 2. And I don't really need this

project 2. And I don't really need this title as well. And we're going to get

title as well. And we're going to get rid of this text as well. So above the

rid of this text as well. So above the image, I want to call out that this is

image, I want to call out that this is the second iteration of this

the second iteration of this consolidating the dashboard into a

consolidating the dashboard into a single focus page. And then underneath

single focus page. And then underneath this, I call out how we've basically

this, I call out how we've basically made this more concise and we focus it

made this more concise and we focus it on key KPIs like job count, skills per

on key KPIs like job count, skills per job, median, yearly, hourly, salaries.

job, median, yearly, hourly, salaries. Um, the last thing to wrap up is the

Um, the last thing to wrap up is the conclusion. And for this, I just

conclusion. And for this, I just highlight once again that this is a V2

highlight once again that this is a V2 and that the purpose of this was really

and that the purpose of this was really streamline a lot of the analytics and

streamline a lot of the analytics and stuff that we did in V1 in order to show

stuff that we did in V1 in order to show what is most important to job seekers,

what is most important to job seekers, job transitioners, and job swappers. All

job transitioners, and job swappers. All right, this is looking good. I'm going

right, this is looking good. I'm going to go ahead and save this. The last

to go ahead and save this. The last thing that we need to do is now just

thing that we need to do is now just update that read me on the main page.

update that read me on the main page. So, I'm going to close out of these that

So, I'm going to close out of these that way it doesn't look as confusing. the

way it doesn't look as confusing. the read me on the main page. And for this,

read me on the main page. And for this, we need to add in that V2 down here,

we need to add in that V2 down here, right? So, we have V1 for our first

right? So, we have V1 for our first dashboard. We need to highlight V2

dashboard. We need to highlight V2 underneath this. So, I've put a title in

underneath this. So, I've put a title in here for this one for data jobs

here for this one for data jobs dashboard V2. And the first thing I want

dashboard V2. And the first thing I want to do is actually show an image. So, I

to do is actually show an image. So, I start by finding that alt text and then

start by finding that alt text and then in parenthesis, the actual hyperlink. I

in parenthesis, the actual hyperlink. I also like above want to have it to where

also like above want to have it to where it goes to the PowerBI service. So I'll

it goes to the PowerBI service. So I'll put in a hyperlink to the PowerBI

put in a hyperlink to the PowerBI service. Now we're going to do a similar

service. Now we're going to do a similar format to what we did up here in that

format to what we did up here in that we're going to talk about the key skills

we're going to talk about the key skills utilized. So underneath this we're going

utilized. So underneath this we're going to be putting that and with this feel

to be putting that and with this feel free to have just chat GPT summarize

free to have just chat GPT summarize what we wrote last in order to capture

what we wrote last in order to capture those key areas we want to talk about.

those key areas we want to talk about. And then underneath this remember we

And then underneath this remember we want them to go to that read me that we

want them to go to that read me that we just created for project 2. So, I'm

just created for project 2. So, I'm going to go ahead and put a link in here

going to go ahead and put a link in here where they can v uh view the full

where they can v uh view the full project. Anytime you create any link,

project. Anytime you create any link, you should always verify that it works

you should always verify that it works and it directs me right to it. Good to

and it directs me right to it. Good to go. Now, at the bottom, here's a little

go. Now, at the bottom, here's a little optional area that I'd recommend adding

optional area that I'd recommend adding for this front page of here. And that's

for this front page of here. And that's a about section about this portfolio.

a about section about this portfolio. And this just explains that every

And this just explains that every project heads read me that you can get

project heads read me that you can get even more insights about it. And then

even more insights about it. And then they direct you to the different PowerBI

they direct you to the different PowerBI files. Okay, this is good to go. Just do

files. Okay, this is good to go. Just do a once over for this. I'm liking it.

a once over for this. I'm liking it. Let's actually get this onto GitHub. So,

Let's actually get this onto GitHub. So, I'm going make a message of project 2

I'm going make a message of project 2 complete. Do commit and then from there

complete. Do commit and then from there sync the changes. Navigating back to the

sync the changes. Navigating back to the source folder. Let's see if it's

source folder. Let's see if it's working. Okay, looks like we have our V2

working. Okay, looks like we have our V2 in there. We have our main page showing

in there. We have our main page showing our first project and then the second

our first project and then the second project as well. And make sure that the

project as well. And make sure that the links are working. And I can navigate to

links are working. And I can navigate to the project 2 readme which has all the

the project 2 readme which has all the different information in here for it.

different information in here for it. It's looking really good.

So no work ever happened unless shared on LinkedIn. We're going to go through

on LinkedIn. We're going to go through the same steps that we did for project

the same steps that we did for project one to share it. The first thing is

one to share it. The first thing is adding a project. Here under our project

adding a project. Here under our project section, we're going to click add. So I

section, we're going to click add. So I added this name of data job skill

added this name of data job skill dashboard 2. I put in these skills which

dashboard 2. I put in these skills which are different from our last one, right?

are different from our last one, right? We have PowerBI but also now DAX Power

We have PowerBI but also now DAX Power Query data modeling and add this one of

Query data modeling and add this one of KPI dashboards. For the link for this, I

KPI dashboards. For the link for this, I would direct them to the readme of V2.

would direct them to the readme of V2. And so I' copy this one here. And then

And so I' copy this one here. And then under add media, I'm going to add in a

under add media, I'm going to add in a link. Paste in that value and then click

link. Paste in that value and then click apply. One quick note, if I navigate

apply. One quick note, if I navigate back to that source folder, right, this

back to that source folder, right, this is where we linked for project one to go

is where we linked for project one to go to, which it may be okay, but if you're

to, which it may be okay, but if you're a perfectionist, feel free to change

a perfectionist, feel free to change that to this area right here for them to

that to this area right here for them to actually navigate into and see project

actually navigate into and see project one. After this, put in a start and end

one. After this, put in a start and end date. And you can go ahead and save

date. And you can go ahead and save this. Now, for those that supported the

this. Now, for those that supported the course, as soon as you complete the end

course, as soon as you complete the end of course survey, I'll automatically

of course survey, I'll automatically send you a certificate of completion.

send you a certificate of completion. And you know what we got to do with this

And you know what we got to do with this bad boy? We also got to get this onto

bad boy? We also got to get this onto LinkedIn. On your profile, under

LinkedIn. On your profile, under licenses and certifications, I'm going

licenses and certifications, I'm going to go ahead and click add. For the name,

to go ahead and click add. For the name, we'll put in PowerBI for data analytics.

we'll put in PowerBI for data analytics. List me as the issuing organization.

List me as the issuing organization. Update the issue date. There is no

Update the issue date. There is no expiration date with this, so you can

expiration date with this, so you can leave that blank. The credential ID is

leave that blank. The credential ID is located on the certificate itself and

located on the certificate itself and then you'll also get emailed with this

then you'll also get emailed with this the actual URL to the certificate so you

the actual URL to the certificate so you can actually display it that you can use

can actually display it that you can use the as the credential URL update those

the as the credential URL update those skills. I just listed the same ones that

skills. I just listed the same ones that we covered in the project too because I

we covered in the project too because I feel like those were more robust and

feel like those were more robust and then from there you can add any media

then from there you can add any media images or whatnot. I would just

images or whatnot. I would just recommend using that GitHub profile,

recommend using that GitHub profile, specifically the main PowerBI dashboard

specifically the main PowerBI dashboard here that allows them to navigate to all

here that allows them to navigate to all the different files we did for this. So,

the different files we did for this. So, I'm just going to copy this and then add

I'm just going to copy this and then add in this link here. Looking good. All

in this link here. Looking good. All right. All we got to do now, click save.

right. All we got to do now, click save. Last thing we got to do is actually post

Last thing we got to do is actually post about it because it's great that you

about it because it's great that you updated your profile, but you actually

updated your profile, but you actually need to let others know what you did in

need to let others know what you did in it. Just make it simple. I've put an

it. Just make it simple. I've put an intro of, hey, I just completed this

intro of, hey, I just completed this course. gave a quick insight of what we

course. gave a quick insight of what we built and the skills we use for this.

built and the skills we use for this. Feel free to tag myself and also Kelly

Feel free to tag myself and also Kelly in this. We love seeing what you

in this. We love seeing what you actually build with this. And then

actually build with this. And then finally, at the end, you can list the

finally, at the end, you can list the URL to the project. Also, some people

URL to the project. Also, some people just like to put it in the comment

just like to put it in the comment section because apparently it works

section because apparently it works better with LinkedIn algorithm. I

better with LinkedIn algorithm. I included a photo, too, to just make it a

included a photo, too, to just make it a little bit more engaging. And go ahead,

little bit more engaging. And go ahead, post. So, once again, congratulations

post. So, once again, congratulations for wrapping up this course. It's been

for wrapping up this course. It's been nothing short of your hard work. One

nothing short of your hard work. One last note, it's not too late for you to

last note, it's not too late for you to get that certificate of completion. All

get that certificate of completion. All you got to do is support the course and

you got to do is support the course and then complete the end of course survey.

then complete the end of course survey. Tell me how I did during this and then

Tell me how I did during this and then I'll email it right to you. All right,

I'll email it right to you. All right, if you got value out of this video,

if you got value out of this video, smash that like button. And with that,

smash that like button. And with that, I'll see you in the next one.

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YouTube Transcript:Power BI for Data Analytics - Full Course for Beginners

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Power BI for Data Analytics - Full Course for Beginners