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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

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Power BI for Data Analytics - Full Course for Beginners