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How I Would Become a Data Analyst In 2025 (if I had to start over again)
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Here's how I would become a data analyst
if I had to start all over again in
2025. Now, I'm lazy and I'm impatient.
So, this method I'm going to be
choosing, the SPN method, is the fastest
and it's the lowest amount of work to
actually land a data job. But, it still
is a lot of work. Step one is I'd
understand the different data roles
available in the data world. There are
so many different data roles and it's
not just data analysts. There are so
many other roles that are just like data
analysts but have slightly different
names and slightly different
responsibilities. For example, business
intelligence analyst, business
intelligence engineer, technical data
analyst, business analyst, healthcare
analyst, risk analyst, price analyst.
There are so many literally so many
different options that you could
possibly choose from. And they're all
pretty similar for the most part, but
some things are going to be slightly
different. So for example, a healthcare
analyst, you're going to be a data
analyst, but specializing in looking at
healthcare data. Financial analyst, same
thing. You'd be looking at financial
data. A BI analyst like a business
intelligence analyst and a data analyst
really a lot of the time are going to be
doing the exact same thing. So it's
important to be looking for all these
roles, understand what these roles do
and what their slight nuances are
because there's a chance that your
previous experience is actually valuable
and would help you get a leg up in
applying for these different jobs. So,
for example, if you have a business
degree and you're trying to transfer
into business analytics, becoming a
business analyst makes a lot of sense.
Or a financial analyst makes a lot of
sense. If you've worked previously as a
nurse or like a CNA, maybe you become a
healthcare analyst. Whatever you've done
previously, there's probably a good
chance that that experience is valuable
in the data world to a specific role.
So, even like I have a lot of truck
drivers in my boot camp. Those truck
drivers can be logistics analysts. They
can be operations analysts. They can be
supply chain analysts because their
previous experience is actually
valuable. The second thing that I would
do is figure out what is actually
required. Because here's the truth.
There is actually thousands of data
skills and tools and programming
languages out there. But if you try to
master all of them, you're going to be
like 150 before you feel prepared to
start applying to jobs. You're going to
be dead. is impossible to learn and it's
impossible to master all the different
data tools and skills and languages. So
by default have to choose a few. Now you
have a decision to make is which ones do
you choose? And I like I said I am lazy
and I want to do the least amount of
work possible. So I believe in the
lowhanging best tasting fruit analogy.
If you can imagine that there's a tree
that has some sort of like a peach or an
apple on it, right? The easiest fruit to
grab is always going to be the closest.
So it's the lowest hanging fruit. But
not only do you want the lowest hanging
fruit, you want the tastiest fruit,
right? So this is stuff that is not only
easy to learn, but is extremely useful.
Those are the things you want to focus
on. Out of the thousands of data skills,
those are the ones you'll want to focus
on. You can do the research on your own
if you'd like by looking at job
descriptions and writing down what is
actually required. But that's a lot of
work. And you can dig it from someone
like me who's been in this space for
about a decade now. Looked at literally
thousands of job descriptions. I even
have my own data job board, find a data
job.com, and I look at it all the time
to see what is being required. So, I've
done this research for you already, and
I will have a link to my conclusions in
the show notes down below. But
basically, what you need to know in
terms of lowhanging fruit, it's Excel,
Tableau, and SQL. That is it. Those are
the top three skills that you should be
learning as a data analyst when you're
just trying to get started. And if that
is too hard to remember, you can
remember every turtle swims, right?
That's easy. Excel, Tableau, and SQL.
That is where I would start, and I
wouldn't really veer off of that until
I've landed my first data job. Now, you
might have noticed that I didn't say
Python, and that might come as a
surprise to many of you because you hear
so much about Python and how cool it is
and how popular it is, and it is really
cool. It can do so many different
things. It's so powerful, and it's
actually my favorite data tool, but it's
actually only required on 30% of data
analyst roles. And it's really hard to
learn. It takes a long time to learn
Python because Python is hard, but also
all programming is hard. And if you
don't have a programming background,
it's going to take a long time to just
kind of even get your foot in the door
in the Python world and understand
what's going on. What's a variable?
What's a loop? What's a function? Those
types of things just they take time. And
so if you only need it for 30% of the
jobs, that means 70% of the jobs don't
require it. And once again, I'm all
about doing the least amount of work
possible and doing it as quickly as
possible. So, I say save Python for
after your first day of job because it's
really just not needed to land that
first one. Once again, I have a free
video that kind of explains what skills
you should learn and in what order and
why. I'll have that in the show notes
down below. The third thing that I would
do if I was trying to become a data
analyst is try to figure out how I'm
going to convince a hiring manager or
recruiter to hire me even though I have
no prior experience. There's this thing
called the cycle of doom, which
basically says, I can't land a data job
because I don't have experience because
I can't land a data job. And it's this
never- ending cycle of, well, you're
never going to get a job unless you have
experience. You can never get experience
unless you get a job. It's kind of like
the chicken or the egg. You know, what
comes first? Uh, so you have to figure
out how am I going to beat the cycle of
doom and how am I going to convince
someone that yeah, I am a data analyst
and you should hire me. How would I do
it personally? I'd build projects.
Projects are a great way that you can
demonstrate your skills. It's basically
the tangible evidence for people to know
that you can do what your resume says
you can do. If you're unfamiliar with
projects, it's like almost doing pretend
work where you're pretending that you're
working for a certain company. You take
a data set and you analyze it and
publish your results. We'll talk about
where to publish them here in a second,
but basically it's allowing you to learn
with realistic data with realistic
problems. But also, you're creating some
sort of evidence, like literally
physical evidence that you can show to
hiring managers, recruiters and be like,
"Hey, look, I can do these things. I can
be a data analyst. I can use Excel. I
can use SQL. I can create a data
visualization in Tableau." Once I
understand those three things, the
fourth thing that I would personally do
is start learning. And I want to
emphasize this is not the first thing.
This is not the second thing. This is
not the third thing. That's the fourth
thing that I would do is start learning.
And I would start learning Excel,
Tableau, SQL, Every Turtle Swims, right?
And I would do that by building projects
because I think building projects is the
most realistic way to learn. I also
think it's the funnest way to learn
because just doing like pointless
exercises on like these like interactive
online learning things. It's just not
realistic. Like in real life, you're
going to be having real data sets.
You're not going to be in some like
controlled environment. you're actually
going to have to be analyzing real data
that's messy, that has issues, that has
flaws, and you have to figure it out.
And so building projects is the best way
to learn because you're also creating
this tangible evidence that you're going
to be able to show to hiring managers
and recruiters. You might be thinking,
well, where do I get started? Well, you
need to figure out where you can find
data sets. You have to have a good data
set. I just did an episode on this
recently, and I'll have the link in the
show notes down below, but the simple
answer, the one-word answer is Kaggle.
Kaggle is the best place to find a data
set. It's not the only place and there's
other great resources, but if you're
only looking for one, Kaggle's usually
the place I would go and I'd personally
build projects based off of what you
want to do ultimately. So, go back to
step one and think about it like if you
have a business degree. Let's say you
want to become a business analyst. I
would try to build projects that are
relevant to to business analytics, maybe
data on sales or marketing or
operations, anything that's business
related. Those are the projects I would
try to seek out. Or if you're not sure,
like if you want to be a business
analyst or a healthcare analyst, or
maybe you don't even care, you'll just
take whatever you've got. I would
suggest doing projects on lots of
different industries. Maybe dip into
healthcare analytics. Maybe do some
people and HR analytics. Maybe do a
project on manufacturing and engineering
data. That way, you're getting exposed
to multiple different industries so you
can kind of figure out maybe what you're
interested in. You're creating a robust
portfolio that will be attractive to
every industry and multiple companies,
right? Because if you just focus on
creating, you know, business projects,
but let's say you want to become a
healthcare analyst, it's like, oh, those
projects don't really match up. So that
way you have a project for whatever role
you might be interested in. So that's
particularly what I suggest doing and
it's what we do inside of my boot camp,
the data analytics accelerator, is we
learn Excel, SQL, and Tableau, my
building projects, and we built multiple
projects in different industries. So
that way we're very robust as
candidates. The fifth thing I would do
if I was trying to become a data analyst
is create a home for my projects. And
this is actually what's called a
portfolio. You know, projects are
something that we do, but if you just do
them and you don't publish them and you
don't share them, they don't actually do
much good. You need to create a
portfolio to home these projects. And
the portfolio platform you'll hear the
most about is GitHub. And I have a
controversial take that I'm not a fan of
it. I don't think GitHub is meant to be
a portfolio. Now, that's me being a
little bit picky, but I just don't think
it's the best option if you're choosing
from scratch. What you need to do is
make sure that your readmmes are really
good because if you have a good readme
on your GitHub, then it can work. But if
you're starting from scratch, I
recommend doing something like LinkedIn
using the featured section or choose
GitHub pages which is from GitHub but
kind of a separate product and it's
their portfolio solution. It's actually
what GitHub recommends as a portfolio or
I really like card c a r d. It's just a
simple website builder be really great
options inside the accelerator my boot
camp. So any of those three would work
just fine. The sixth thing I would do is
make sure that my LinkedIn and resume
are up to date and optimized. And I
would do this early, even before I've
actually mastered Excel or I've, you
know, tackled Tableau. The earlier you
do this, the better because your
LinkedIn is your professional business
card to the world. One of the really
cool things is LinkedIn has a feature
called open to work. There's two
different settings on it. We can talk
about it later, but basically you can
have open to work for the entire world
or you can just have open to work for
recruiters. And either way, if you set
up your LinkedIn correctly, your
LinkedIn can start to work for you. and
instead of you going out and applying
for jobs, recruiters and hiring managers
are actually applying to you for
specific jobs. They'll reach out to you
and be like, "Hey, I think you're a good
fit for this job." So, having an
optimized LinkedIn is is really key. And
then, of course, having an optimized
resume is a must because once you start
applying for jobs, if your resume isn't
optimized, you're probably not going to
get many interviews. And the reason is
there's so many candidates trying to get
into data analytics roles, especially
the entry level ones, that recruiters
and hiring managers have to use what's
called the ATS, which is the applicant
tracking system. And basically, it's
it's computer, it's AI. It's actually
not even really that complicated. But
there's certain things you need to do on
your resume to have it be optimized and
ATS friendly so you can get past the
computer screening and actually have a
human being look at your resume. because
it's so frustrating when you get
rejection after rejection after
rejection that you don't even know if a
human's looking at your resume. A lot of
the times you're just getting rejected
by the ATS. And so you need to make sure
you have an optimized resume. So in
terms of having an optimized resume, it
would basically look like not having any
columns on your resume or any tables on
your resume and then using really
keywords that match the job descriptions
so that way you appear as a good
applicant to the ATS. The seventh step
that I would take is to start applying.
And I think this is obvious, but a lot
of people don't ever start applying for
jobs. And I get it because it's scary.
How do you know if you're ready to land
a data job? It's hard to know and you
probably will never feel ready. So, I
suggest just start applying anyways. And
when you start applying, don't only
apply on LinkedIn jobs. LinkedIn jobs is
where everyone applies and there's going
to be hundreds of candidates in a matter
of a few days on those platforms the
majority of the time because everyone's
doing that. So, you might want to try
something new like going to company
websites or checking out my job board,
find a data job.com, or some other
combination of other job websites. The
point here is you need to be looking in
multiple places and actually start
applying. I know it's scary, but just do
it scared. The next step I would do in
this process is I would really try to be
networking. And I I would try to be
networking the entire time, like even in
step one, but this is where it would fit
on today's road map is step eight. So,
it's way easier to get hired when you
know someone. In fact, my brother was
just recently looking for a job and
having a hard time and he ended up
getting an interview and landing that
job because his wife's friend works
there and like I can't tell you how
often that actually happens. So,
networking doesn't have to be hard. You
can do it on LinkedIn by posting and
commenting on LinkedIn. I think that's
really important to do, but I understand
that's hard and a scary step. One thing
that's really a lot easier is just to
talk to your friends and family. Just
say, "Hey, I'm trying to become a data
analyst. Do you know anyone who's a data
analyst? Does your company hire data
analysts?" And have a conversation.
You're not even really asking them
anything. You're just opening a
conversation. I know this is hard and I
know it's uncomfortable and I know it's
not fun. Like, it's much more fun to
learn data skills than it is to network,
but honestly, networking gets you the
same, if not better, results than
upskilling and actually learning new
data things. So, you can't be ignoring
this. Couldn't be ignoring this. I have
to be networking no matter how hard it
is. Now, if all is going well and I'm
doing all the previous eight things that
I've talked about, I think at this point
I'd probably start to land interviews.
There's two parts to an interview. The
technical and the behavioral. The
technical interview is when they're
going to be asking you questions about
data skills. It might be Excel questions
or data visualization questions or
oftentimes SQL questions and they'll ask
you to write certain SQL queries. This
can be really scary and intimidating and
honestly they can be really hard. The
cool part is they don't always occur or
or if they occur, they occur very
easily. Sometimes they're very hard,
sometimes they're very easy. It really
just depends. And to prepare for the
technical resources, there's a lot of
things that I could do. There's a lot of
resources out there that would help me
prepare. There's something called Strata
Scratch that I'll have a link in the
show notes down below that you guys can
check. There's data lemur. There's a
bunch of tools that will help you
prepare for these technical interviews.
Behavioral interview is going to be more
like them trying to feel for who you are
and what you've done previously and like
how you would act as a human being, as
an employee. And that is a little bit
harder to prepare for because it's more
of like instead of answering technical
questions, it's answering like personal
questions. There's not a whole lot of
resources out there. One of the things
you would want to do is use the STAR
method. You want to answer every
question by saying this is the situation
I was in. This is the task I was given.
This was the action I took. and this is
the results that came from that action.
And if you answer using that method,
most of the time you'll be good. It can
be scary and there's not a whole lot of
resources out there for this. So, if you
want to check out one that I made, it's
called interview simulator.io and it
basically helps you practice these
questions where I'll ask you the
question via video and you will respond
via video and then we'll actually grade
your answer and tell you what you did
well and where you could improve. It's a
pretty cool software. I'll have a link
for that in the show notes down below as
well. Wow, lots of links in the show
notes. So, be sure to check those out.
So, those are the nine steps that I
would take if I had to start from
scratch and land a day job in 2025. And
remember, I'm lazy. I'm trying to do
this the easiest way possible. This is
what I call the SPN method. You need to
learn the right skills. Not all the
skills, but the right skills. You need
to build projects and put them on a
portfolio. That's the P part. And then
you need to be networking, updating your
LinkedIn, and updating your resume.
That's the M part. And it's the easiest
way to land a data job. Now, you can do
all this stuff that I told you on your
own, and you'd be 100% okay, but it's a
lot more fun to do it in community, and
it's a lot easier to do with a coach.
Once again, I'm all about doing it fast
and easy, and it's much easier to do
that with a given curriculum where you
don't have to be questioning, am I doing
this right? How do I actually do this?
So on and so forth. And so that's why I
created the data analytics accelerator
program, which is basically a 10-week
boot camp to help you land your first
data job. We'll go over all of these
nine steps hand by hand, step by step
together and make sure you're ready to
land that data job. If you want to check
that out, you can go to data
careerjumpstart.com/da. DAA standing for
data analytics accelerator. And of
course, I'll have a link to that in the
show notes down below. Let me know what
I missed and what questions you have.
I'll try to respond to everyone in the
comments down below if you're watching
on YouTube or on Spotify. And I wish you
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