136: How I Would Become a Data Analyst In 2025 (if I had to start over again)
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⌚ TIMESTAMPS
00:16 Understanding Different Data Roles
01:48 Essential Data Skills and Tools
04:36 Building Projects to Showcase Skills
08:13 Creating a Portfolio for Your Projects
09:06 Optimizing LinkedIn and Resume
10:46 Applying for Jobs and Networking
12:38 Preparing for Interviews
14:25 Conclusion and Final Tips
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Transcript
Here's how I would become a data analyst if I had to
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:start all over again in 2025.
3
:Now, I'm lazy and I'm impatient,
so this method that I'm going to
4
:be choosing, the SPN method, is the
fastest and it's the lowest amount
5
:of work to actually land a data job.
6
:But it still is a lot of work.
7
:Step one is I'd understand the different
data roles available in the data world.
8
:There are so many different data
roles, and it's not just data analysts.
9
:There are so many other roles, That
are just like data analysts, but
10
:have slightly different names and
slightly different responsibilities.
11
:For example, business intelligence
analyst, business intelligence
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:engineer, technical data analyst,
business analyst, healthcare
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:analyst, risk analyst, price analyst.
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:There are so many, literally
so many different options that
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:you could possibly choose from.
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:And they're all pretty similar for
the most part, but some things are
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:going to be slightly different.
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:So for example, a healthcare analyst,
you're going to be a data analyst.
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:But specializing and
looking at healthcare data.
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:Financial analysts, same thing.
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:You'd be looking at financial data.
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:A BI analyst, like a business
intelligence analyst, and a data
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:analyst, really a lot of the time are
going to be doing the exact same thing.
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:So it's important to be looking for
all these roles, understand what these
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:roles do and what their slight nuances
are, because there's a chance that
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:your previous experience is actually
valuable and would help you get a leg
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:up in applying for these different jobs.
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:So for example, If you have a business
degree and you're trying to transfer
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:into business analytics, becoming a
business analyst makes a lot of sense or
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:a financial analyst makes a lot of sense.
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:If you've worked previously as
a nurse or like a CNA, maybe
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:you become a healthcare analyst.
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:Whatever you've done previously,
there's probably a good chance
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:that that experience is valuable in
the data world to a specific role.
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:So even like I have a lot of
truck drivers in my business.
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:Bootcamp.
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:Those truck drivers can be logistics
analysts, they can be operations
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:analysts, they can be supply chain
analysts, because their previous
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:experience is actually valuable.
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:The second thing that I would do
is figure out what is actually
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:required, because here's the truth.
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:There is actually thousands
of data skills and tools.
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:and programming languages out there,
but if you try to master all of them,
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:you're going to be like 150 before you
feel prepared to start applying to jobs.
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:You're going to be dead.
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:It is impossible to learn.
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:It's impossible to master
all the different data tools
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:and skills and languages.
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:So by default, have to choose a few.
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:Now you have a decision to make
is which ones do you choose?
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:And I, like I said, I am lazy and I want
to do the least amount of work possible.
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:So I believe in the low hanging best.
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:Tasting fruit analogy.
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:If you can imagine that there's
a tree that has some sort of like
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:a peach or an apple on it, right?
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:The easiest fruit to grab is
always going to be the closest,
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:so it's the lowest hanging fruit.
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:But not only do you want the
lowest hanging fruit, you want
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:the tastiest fruit, right?
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:So this is stuff that is not only easy
to learn, but is extremely useful.
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:Those are the things you want to focus on.
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:Out of the thousands of data skills, those
are the ones you'll want to focus on.
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:You can do the research on your own,
if you'd like, by looking at job
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:descriptions and writing down what
is actually required, but that's a
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:lot of work and you can take it from
someone like me, who's been in this
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:space for about a decade now, looked at
literally thousands of job descriptions.
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:I even have my own data job board.
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:Findadatajob.
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:com.
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:And I look at it all the time
to see what is being required.
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:So I've done this research for you
already, and I will have a link to
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:my conclusions in the show notes
down below, but basically what you
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:need to know in terms of low hanging
fruit, it's Excel, Tableau, and SQL.
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:That is it.
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:Those are the top three skills that you
should be learning as a data analyst
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:when you're just trying to get started.
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:And if that is too hard to remember, you
can remember every turtle swims, right?
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:That's easy.
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:Excel.
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:Tableau and SQL.
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:That is where I'd start and I
wouldn't really veer off of that
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:until I've landed my first data job.
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:Now you might have noticed that I
didn't say Python and that might
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:come as a surprise to many of you
because you hear so much about
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:Python and how cool it is and how
popular it is and it is really cool.
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:It can do so many different things.
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:It's so powerful and it's actually my
favorite data tool but it's actually only
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:required on 30 percent of data analyst
roles and it's really hard to learn.
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:It takes a long time to learn
Python because Python is hard,
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:but also all programming is hard.
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:And if you don't have a programming
background, it's going to take a
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:long time to just kind of even get
your foot in the door in the Python
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:world and understand what's going on.
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:What's a variable?
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:What's a loop?
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:What's a function?
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:Those types of things just, they take
time and so if you only need it for
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:30 percent of the jobs, that means 70
percent of the jobs don't require it.
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:And once again, I am all about doing
the least amount of work possible
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:and doing it as quickly as possible.
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:So I say save Python for after your
first day at a job because it's really
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:just not needed to land that first one.
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:Once again, I have a free video that
kind of explains what skills you
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:should learn and in what order and why.
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:I'll have that in the
show notes down below.
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:The third thing that I would do if I was
trying to become a data analyst is try
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:to figure out how I'm going to convince
a hiring manager or recruiter to hire me,
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:even though I have no prior experience.
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:There's this thing called the cycle of
doom, which basically says I can't land a
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:data job because I don't have experience
because I can't land a data job.
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:And it's this never ending cycle
of, well, you're never going to get
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:a job unless you have experience.
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:You can never get experience
unless you get a job.
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:It's kind of like the
chicken or the egg, you know?
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:So you have to figure out, how am
I going to beat the cycle of doom?
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:And how am I going to convince
someone that, yeah, I am a data
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:analyst and you should hire me.
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:How would I do it, personally?
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:I'd build projects.
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:Projects are a great way that
you can demonstrate your skills.
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:It's basically the tangible evidence
for people to know that you can do
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:what your resume says you can do.
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:If you're unfamiliar with projects,
It's like almost doing pretend work
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:where you're pretending that you're
working for a certain company.
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:You take a data set and you analyze
it and publish your results.
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:We'll talk about where to publish them
here in a second, but basically it's
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:allowing you to learn with realistic data
with realistic problems, but also you're
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:creating some sort of evidence, like
literally physical evidence that you can
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:show to hiring managers, recruiters, and
be like, Hey, look, I can do these things.
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:I can be a data analyst.
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:I can use Excel.
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:I can use SQL.
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:I can create a data
visualization in Tableau.
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:Once I understand those three
things, the fourth thing that I would
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:personally do is start learning.
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:And I want to emphasize
this is not the first thing.
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:This is not the second thing.
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:This is not the third thing.
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:It's the fourth thing that I
would do is start learning.
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:And I would start learning Excel,
Tableau, SQL, every turtle swims, right?
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:And I would do that by building projects,
because I think building projects
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:is the most realistic way to learn.
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:I'll think it's It's the funnest
way to learn because just doing like
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:pointless exercises on like these
like interactive online learning
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:things, this is not realistic.
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:Like in real life, you're going
to be having real data sets.
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:You're not going to be in some
like controlled environment.
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:You're actually going to have to be
analyzing real data that's messy,
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:that has issues that has flaws
and you have to figure it out.
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:And so building projects is the
best way to learn because you're
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:also creating this tangible evidence
that you're going to be able to show
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:to hiring managers and recruiters.
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:You might be thinking, well,
where do I get started?
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:Well, you need to figure out
where you can find datasets.
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:You have to have a good dataset.
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:I just did an episode on this
recently, and I'll have the link
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:to the show notes down below.
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:But the simple answer, the
one word answer is Kaggle.
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:Kaggle is the best
place to find a dataset.
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:It's not the only place, and there's
other great resources, but if
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:you're only looking for one, Kaggle
is usually the place I would go.
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:And I'd personally build projects based
off of what you want to do ultimately.
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:So go back to step one and think about it.
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:Like if you have a business degree, let's
say you want to become a business analyst,
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:I would try to build projects that are
relevant to, to business analytics.
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:Maybe data on sales or marketing
or operations, anything
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:that's business related.
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:Those are the projects
I would try to seek out.
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:Or if you're not sure, like if you want
to be a business analyst or a healthcare
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:analyst, or maybe you don't even care.
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:You'll just take whatever you've got.
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:I would suggest doing projects
on lots of different industries.
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:Maybe dip into healthcare analytics.
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:Maybe do some people and HR analytics.
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:Maybe do a project on
manufacturing and engineering data.
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:That way you're getting exposed
to multiple different industries,
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:so you can kind of figure out
maybe what you're interested in.
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:You're creating a robust portfolio
that will be attractive to every
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:industry and multiple companies, right?
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:Because if you just focus on creating,
you know, business projects, but
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:let's say you want to become a
healthcare analyst, it's like, oh,
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:those projects don't really match up.
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:So.
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:That way you have a project for whatever
role you might be interested in.
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:So that's particularly
what I suggest doing.
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:And it's what we do inside of
my bootcamp, the Data Analytics
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:Accelerator is we learn Excel, SQL,
and Tableau by building projects.
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:And we built multiple projects
in different industries.
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:So that way we're very robust as can.
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:The fifth thing I would do if I was
trying to become a data analyst.
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:is create a home for my projects.
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:And this is actually
what's called a portfolio.
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:You know, projects are something that
we do but if you just do them and you
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:don't publish them and you don't share
them, they don't actually do much good.
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:You need to create a portfolio
to home these projects.
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:And the portfolio platform you'll
hear the most about is GitHub.
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:And I have a controversial
take that I'm not a fan of it.
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:I don't think GitHub is
meant to be a portfolio.
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:Now that's me being a little bit picky,
but I just don't think it's the best
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:option if you're choosing from scratch.
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:What you need to do is make sure
that your readmes are really good,
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:because if you have a good readme
on your GitHub, then it can work.
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:But if you're starting from scratch,
I recommend doing something like
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:LinkedIn, using the featured section.
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:Or choose GitHub Pages, which is from
GitHub, but kind of a separate product,
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:and it's their portfolio solution.
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:It's actually what GitHub
recommends as a portfolio.
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:Or I really like Card, C A R R D.
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:It's just a simple website builder,
be really great options inside the
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:accelerator, my bootcamp, so any of
those three would work just fine.
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:The sixth thing I would do is make
sure that my LinkedIn and resume
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:are up to date and optimized.
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:And I would do this early, even
before I've actually mastered Excel
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:or I've, you know, tackled Tableau.
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:The earlier you do this,
the better, because.
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:Your LinkedIn is your professional
business card to the world.
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:One of the really cool things is LinkedIn
has a feature called Open to Work.
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:There's two different settings on it.
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:We can talk about it later, but
basically you can have Open to Work
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:for the entire world or you can just
have Open to Work for recruiters.
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:And either way, if you set up
your LinkedIn correctly, your
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:LinkedIn can start to work for you.
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:And instead of you going out and
applying for jobs, recruiters
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:and hiring managers are actually
applying to you for specific jobs.
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:They'll reach out to you and be like, Hey,
I think you're a good fit for this job.
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:So having an optimized
LinkedIn is, is really key.
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:And then of course, having an
optimized resume is a must because
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:once you start applying for jobs.
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:If your resume isn't optimized, you're
probably not going to get many interviews.
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:And the reason is there's so many
candidates trying to get into data
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:analytics roles, especially the
entry level ones, that recruiters
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:and hiring managers have to use
what's called the ATS, which is
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:the Applicant Tracking System.
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:And basically it's, it's computer, it's
AI, it's It's actually not even really
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:that complicated, but there are certain
things you need to do on your resume to
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:have it be optimized and ATS friendly, so
you can get past the computer screening
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:and actually have a human being look at
your resume, because it's so frustrating
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:when you get rejection after rejection
after rejection that you don't even know
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:if a human's looking at your resume.
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:A lot of the times you're just getting
rejected by the ATS, and so you need to
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:make sure you have an optimized resume.
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:So, in terms of having an optimized
resume, it would basically look like
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:not having any columns on your resume,
or any tables on your resume, and
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:then using really key words that match
the job descriptions, so that way you
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:appear as a good applicant to the ATS.
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:The seventh step that I would take
is to start applying, and I think
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:this is obvious, but a lot of people
don't ever start applying for jobs.
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:And I get it, because it's scary.
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:How do you know if you're
ready to land a data job?
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:It's hard to know, and you probably
will never feel ready, so I suggest
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:just start applying anyways.
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:And when you start applying,
don't only apply on LinkedIn jobs.
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:LinkedIn jobs is where everyone applies,
and there's going to be hundreds of
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:candidates in a matter of a few days
on those platforms, the majority of the
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:time, because everyone's doing that.
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:So you might want to try something
new, like going to company websites or
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:checking out my job board, findadatajob.
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:com or some other combination
of other job websites.
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:The point here is you need to
be looking at multiple places
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:and actually start applying.
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:I know it's scary, but just do it scared.
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:The next step I would do in this process
is I would really try to be networking.
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:And I, I would try to be networking
the entire time, like even in step one.
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:But this is where I fit on
today's roadmap is step eight.
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:So it's way easier to get
hired when you know someone.
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:In fact, my brother was just recently
looking for a job and having a
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:hard time and he ended up Getting
an interview and landing that job
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:because his wife's friend works there.
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:And like, I can't tell you how
often that actually happens.
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:So networking doesn't have to be hard.
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:You can do it on LinkedIn by
posting and commenting on LinkedIn.
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:I think that's really important to do, but
I understand that's hard and a scary step.
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:One thing that's really a lot easier is
just to talk to your friends and family.
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:Just say, Hey, I'm trying
to become a data analyst.
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:Do you know anyone who's a data analyst?
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:Does your company hire data
analysts and have a conversation?
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:You're not even really
asking them anything.
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:You're just opening a conversation.
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:I know this is hard and I know it's
uncomfortable and I know it's not fun.
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:Like it's much more fun to learn data
skills than it is to network, but
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:honestly, networking gets you the same,
if not better results than upscaling
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:and actually learning new data things.
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:So you can't be ignoring this.
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:Couldn't be ignoring this.
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:I have to be networking,
no matter how hard it is.
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:Now, if all is going well, and I'm doing
all the previous eight things that I've
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:talked about, I think at this point
I'd probably start to land interviews.
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:There's two parts to an interview,
the technical and the behavioral.
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:The technical interview is when
they're going to be asking you
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:questions about data skills.
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:It might be like, Excel questions or data
visualization questions or oftentimes
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:sequel questions and I'll ask you to
write certain sequel queries This can
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:be really scary and intimidating and
honestly, they can be really hard The
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:cool part is they don't always occur
or or if they occur they occur very
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:easily Sometimes they're very hard.
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:Sometimes they're very easy.
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:It really just depends and to prepare
for the technical resources There's
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:a lot of things that I could do.
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:There's a lot of resources out
there that would help me prepare.
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:Um, there's something called Scrata
Scratch that I'll have a link in the show
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:notes down below that you guys can check.
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:There's Data Lemur.
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:There's a bunch of tools that
will help you prepare for
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:these technical interviews.
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:Behavioral interview is going to be
more like them trying to feel for
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:who you are and what you've done
previously and like how you would
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:act as a human being, as an employee.
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:And that is a little bit harder to
prepare for because it's more of like,
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:instead of answering technical questions,
it's answering like personal questions.
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:There's not a whole lot
of resources out there.
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:One of the things you would want
to do is use the STAR method.
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:You want to answer every question by
saying, this is the situation I was
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:in, this is the task I was given, this
was the action I took, and this is the
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:results that came from that action.
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:And if you answer using that method,
most of the time you'll be good.
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:It can be scary, and there's not a whole
lot of resources out there for this.
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:So do you want to check
out one that I made?
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:It's called interview simulator.
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:io, and it basically helps you
practice these questions where
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:I'll ask you the question via video
and you will respond via video.
321
:And then we'll actually grade your
answer and tell you what you did
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:well and where you could improve.
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:It's a pretty cool software.
324
:I'll link for that in the
show notes down below as well.
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:Wow, lots of links in the show
notes, so be sure to check those out.
326
:So those are the nine steps that I
would take if I had to start from
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:scratch and land a day job in 2025.
328
:And remember, I'm lazy, I'm trying
to do this the easiest way possible.
329
:This is This is what
I call the SPN method.
330
:You need to learn the right skills, not
all the skills, but the right skills.
331
:You need to build projects
and put them on a portfolio.
332
:That's the P part.
333
:And then you need to be
networking, updating your
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:LinkedIn and updating your resume.
335
:That's the N part.
336
:And it's the easiest
way to land a data job.
337
:Now you can do all this stuff that
I told you on your own and you'd
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:be 100 percent okay, but it's a lot
more fun to do it in community and
339
:it's a lot easier to do with a coach.
340
:Once again, I'm all about doing it
fast, And it's much easier to do
341
:that with a given curriculum where
you don't have to be questioning.
342
:Am I doing this right?
343
:How do I actually do this?
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:So on and so forth.
345
:And so that's why I created the data
analytics accelerator program, which
346
:is basically a 10 week bootcamp to
help you land your first day at a job.
347
:We'll go over all of these nine steps.
348
:Hand by hand, step by step together, and
make sure you're ready to land a data job.
349
:If you want to check that out,
you can go to datacareerjumpstart.
350
:com slash D A A D A A standing
for Data Analytics Accelerator.
351
:And of course, I'll have a link to
that in the show notes down below.
352
:Let me know what I missed
and what questions you have.
353
:I'll try to respond to everyone in
the comments down below if you're
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:watching on YouTube or on Spotify.
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:And I wish you the best of luck in 2025.