136: How I Would Become a Data Analyst In 2025 (if I had to start over again)

Avery: 00:00:00

Here's how I would become a data analyst if I had to

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start all over again in 2025.

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Now, I'm lazy and I'm impatient,

so this method that I'm going to

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be choosing, the SPN method, is the

fastest and it's the lowest amount

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of work to actually land a data job.

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But it still is a lot of work.

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Step one is I'd understand the different

data roles available in the data world.

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There are so many different data

roles, and it's not just data analysts.

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There are so many other roles, That

are just like data analysts, but

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have slightly different names and

slightly different responsibilities.

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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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: 00:13:35

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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: 00:13:57

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.

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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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: 00:14:18

It's a pretty cool software.

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: 00:14:20

I'll link for that in the

show notes down below as well.

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: 00:14:22

Wow, lots of links in the show

notes, so be sure to check those out.

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: 00:14:25

So those are the nine steps that I

would take if I had to start from

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: 00:14:29

scratch and land a day job in 2025.

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: 00:14:31

And remember, I'm lazy, I'm trying

to do this the easiest way possible.

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: 00:14:35

This is This is what

I call the SPN method.

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: 00:14:36

You need to learn the right skills, not

all the skills, but the right skills.

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: 00:14:39

You need to build projects

and put them on a portfolio.

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: 00:14:41

That's the P part.

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: 00:14:42

And then you need to be

networking, updating your

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LinkedIn and updating your resume.

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: 00:14:46

That's the N part.

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: 00:14:47

And it's the easiest

way to land a data job.

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: 00:14:50

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

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: 00:14:56

it's a lot easier to do with a coach.

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: 00:14:58

Once again, I'm all about doing it

fast, And it's much easier to do

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that with a given curriculum where

you don't have to be questioning.

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: 00:15:05

Am I doing this right?

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: 00:15:06

How do I actually do this?

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: 00:15:07

So on and so forth.

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: 00:15:08

And so that's why I created the data

analytics accelerator program, which

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: 00:15:11

is basically a 10 week bootcamp to

help you land your first day at a job.

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: 00:15:14

We'll go over all of these nine steps.

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Hand by hand, step by step together, and

make sure you're ready to land a data job.

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: 00:15:20

If you want to check that out,

you can go to datacareerjumpstart.

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: 00:15:23

com slash D A A D A A standing

for Data Analytics Accelerator.

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: 00:15:27

And of course, I'll have a link to

that in the show notes down below.

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: 00:15:30

Let me know what I missed

and what questions you have.

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: 00:15:32

I'll try to respond to everyone in

the comments down below if you're

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: 00:15:34

watching on YouTube or on Spotify.

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And I wish you the best of luck in 2025.