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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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Avery: 00:00:00

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

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: 00:00:02

start all over again in 2025.

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: 00:00:05

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

so this method that I'm going to

4

: 00:00:08

be choosing, the SPN method, is the

fastest and it's the lowest amount

5

: 00:00:12

of work to actually land a data job.

6

: 00:00:14

But it still is a lot of work.

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: 00:00:16

Step one is I'd understand the different

data roles available in the data world.

8

: 00:00:20

There are so many different data

roles, and it's not just data analysts.

9

: 00:00:24

There are so many other roles, That

are just like data analysts, but

10

: 00:00:27

have slightly different names and

slightly different responsibilities.

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: 00:00:30

For example, business intelligence

analyst, business intelligence

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: 00:00:33

engineer, technical data analyst,

business analyst, healthcare

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: 00:00:37

analyst, risk analyst, price analyst.

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: 00:00:39

There are so many, literally

so many different options that

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: 00:00:42

you could possibly choose from.

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: 00:00:43

And they're all pretty similar for

the most part, but some things are

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: 00:00:47

going to be slightly different.

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: 00:00:48

So for example, a healthcare analyst,

you're going to be a data analyst.

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: 00:00:51

But specializing and

looking at healthcare data.

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: 00:00:53

Financial analysts, same thing.

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: 00:00:55

You'd be looking at financial data.

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: 00:00:57

A BI analyst, like a business

intelligence analyst, and a data

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: 00:01:00

analyst, really a lot of the time are

going to be doing the exact same thing.

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: 00:01:03

So it's important to be looking for

all these roles, understand what these

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: 00:01:07

roles do and what their slight nuances

are, because there's a chance that

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: 00:01:10

your previous experience is actually

valuable and would help you get a leg

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: 00:01:15

up in applying for these different jobs.

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: 00:01:16

So for example, If you have a business

degree and you're trying to transfer

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: 00:01:19

into business analytics, becoming a

business analyst makes a lot of sense or

30

: 00:01:22

a financial analyst makes a lot of sense.

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: 00:01:24

If you've worked previously as

a nurse or like a CNA, maybe

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: 00:01:28

you become a healthcare analyst.

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: 00:01:30

Whatever you've done previously,

there's probably a good chance

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: 00:01:33

that that experience is valuable in

the data world to a specific role.

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: 00:01:37

So even like I have a lot of

truck drivers in my business.

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: 00:01:40

Bootcamp.

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: 00:01:40

Those truck drivers can be logistics

analysts, they can be operations

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: 00:01:43

analysts, they can be supply chain

analysts, because their previous

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: 00:01:46

experience is actually valuable.

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: 00:01:48

The second thing that I would do

is figure out what is actually

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: 00:01:51

required, because here's the truth.

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: 00:01:53

There is actually thousands

of data skills and tools.

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: 00:01:58

and programming languages out there,

but if you try to master all of them,

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: 00:02:02

you're going to be like 150 before you

feel prepared to start applying to jobs.

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: 00:02:07

You're going to be dead.

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: 00:02:08

It is impossible to learn.

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: 00:02:10

It's impossible to master

all the different data tools

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: 00:02:12

and skills and languages.

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: 00:02:13

So by default, have to choose a few.

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: 00:02:16

Now you have a decision to make

is which ones do you choose?

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: 00:02:19

And I, like I said, I am lazy and I want

to do the least amount of work possible.

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: 00:02:24

So I believe in the low hanging best.

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: 00:02:26

Tasting fruit analogy.

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: 00:02:28

If you can imagine that there's

a tree that has some sort of like

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: 00:02:30

a peach or an apple on it, right?

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: 00:02:31

The easiest fruit to grab is

always going to be the closest,

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: 00:02:34

so it's the lowest hanging fruit.

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: 00:02:36

But not only do you want the

lowest hanging fruit, you want

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: 00:02:38

the tastiest fruit, right?

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: 00:02:39

So this is stuff that is not only easy

to learn, but is extremely useful.

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: 00:02:44

Those are the things you want to focus on.

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: 00:02:45

Out of the thousands of data skills, those

are the ones you'll want to focus on.

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: 00:02:49

You can do the research on your own,

if you'd like, by looking at job

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: 00:02:51

descriptions and writing down what

is actually required, but that's a

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: 00:02:55

lot of work and you can take it from

someone like me, who's been in this

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: 00:02:57

space for about a decade now, looked at

literally thousands of job descriptions.

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: 00:03:01

I even have my own data job board.

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Findadatajob.

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: 00:03:04

com.

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And I look at it all the time

to see what is being required.

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: 00:03:07

So I've done this research for you

already, and I will have a link to

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: 00:03:11

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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: 00:03:19

That is it.

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: 00:03:20

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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: 00:03:24

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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: 00:03:42

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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: 00:04:02

And if you don't have a programming

background, it's going to take a

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: 00:04:04

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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: 00:04:09

What's a variable?

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: 00:04:10

What's a loop?

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What's a function?

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: 00:04:12

Those types of things just, they take

time and so if you only need it for

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: 00:04:16

30 percent of the jobs, that means 70

percent of the jobs don't require it.

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: 00:04:19

And once again, I am all about doing

the least amount of work possible

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: 00:04:22

and doing it as quickly as possible.

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: 00:04:23

So I say save Python for after your

first day at a job because it's really

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: 00:04:27

just not needed to land that first one.

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: 00:04:29

Once again, I have a free video that

kind of explains what skills you

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: 00:04:32

should learn and in what order and why.

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: 00:04:34

I'll have that in the

show notes down below.

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: 00:04:36

The third thing that I would do if I was

trying to become a data analyst is try

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: 00:04:40

to figure out how I'm going to convince

a hiring manager or recruiter to hire me,

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: 00:04:44

even though I have no prior experience.

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: 00:04:47

There's this thing called the cycle of

doom, which basically says I can't land a

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: 00:04:51

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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: 00:04:57

a job unless you have experience.

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: 00:04:59

You can never get experience

unless you get a job.

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: 00:05:01

It's kind of like the

chicken or the egg, you know?

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: 00:05:05

So you have to figure out, how am

I going to beat the cycle of doom?

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: 00:05:08

And how am I going to convince

someone that, yeah, I am a data

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: 00:05:11

analyst and you should hire me.

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: 00:05:13

How would I do it, personally?

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: 00:05:14

I'd build projects.

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: 00:05:15

Projects are a great way that

you can demonstrate your skills.

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: 00:05:19

It's basically the tangible evidence

for people to know that you can do

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: 00:05:23

what your resume says you can do.

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: 00:05:25

If you're unfamiliar with projects,

It's like almost doing pretend work

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: 00:05:28

where you're pretending that you're

working for a certain company.

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: 00:05:31

You take a data set and you analyze

it and publish your results.

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: 00:05:35

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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: 00:05:42

creating some sort of evidence, like

literally physical evidence that you can

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: 00:05:46

show to hiring managers, recruiters, and

be like, Hey, look, I can do these things.

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: 00:05:50

I can be a data analyst.

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: 00:05:51

I can use Excel.

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: 00:05:52

I can use SQL.

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: 00:05:53

I can create a data

visualization in Tableau.

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: 00:05:55

Once I understand those three

things, the fourth thing that I would

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: 00:05:58

personally do is start learning.

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: 00:06:00

And I want to emphasize

this is not the first thing.

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: 00:06:02

This is not the second thing.

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: 00:06:02

This is not the third thing.

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: 00:06:03

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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: 00:06:14

is the most realistic way to learn.

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: 00:06:16

I'll think it's It's the funnest

way to learn because just doing like

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: 00:06:19

pointless exercises on like these

like interactive online learning

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: 00:06:24

things, this is not realistic.

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: 00:06:25

Like in real life, you're going

to be having real data sets.

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: 00:06:27

You're not going to be in some

like controlled environment.

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: 00:06:30

You're actually going to have to be

analyzing real data that's messy,

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: 00:06:33

that has issues that has flaws

and you have to figure it out.

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: 00:06:35

And so building projects is the

best way to learn because you're

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: 00:06:38

also creating this tangible evidence

that you're going to be able to show

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: 00:06:40

to hiring managers and recruiters.

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: 00:06:42

You might be thinking, well,

where do I get started?

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: 00:06:43

Well, you need to figure out

where you can find datasets.

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: 00:06:46

You have to have a good dataset.

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: 00:06:47

I just did an episode on this

recently, and I'll have the link

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: 00:06:49

to the show notes down below.

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: 00:06:50

But the simple answer, the

one word answer is Kaggle.

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: 00:06:53

Kaggle is the best

place to find a dataset.

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: 00:06:55

It's not the only place, and there's

other great resources, but if

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: 00:06:57

you're only looking for one, Kaggle

is usually the place I would go.

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: 00:07:00

And I'd personally build projects based

off of what you want to do ultimately.

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: 00:07:04

So go back to step one and think about it.

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: 00:07:06

Like if you have a business degree, let's

say you want to become a business analyst,

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: 00:07:09

I would try to build projects that are

relevant to, to business analytics.

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: 00:07:13

Maybe data on sales or marketing

or operations, anything

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: 00:07:17

that's business related.

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: 00:07:18

Those are the projects

I would try to seek out.

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: 00:07:20

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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: 00:07:25

You'll just take whatever you've got.

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: 00:07:26

I would suggest doing projects

on lots of different industries.

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Maybe dip into healthcare analytics.

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: 00:07:31

Maybe do some people and HR analytics.

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: 00:07:33

Maybe do a project on

manufacturing and engineering data.

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: 00:07:37

That way you're getting exposed

to multiple different industries,

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: 00:07:39

so you can kind of figure out

maybe what you're interested in.

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: 00:07:42

You're creating a robust portfolio

that will be attractive to every

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: 00:07:45

industry and multiple companies, right?

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: 00:07:47

Because if you just focus on creating,

you know, business projects, but

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: 00:07:50

let's say you want to become a

healthcare analyst, it's like, oh,

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: 00:07:52

those projects don't really match up.

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: 00:07:54

So.

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That way you have a project for whatever

role you might be interested in.

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: 00:07:57

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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: 00:08:06

And we built multiple projects

in different industries.

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: 00:08:08

So that way we're very robust as can.

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: 00:08:10

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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: 00:08:15

And this is actually

what's called a portfolio.

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: 00:08:17

You know, projects are something that

we do but if you just do them and you

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: 00:08:20

don't publish them and you don't share

them, they don't actually do much good.

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: 00:08:23

You need to create a portfolio

to home these projects.

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: 00:08:26

And the portfolio platform you'll

hear the most about is GitHub.

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: 00:08:29

And I have a controversial

take that I'm not a fan of it.

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: 00:08:31

I don't think GitHub is

meant to be a portfolio.

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: 00:08:34

Now that's me being a little bit picky,

but I just don't think it's the best

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: 00:08:38

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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: 00:08:42

because if you have a good readme

on your GitHub, then it can work.

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: 00:08:45

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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: 00:08:50

Or choose GitHub Pages, which is from

GitHub, but kind of a separate product,

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: 00:08:54

and it's their portfolio solution.

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: 00:08:55

It's actually what GitHub

recommends as a portfolio.

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: 00:08:58

Or I really like Card, C A R R D.

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: 00:09:00

It's just a simple website builder,

be really great options inside the

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: 00:09:04

accelerator, my bootcamp, so any of

those three would work just fine.

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: 00:09:06

The sixth thing I would do is make

sure that my LinkedIn and resume

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: 00:09:10

are up to date and optimized.

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: 00:09:12

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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: 00:09:18

The earlier you do this,

the better, because.

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: 00:09:19

Your LinkedIn is your professional

business card to the world.

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: 00:09:23

One of the really cool things is LinkedIn

has a feature called Open to Work.

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: 00:09:26

There's two different settings on it.

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: 00:09:27

We can talk about it later, but

basically you can have Open to Work

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: 00:09:31

for the entire world or you can just

have Open to Work for recruiters.

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: 00:09:33

And either way, if you set up

your LinkedIn correctly, your

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: 00:09:36

LinkedIn can start to work for you.

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: 00:09:38

And instead of you going out and

applying for jobs, recruiters

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: 00:09:40

and hiring managers are actually

applying to you for specific jobs.

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: 00:09:43

They'll reach out to you and be like, Hey,

I think you're a good fit for this job.

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: 00:09:46

So having an optimized

LinkedIn is, is really key.

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: 00:09:48

And then of course, having an

optimized resume is a must because

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: 00:09:52

once you start applying for jobs.

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: 00:09:53

If your resume isn't optimized, you're

probably not going to get many interviews.

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: 00:09:57

And the reason is there's so many

candidates trying to get into data

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: 00:10:01

analytics roles, especially the

entry level ones, that recruiters

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: 00:10:04

and hiring managers have to use

what's called the ATS, which is

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: 00:10:07

the Applicant Tracking System.

234

: 00:10:08

And basically it's, it's computer, it's

AI, it's It's actually not even really

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: 00:10:13

that complicated, but there are certain

things you need to do on your resume to

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: 00:10:16

have it be optimized and ATS friendly, so

you can get past the computer screening

237

: 00:10:20

and actually have a human being look at

your resume, because it's so frustrating

238

: 00:10:24

when you get rejection after rejection

after rejection that you don't even know

239

: 00:10:27

if a human's looking at your resume.

240

: 00:10:29

A lot of the times you're just getting

rejected by the ATS, and so you need to

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: 00:10:32

make sure you have an optimized resume.

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: 00:10:34

So, in terms of having an optimized

resume, it would basically look like

243

: 00:10:36

not having any columns on your resume,

or any tables on your resume, and

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: 00:10:40

then using really key words that match

the job descriptions, so that way you

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: 00:10:43

appear as a good applicant to the ATS.

246

: 00:10:46

The seventh step that I would take

is to start applying, and I think

247

: 00:10:50

this is obvious, but a lot of people

don't ever start applying for jobs.

248

: 00:10:54

And I get it, because it's scary.

249

: 00:10:56

How do you know if you're

ready to land a data job?

250

: 00:10:58

It's hard to know, and you probably

will never feel ready, so I suggest

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: 00:11:02

just start applying anyways.

252

: 00:11:03

And when you start applying,

don't only apply on LinkedIn jobs.

253

: 00:11:07

LinkedIn jobs is where everyone applies,

and there's going to be hundreds of

254

: 00:11:10

candidates in a matter of a few days

on those platforms, the majority of the

255

: 00:11:14

time, because everyone's doing that.

256

: 00:11:16

So you might want to try something

new, like going to company websites or

257

: 00:11:20

checking out my job board, findadatajob.

258

: 00:11:22

com or some other combination

of other job websites.

259

: 00:11:25

The point here is you need to

be looking at multiple places

260

: 00:11:28

and actually start applying.

261

: 00:11:30

I know it's scary, but just do it scared.

262

: 00:11:32

The next step I would do in this process

is I would really try to be networking.

263

: 00:11:36

And I, I would try to be networking

the entire time, like even in step one.

264

: 00:11:39

But this is where I fit on

today's roadmap is step eight.

265

: 00:11:42

So it's way easier to get

hired when you know someone.

266

: 00:11:46

In fact, my brother was just recently

looking for a job and having a

267

: 00:11:49

hard time and he ended up Getting

an interview and landing that job

268

: 00:11:52

because his wife's friend works there.

269

: 00:11:55

And like, I can't tell you how

often that actually happens.

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: 00:11:58

So networking doesn't have to be hard.

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: 00:12:00

You can do it on LinkedIn by

posting and commenting on LinkedIn.

272

: 00:12:02

I think that's really important to do, but

I understand that's hard and a scary step.

273

: 00:12:07

One thing that's really a lot easier is

just to talk to your friends and family.

274

: 00:12:10

Just say, Hey, I'm trying

to become a data analyst.

275

: 00:12:12

Do you know anyone who's a data analyst?

276

: 00:12:14

Does your company hire data

analysts and have a conversation?

277

: 00:12:17

You're not even really

asking them anything.

278

: 00:12:19

You're just opening a conversation.

279

: 00:12:20

I know this is hard and I know it's

uncomfortable and I know it's not fun.

280

: 00:12:24

Like it's much more fun to learn data

skills than it is to network, but

281

: 00:12:27

honestly, networking gets you the same,

if not better results than upscaling

282

: 00:12:31

and actually learning new data things.

283

: 00:12:33

So you can't be ignoring this.

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: 00:12:34

Couldn't be ignoring this.

285

: 00:12:35

I have to be networking,

no matter how hard it is.

286

: 00:12:38

Now, if all is going well, and I'm doing

all the previous eight things that I've

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: 00:12:41

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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: 00:13:15

There's a lot of resources out

there that would help me prepare.

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: 00:13:18

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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: 00:13:43

There's not a whole lot

of resources out there.

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: 00:13:45

One of the things you would want

to do is use the STAR method.

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: 00:13:48

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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: 00:14:00

It can be scary, and there's not a whole

lot of resources out there for this.

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: 00:14:03

So do you want to check

out one that I made?

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: 00:14:04

It's called interview simulator.

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: 00:14:06

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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: 00:14:14

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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: 00:14:53

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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: 00:15:02

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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: 00:15:16

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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: 00:15:36

And I wish you the best of luck in 2025.