185: How I Would Become a Data Analyst in 2026 (if I had to start over again)

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Here's exactly how I would become a data analyst if I

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had to start all over again in 2026.

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Now I'm low key, pretty lazy,

and I'm also very impatient.

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So I'd want to choose the fastest

roadmap with the least amount of work

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required to actually land a data job.

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That roadmap is called the SPN method,

but it still has a lot of work.

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Step one, I'd wanna figure out

exactly what skills are required

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because there's literally.

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Thousands of different data

tools and skills that you

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could possibly be learning.

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And if you're gonna master them

all, it's gonna take you so long.

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It's gonna take you decades before

you even feel close to ready.

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Once again, remember, I'm very

lazy and I'm very impatient.

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So I want to learn the bare minimum of

skills required to land my first data job.

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So which skills and what

tools would I focus on?

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Ideally, I choose the skills that

have the biggest bang for your

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buck, the lowest hanging fruit.

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So basically what that means are the

ones that are used the most in industry.

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But also the ones that are the easiest

to learn, so I can learn them quickly.

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That way I could have employable in-demand

skills really, really, really fast.

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Uh, so what are those skills?

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You're probably wondering, well, you

can do the research for yourself by

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going through like hundreds, thousands

of different job descriptions and

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keeping tallies and track of what data

tools are mentioned the most often.

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But obviously that's

gonna be a lot of work.

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The good news is I already did all that

research and work for you, so here you go.

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The most in demand tools that are

also pretty easy to learn are Excel.

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Tableau sql.

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Literally, that's it in that order.

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These are the top three data skills

that you should be learning when you're

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just starting out in data analytics.

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And if you need any help remembering

that I came up with something called

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a pneumonic, I think is what it's

called to make it kind of easy.

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It's every turtle swims.

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E for Excel, T four

Tableau and S four sql.

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And that's where I'd personally start.

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If I had to start all over, I wouldn't

really study anything else until

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after landing that first data job.

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Now I can hear everyone

in the comments already.

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Well, what about Python

and what about Power bi?

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And here's the truth, I love Python.

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It's literally my favorite data tool.

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But honestly, there is a little

bit of a steep learning curve,

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and it's only required in like.

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13% of data analyst jobs.

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It just takes so freaking long to learn.

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And remember, I'm not trying to be

in this job hunting mode forever.

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I'm trying to land a data job quickly.

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So learning Python, it's gonna

take a freaking long time.

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And to me, it's just not worth the

time investment at the beginning

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because it's not the most in demand

skill and it's not the easiest.

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So it makes sense for me to

leave it till later, and at that

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point I can probably learn it.

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On the job, so I'm gonna be getting

paid to learn and I'm all about

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that, so sign me up for that.

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In fact, I did a video in the

past about how to get paid to

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learn stuff in data analytics.

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You can check that out right there.

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Step two, I'd wanna make sure I understand

all the different data jobs available.

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Obviously there's data analyst and

that is a great place to start.

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In fact, I think it's the best

place to start, but there's actually

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so many more jobs than just.

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That they all have slightly different

names and slightly different

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responsibilities, but a lot of the times

they're doing pretty similar stuff to

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what you'd be doing as a data analyst.

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So the first two I wanna talk about

are data scientists and data engineer.

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If you're just getting started,

I would not try to get those jobs

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because it is hard to land those roles.

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It requires a lot of programming knowledge

and math knowledge land, those roles.

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And I just think they're

really hard to land.

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So instead, I'd focus on things like

data analyst, financial analyst,

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healthcare analyst, marketing analyst.

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Almost anything that has the word analyst

in it, or that might have the word

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data in it, I would at least consider.

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Now, there's so many different

jobs here and I can't possibly

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tell you every single one, but

let's just start with the big one.

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So financial analyst and business

analysts are two of the most

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common analyst roles I've been

seeing on job boards quite a bit.

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In fact, I run my own data job board.

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We'll talk about it here in a

second, but on that job board.

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Financial analyst and business

analyst roles are pretty much more

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common than data analyst roles.

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The financial analyst roles you're

going to be dealing with, like p and

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ls, a little bit more profit and loss

statements, uh, a little bit like more

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kind of data plus accounting, e uh, a

little bit about forecasting and just

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like how much cash you have on hand.

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A business analyst role, that's like

half business, half data analyst

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kind of meet in the middle, so

their jobs can be quite varied,

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um, in what they're actually doing.

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But a lot of the times they're just like.

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Approaching business problems

with like Excel or with Tableau or

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with SQL or something like that.

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The next most common one is healthcare

analyst, and it is kind of self-evident,

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but basically you're doing data

analytics with healthcare data.

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A lot of the times you'd think that this

is like looking at medical charts and.

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Different medicines and

procedures and stuff like that.

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But honestly, unfortunately, a lot of

the healthcare analyst roles are more

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about the operations of healthcare,

like appointments and billing, uh,

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and scheduling and stuff like that.

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There's a huge demand for healthcare

analyst roles, and I don't see that

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demand going away anytime soon.

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So this is a great role, especially

if you have healthcare experience

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in the past, if you've worked

maybe as a nurse or some sort of.

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Medical tech, this could

be a great fit for you.

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Marketing analyst, once again,

very self-evident in the name,

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but basically you're doing data

analytics on marketing data.

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If you've ever worked as a

marketer, if you know anything

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about ads, if you know anything

about social media or like website

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analytics, this is a great place to.

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For you to start now.

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There's so many more jobs I can't even

talk about right now in this video.

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So here's a big list on the screen

right here, and if you're listening

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to the audio version, I'll have a

link in the show notes down below.

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But there's so many

different data jobs you guys.

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So pause this video, take a screenshot of

this, and start looking for these jobs.

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The reason you wanna start looking for

these roles instead of data analyst roles

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is one less people know about these roles,

so they're going to have less applicants.

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And two, a lot of the time.

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Your domain experience is going to

be very valuable for these roles.

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So for example, if you've been

an accountant before, a financial

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analyst role is a really good

fit for you because you already

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have that accounting experience.

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So when you go to apply to financial

analyst jobs, they can look at

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your resume and be like, oh, this

person's already been an accountant.

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They're gonna understand this

data set better than most.

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And that's something that

I'd have to take in as well.

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So in my previous life, I was a chemical

lab technician, so I'd be probably

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looking for data jobs that maybe have

to do with laboratory data or companies

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that deal with some sort of chemicals.

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Now there's also a bunch of like these

in-between jobs that are like half

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data jobs, half domain jobs, um, and

they're a little bit more entry level.

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They require less skills.

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Maybe they only require

Excel, for example.

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You've probably never heard of

these jobs and that's totally okay.

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I made a whole separate video,

so you can watch that on YouTube

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right here, or we'll have a link to

it and the show notes down below.

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And that will basically explain these

roles that are a little bit more entry

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level than even a data analyst role.

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They don't pay as well as data

analyst role, but you could probably

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land them today if you know Excel.

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So once again, check that out.

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And honestly, if I had to start all

over again, I might go for one of

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these roles first because when I

was a chemical lab technician, I was

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making like $15 an hour, and these

roles are like closer to $25 an hour.

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So I might wanna start with one of these

roles, get the word data on my resume,

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and then start applying for data analyst

jobs after I get data on my resume.

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Step three is I need to figure

out a way to convince a hiring

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manager to actually hire me.

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Why would anyone wanna hire me?

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I'm a chemical lab technician.

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I've never been a data analyst.

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I don't have very many data skills,

like why on earth would someone hire me?

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Um, and you've maybe felt this way before.

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I call it the circle of doom.

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It's basically like I can't

get data experience because I

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can't get a data job because.

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I can't get data experience.

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And so this never ending cycle of doom

where it's like, how the heck am I ever

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supposed to get a job when I don't have

experience, but I can't get experience?

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'cause no one's gonna gimme a job.

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And honestly, it's the absolute worst.

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If you're in the circle of

doom right now, let me know in

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the comments and I'm so sorry.

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That is not a fun place to be.

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But here's the truth, is you could

actually create your own experience

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and you do that by building projects.

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Now a project is basically.

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A real world life example

of you analyzing data.

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It's almost like you have some sort of

proof that like, hey, not only does my

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resume say that I can do Excel, that I

can analyze data in sql, that I can make

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a Tableau dashboard, but here's some

tangible proof via project that I can.

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And it's one thing to know the skills.

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It's another thing to show

that you know the skills.

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And those are different things.

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So think about it, if I'm

like interviewing with a

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hiring manager and I'm.

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Tell the hiring manager, Hey, yeah,

I know sql, I've been learning sql.

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They're gonna be like, well,

can you prove it to me?

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Right?

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And if I can have a project where

like, I'm like, yes, I can look it.

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Here's some healthcare

data that I analyzed.

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You know, here's some financial

transactions that I analyzed.

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Here's some manufacturing sensor data

that I actually analyzed, and I created

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this dashboard for you in Tableau.

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See how powerful that is.

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All of a sudden, the hiring manager is

like on the defense at the beginning,

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like, I don't know if this person

actually can do what we need them to do.

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Two, oh my gosh, this person already

has done what I need them to do.

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Here's the evidence.

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I like this person.

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I mean, it's hard to do, but put

yourself in the hiring manager's shoes.

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Let's say that you were a hiring manager.

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For like the next Fast and the

Furious movie that's coming out and

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you need to hire a stunt double.

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Let's say you get two applicants.

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Applicant, a, you know, on their resume

it says that they can jump over a car.

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Great.

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Uh, applicant B'S resume also

says they can jump over a car.

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Fantastic, but they also send a

video of them jumping over a car.

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Who are you more likely to hire?

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Uh, option A or option.

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It's option B, right?

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Why?

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Think about it for a second, because

they gave evidence that they can

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do what the job description says.

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They took the risk out of it

because now that I'm on the other

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side of, I hire people, right?

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I'm a hiring manager now and I

hired some wrong people this year

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and it has bit me in the butt.

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It has cost me honestly

thousands of dollars, uh,

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because I didn't hire correctly.

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And so when you are, you know, trying

to convince a hiring manager that

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you are the right person, if you

can lower that risk with projects.

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All of a sudden you're

breaking the circle of doom.

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You have experience and you're

letting the hiring manager know in a

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undeniable way, Hey, I've got this.

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Don't worry about me.

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So I would need to

start building projects.

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And if I didn't know where to go or how

to start building projects, you always

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gotta start with a dataset and you

gotta find a dataset somewhere online.

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So one of the best places you

can find data sets, well, there's

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a bunch of different options.

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I actually did a whole nother

video about it right here, you

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can find in the show notes.

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Um, but the short answer is Kaggle.

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Kaggle is a great place to

find, uh, a data set like.

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90% of the time, and usually

that's like good enough.

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So that's where I'd start.

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And then in terms of like what

to do in the project, first

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pick, should you do it in Excel?

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Should you do it in sql?

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Should you do it in Tableau?

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Uh, just pick whatever one you're maybe

the best at, and then start to answer some

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business questions about the data set.

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Think about how many, what's

the max, what's the average?

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What's the relationship

between these two columns?

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What happens over time?

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Those are some of the questions that you

can ask at the beginning, and you can just

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answer maybe two or three or four of 'em,

and all of a sudden you have a project.

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You have evidence, all of a

sudden you have experience.

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And I would be qualified, or at

least I would be able to talk to a

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hiring manager with like some sort

of defense like, no, I am good.

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You should hire me.

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So I need to build projects.

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Step four, I would need to create

a home for these projects, right?

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Because if you do these projects.

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But they're not tangible, then.

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They're not tangible.

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And how are you gonna convince the hiring

manager that you're the person, right?

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So if your project is just in your

head, it doesn't really count.

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If it's just on your desktop,

it doesn't really count.

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That doesn't do you any good.

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You need this to be public.

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You need this to be easily shareable.

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You need this to look good and look

pretty and make yourself look good, right?

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This is really key to have a portfolio.

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So a portfolio is basically a home.

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For your projects, and you'll want to have

maybe one to, I don't know, 10 different

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projects that that's a big order.

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It depends on the, the

quality of your projects.

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One really, really, really good

project could be better than

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like seven mediocre projects.

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It really just depends.

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So where should you build your portfolio?

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There's a couple different options.

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And I teach all these different options

inside of my program, the data Analytics

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accelerator, and I actually give them

templates to just do this really easily.

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Probably the most common place

to have a portfolio is GitHub.

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Uh, but I don't like GitHub as

a portfolio for data analysts.

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Um, I can hear you guys in the comments.

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Oh, GitHub's awesome for data scientists

and data engineers and programmers.

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Yeah, I get it.

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Okay.

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But a lot of you guys at the beginning.

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You're not gonna be writing code.

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GitHub is literally meant for code.

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Now you can kind of reverse engineer,

hack it and make it for anything, and

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it, it could work as a good portfolio,

but it's really hard to navigate and it's

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really hard to look good inside of GitHub.

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Just trust me on this and try one

of these other things instead.

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I really like to use LinkedIn.

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LinkedIn.

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That's a great place where

recruiters are right?

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Like it's like 97% of recruiters are

actively using LinkedIn every single day.

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So why not be where they are?

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Right?

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Because those are the people

that can change your life.

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Those are the people that

can all of a sudden reach out

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to you and offer you a job.

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So I like using LinkedIn.

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There's a featured section on there.

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There's a project section on there.

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We like to use LinkedIn articles

too, to make these projects go.

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And that's what I suggest.

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That's one of the things I

teach inside of my bootcamp.

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The next thing I also do inside

the bootcamp is card dot, uh, co.

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I think.

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I'll, I'll put a link, uh, right here

and in the show notes down below.

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But basically it's just a website

builder, a simple website builder.

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Um, I think it costs like nine to

$20 a year and it's so worth it.

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You guys, your portfolio looks, looks so

good and you can build it pretty quickly.

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So, uh, our students inside of

our bootcamp actually just get.

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This template from us right here, that

they can literally just fill in the blanks

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with their information so it doesn't

take them like the, I don't know, couple

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hours that it might take you to set up.

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But, uh, I really like card.

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I really like LinkedIn.

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You could do it on Medium, you could

do it on any sort of Squarespace

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or Wix or other website builder.

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Also, if you like GitHub, there is

an alternative called GitHub pages.

311

: 00:12:54

GitHub realize, Hey, people

are using this as a portfolio.

312

: 00:12:56

We're not really built to be a portfolio,

so let's build a like separate product

313

: 00:13:00

that makes portfolios really well,

and that's called GitHub pages.

314

: 00:13:03

And I really recommend that it's

just a little bit of a steep

315

: 00:13:05

learning curve if you're not really.

316

: 00:13:08

Knowing about GitHub or you don't

know about markdown, markdowns kind

317

: 00:13:12

of like a programming language.

318

: 00:13:13

It's kind of not, but uh, regardless

it's a little bit more technical, so

319

: 00:13:17

I'd wanna make sure I have a portfolio.

320

: 00:13:18

Ideally in LinkedIn or card step five,

I'd need to make sure that my resume

321

: 00:13:24

and LinkedIn are working for me.

322

: 00:13:26

And these are really the only two tools

you get when you're trying to land a

323

: 00:13:30

data job and you need to invest in them.

324

: 00:13:32

They need to be like little mini.

325

: 00:13:34

Employees running around working for you.

326

: 00:13:36

Okay.

327

: 00:13:36

And let me talk about what I mean by that.

328

: 00:13:38

Number one, when you're applying for

jobs, your resume either is going

329

: 00:13:41

to pass what's called the a TS, the

applicant tracking system, or it's not

330

: 00:13:45

every time, it does not pass the a TS.

331

: 00:13:48

There's kind of two scenarios.

332

: 00:13:49

One, your resume couldn't really

be read very well, and it's not.

333

: 00:13:53

A TS compliant, meaning there's some

formatting issues on it, or two, you

334

: 00:13:57

didn't fit what the job description

or the a TS was looking for.

335

: 00:14:00

Number one, you wanna just make

sure that you have a really

336

: 00:14:02

good a TS friendly resume.

337

: 00:14:04

We give our students all a bunch of

templates that they can choose from, but

338

: 00:14:07

the key here is basically no pictures,

one column, no tables, and make sure

339

: 00:14:12

it's like pretty simple, like don't

try to do too much with your resume.

340

: 00:14:15

Next, these ATSs, they're

honestly not very smart.

341

: 00:14:19

Even with ai, they're kind of dumb.

342

: 00:14:20

Basically what they're looking for

is they're looking at your resume

343

: 00:14:23

and they're looking at the job

description, and they're trying to

344

: 00:14:25

figure out if you're a match or not.

345

: 00:14:26

Now, what would make you a match?

346

: 00:14:28

Think about it.

347

: 00:14:29

Whatever's on the job description

should match your resume, and so if

348

: 00:14:32

you're applying for a data analyst role.

349

: 00:14:34

Well, I'm sorry.

350

: 00:14:34

You live in a world where they want

to hire someone with experience.

351

: 00:14:37

There is no non-zero

experience jobs anymore.

352

: 00:14:39

The lucky thing is we talked about

earlier how to create experience.

353

: 00:14:42

So if you're applying for data

analyst jobs and you don't

354

: 00:14:44

have the term data analyst.

355

: 00:14:45

On your resume anywhere, you're

probably not gonna pass the a s, so

356

: 00:14:48

you can kind of hack the system here.

357

: 00:14:50

You can put it next to your

name at the top of your resume.

358

: 00:14:52

You can put it in like your objective

statement at the top and or you

359

: 00:14:56

can put it in your experience

section and have a data analyst job.

360

: 00:14:59

That could be one that it's just

you making projects on your own.

361

: 00:15:02

You could hire yourself,

start your own company.

362

: 00:15:03

All of a sudden you're doing data,

freelance, data analytics, just you

363

: 00:15:06

need to have the word data analyst, or

whatever role you're trying to apply

364

: 00:15:08

for financial analysts, marketing

analysts, business intelligence engineer.

365

: 00:15:12

You need to have that

somewhere on your resume.

366

: 00:15:14

And if you don't, you're not

likely to get called back.

367

: 00:15:16

So I'd wanna make sure that my

resume said data analyst like

368

: 00:15:19

three or four different times.

369

: 00:15:20

Now, on a similar note, if the

job description is asking for sql,

370

: 00:15:24

I'll wanna make sure that I have

SQL on my resume multiple times.

371

: 00:15:27

So once again, I wanna put

it in my skill section.

372

: 00:15:30

Maybe I put it in my statement,

my objective at the top, uh, maybe

373

: 00:15:33

I tried to put it in my bullet

points in my experience section.

374

: 00:15:36

Maybe I have a project

section now on my resume.

375

: 00:15:38

I'd want to put it there.

376

: 00:15:39

You want to add as many

keywords as you can.

377

: 00:15:43

If you don't have the word Excel, the

word sql, the word Tableau, power, bi,

378

: 00:15:47

python, whatever, whatever terms you're

trying to go for, if those aren't on your

379

: 00:15:50

resume, you're not gonna get interviews.

380

: 00:15:52

So I wanna make sure that I

put SQL, Tableau in Excel, and

381

: 00:15:56

in many places I possibly can.

382

: 00:15:57

On my resume along with

a data analyst tile.

383

: 00:16:00

Next, I'd wanna do the

same thing with LinkedIn.

384

: 00:16:02

I wanna make sure that all

of my experience section

385

: 00:16:04

on LinkedIn is filled out.

386

: 00:16:05

I wanna make sure it has bullet points.

387

: 00:16:07

I wanna make sure I have a

really good about section.

388

: 00:16:09

I have a really good headline, a

clear profile picture, a good cover

389

: 00:16:12

photo on LinkedIn, and make sure every

single part of my LinkedIn profile.

390

: 00:16:17

Has information.

391

: 00:16:18

Why?

392

: 00:16:19

Because once again, 97% of recruiters,

these are the people who hire

393

: 00:16:22

you, are on LinkedIn every day.

394

: 00:16:24

And if they're on LinkedIn every

day, I think I should probably

395

: 00:16:26

be on LinkedIn every day as well.

396

: 00:16:27

I can't tell you how many times people

go through my program and they do

397

: 00:16:30

our LinkedIn section, they update

their LinkedIn, and all of a sudden

398

: 00:16:33

they have people reaching out to

them, recruiters, Hey, would you be

399

: 00:16:35

interested to interview for this role?

400

: 00:16:36

Would you be interested to

interview for that role?

401

: 00:16:38

And all it does is take

some LinkedIn optimization.

402

: 00:16:40

Once again, you want to keyword

stuff on your LinkedIn in as

403

: 00:16:43

many places as you possibly can.

404

: 00:16:44

Add skills, add whatever's in the job

description, put that on your LinkedIn.

405

: 00:16:48

The other thing to kind of consider

on your resume in LinkedIn, and

406

: 00:16:51

this is a little controversial,

so uh, if you don't like it, I'm

407

: 00:16:54

sorry, but this honestly helps you.

408

: 00:16:55

Can you change any of

your previous titles?

409

: 00:16:58

Can you go through your titles and can

you make them sound more data analyst?

410

: 00:17:02

Can you add the word analyst anywhere?

411

: 00:17:04

Can you add the word data anywhere?

412

: 00:17:05

The more that you have data and

analyst on your resume in your

413

: 00:17:09

title section of your experience?

414

: 00:17:11

The better.

415

: 00:17:12

So maybe you are a program specialist.

416

: 00:17:14

Can we substitute the word

analyst for specialist?

417

: 00:17:16

Would that be the end of the world?

418

: 00:17:17

The term analyst is pretty broad,

so I feel like it's safe to do.

419

: 00:17:21

And honestly like most titles

are all over the place.

420

: 00:17:24

Like a title at one company

does not mean the same as what

421

: 00:17:27

it would be at another company.

422

: 00:17:28

They're all made up.

423

: 00:17:28

There's no such thing as like

real titles, to be honest.

424

: 00:17:31

So I think if you can do this.

425

: 00:17:32

You should, and I honestly,

I would elect to do that.

426

: 00:17:35

So chemical lab technician, maybe

I'd be chemical lab analyst.

427

: 00:17:38

That feels like a little bit of

a stretch, but here's the key.

428

: 00:17:41

If it feels like a stretch, just

remember you're just tricking the a TS.

429

: 00:17:45

You could explain it to a human.

430

: 00:17:46

Oh, that was actually more of

like, uh, lab like technician role.

431

: 00:17:50

But I did do a little bit of

Excel analysis on that job.

432

: 00:17:53

Humans can understand nuanced computers,

ATSs cannot, so I'd probably update

433

: 00:17:57

my LinkedIn and resume those ways.

434

: 00:17:59

Step six is I would need

to start applying for jobs.

435

: 00:18:02

Um, obviously this might be really

obvious, but I'm not going to land

436

: 00:18:06

a job if I don't apply for jobs.

437

: 00:18:08

And the same is true for you.

438

: 00:18:09

So if you're applying to only a few jobs

and you're not getting any bites and

439

: 00:18:13

you're like, why can't I land a job?

440

: 00:18:14

The answer is apply for more jobs.

441

: 00:18:16

Now, I hate saying that because I'm

also not a fan of just the spray and

442

: 00:18:19

pray method where you're literally,

you know, bombing your resume out

443

: 00:18:23

to hundreds of thousands of people.

444

: 00:18:24

Like I don't think that

is a good method either.

445

: 00:18:26

I think that there is kind of a

middle ground where you're applying,

446

: 00:18:29

probably unfortunately, in today's

economy for hundreds of roles.

447

: 00:18:32

But you're doing so in a targeted manner

with human-centric motion in mind.

448

: 00:18:37

And what I mean by that is 67% of jobs

come from being recruited or referred.

449

: 00:18:42

So that's why I really wanted

to update my LinkedIn earlier.

450

: 00:18:44

Right.

451

: 00:18:44

So I can get recruited, but let's

talk about referrals, referrals.

452

: 00:18:47

Are amazing.

453

: 00:18:48

This is when someone at a company will

refer you to a role at that company

454

: 00:18:53

and hiring managers and recruiters

love that because if your friend's at

455

: 00:18:56

a company and they're doing good work,

they probably like your friend and they

456

: 00:18:59

would probably be glad to hire more

people like your friend, and hopefully

457

: 00:19:02

you're just as good as your friend.

458

: 00:19:03

So.

459

: 00:19:04

Networking is really key here.

460

: 00:19:05

You need, you need, you

need to be networking.

461

: 00:19:09

If you're not networking, your job

hunt will take, I'm not even being

462

: 00:19:12

dramatic here, 10 times longer.

463

: 00:19:14

Networking is literally the key

to landing a data job quickly.

464

: 00:19:17

Now, how do you do that?

465

: 00:19:17

We talked about updating

our LinkedIn profile.

466

: 00:19:19

That's a great start.

467

: 00:19:20

I would also tell you to start

documenting your journey on

468

: 00:19:23

LinkedIn via posts and comments.

469

: 00:19:25

Um, that's what we teach our students.

470

: 00:19:26

I know that's scary for a lot of you.

471

: 00:19:28

But I've literally seen it work wonders

for so many students who had zero

472

: 00:19:31

job experience and they were able

to land a data job because of that.

473

: 00:19:34

If that sounds scary, no worries.

474

: 00:19:36

You can go to your neighbor, you

can go to your cousin, you can go to

475

: 00:19:39

your mom's friend's aunt and just be

like, Hey, what do you do for work?

476

: 00:19:42

Pull out your phone.

477

: 00:19:43

Go through every contact in your phone.

478

: 00:19:44

Write down what every single

person does for work and

479

: 00:19:47

where they work, and then ask.

480

: 00:19:48

Would they ever hire a data analyst?

481

: 00:19:50

Do they, do they have data analysts

working at their company now?

482

: 00:19:52

If so, send them a message.

483

: 00:19:54

Start with the people who in

your network already are in the

484

: 00:19:57

data world or in the tech world.

485

: 00:19:58

They can be really good resources

for you and if they're actually your

486

: 00:20:00

friends, if they're actually your

family, they're willing to help you.

487

: 00:20:02

They will be willing to help you.

488

: 00:20:04

You just need to ask the right way.

489

: 00:20:05

So a really easy way to not be intrusive,

it's just to be like, Hey, I know that

490

: 00:20:08

you're, you know, a program manager.

491

: 00:20:10

At IBM, do you enjoy it?

492

: 00:20:12

Just start the conversation that way.

493

: 00:20:14

Oh, like, yeah, it's great.

494

: 00:20:15

Yeah, it's awesome.

495

: 00:20:16

You can be like, yeah, cool.

496

: 00:20:17

I'm like looking to become a data analyst.

497

: 00:20:19

Do you know any data analyst at IBM?

498

: 00:20:21

Oh yeah, I know this guy.

499

: 00:20:22

That's very cool.

500

: 00:20:22

I can introduce you if you'd like.

501

: 00:20:23

Oh yeah, that'd be great.

502

: 00:20:24

See, I didn't even ask, I didn't

even ask for anything right in that

503

: 00:20:27

scenario, but I got what I wanted.

504

: 00:20:29

So if you're not networking,

it's gonna be hard.

505

: 00:20:31

You need to be applying for jobs.

506

: 00:20:32

Also I recommend varying

where you apply for jobs.

507

: 00:20:35

LinkedIn, great place to apply for

jobs, maybe check your local listings.

508

: 00:20:39

Those will don't get as many

applicants and could be really,

509

: 00:20:42

really easy to land interviews.

510

: 00:20:43

Also, try other job platforms.

511

: 00:20:45

I'm not gonna list them

all, but I'm biased.

512

: 00:20:46

You can try find a data job.com.

513

: 00:20:49

This is my free data job board where

I post a lot of different data jobs.

514

: 00:20:52

I also have another one that is premium.

515

: 00:20:54

It is paid.

516

: 00:20:54

It's called premium data jobs.com.

517

: 00:20:56

Those ones.

518

: 00:20:57

Always have a recruiter or hiring manager

that you could reach out to today.

519

: 00:21:00

So that's why it's a little bit special.

520

: 00:21:01

That's why it's paid.

521

: 00:21:02

Check out both those, but just make

sure you're going to different job

522

: 00:21:04

boards and trying different application

methods because it is a little bit of

523

: 00:21:08

a luck, a little bit of a numbers game.

524

: 00:21:09

Now, if I've done steps one through

six, I'm probably ready for steps

525

: 00:21:13

seven, which is start landing

and preparing for interviews and.

526

: 00:21:17

Interviews are how you seal the deal.

527

: 00:21:19

That's how you actually

get job offers, right?

528

: 00:21:20

But you shouldn't be stressed.

529

: 00:21:22

I shouldn't be stressed about interviews

until I start landing them because there's

530

: 00:21:26

two different separate skills here.

531

: 00:21:27

The skills and the process of landing

interviews, and then the process of

532

: 00:21:32

passing interviews, and those are

two different things, and you should

533

: 00:21:35

prepare for them and work on them at

different times and in different ways.

534

: 00:21:39

So I would not be stressed about an

interview until I've landed an interview.

535

: 00:21:43

Once I landed an interview, I will cram.

536

: 00:21:45

Uh, and there's lots of different things

you have to think about in an interview,

537

: 00:21:47

but basically most data interviews

have two main parts, the behavioral

538

: 00:21:52

part and then the technical part.

539

: 00:21:54

The behavioral part.

540

: 00:21:54

They're gonna be asking questions

that usually start with, tell me about

541

: 00:21:57

a time, tell me about a time you.

542

: 00:21:59

Had to be a leader.

543

: 00:22:00

You had an issue with a coworker, and

these questions are basically like, let's

544

: 00:22:04

look in their behavior in the past to

predict what they might do in the future.

545

: 00:22:08

It's like, once again, the recruiter and

hiring manager here are trying to figure

546

: 00:22:11

out how risky you are and hopefully

not how risky you are once you've.

547

: 00:22:15

You've shown that, hey,

I'm a normal human being.

548

: 00:22:17

I can work.

549

: 00:22:18

They might ask more technical

questions, and a lot of the times

550

: 00:22:20

this will be maybe Excel specific

questions or SQL specific questions.

551

: 00:22:24

It kind of just depends on

the role and the company.

552

: 00:22:26

There's so many platforms you

can try to prepare for these,

553

: 00:22:30

these technical interviews.

554

: 00:22:31

Just to list a few analyst builders,

strato, scratch, uh, data lemur.

555

: 00:22:35

There's like so many different data

analyst prep, interview prep courses

556

: 00:22:38

and classes and online things that I

don't wanna talk about it right now

557

: 00:22:41

and you shouldn't worry about it.

558

: 00:22:42

I'm not worrying about it until I

land interviews, but once you do.

559

: 00:22:45

Those are right there for you to practice.

560

: 00:22:47

So that's how I would hopefully land

my first data job if I was starting

561

: 00:22:51

from absolute scratch this year.

562

: 00:22:53

And if you joined this method,

we call it the SPN method.

563

: 00:22:55

And what it means is it is not

just learning skills, that's

564

: 00:22:59

the s part of the SPN method.

565

: 00:23:00

If you're just learning skills.

566

: 00:23:02

You're not gonna land interviews, you're

not gonna land jobs 'cause you're missing

567

: 00:23:04

out on the other two thirds of the

equation for landing your first data job.

568

: 00:23:08

The P in the N, the P stands

for projects in a portfolio.

569

: 00:23:11

So that's what we talked about earlier.

570

: 00:23:12

You need to have projects,

you need to have that proof

571

: 00:23:14

and have it in a portfolio.

572

: 00:23:15

And the last part is the N, which

is the networking, which is if, like

573

: 00:23:19

I said, if you're not networking,

you're not gonna land a job.

574

: 00:23:21

So if you like this roadmap and

you actually wanna follow it,

575

: 00:23:23

please watch this video over and

over again until you can finally

576

: 00:23:26

figure out exactly what I said.

577

: 00:23:27

If you'd like a hand by hand guide.

578

: 00:23:30

Walking you through all the

steps, literally giving you

579

: 00:23:32

step-by-step instructions on this

is how you network, this is what

580

: 00:23:35

your LinkedIn should look like.

581

: 00:23:36

Here's a bunch of

projects that you can do.

582

: 00:23:38

Here's a template for the

resume and for the portfolio.

583

: 00:23:40

Then consider joining the

data analytics accelerator.

584

: 00:23:43

This is my all-inclusive data

analytics bootcamp, where I'll

585

: 00:23:46

take you from zero to data analyst.

586

: 00:23:48

Literally, this has worked for so

many different people in my program

587

: 00:23:51

from so many different backgrounds.

588

: 00:23:52

We've helped teachers, truck drivers, Uber

drivers, warehouse workers, accountants,

589

: 00:23:55

therapists, music therapists, like

whatever your current role is, we can

590

: 00:23:59

probably help you transition into a data

analyst if you wanna check that out.

591

: 00:24:02

I have a link in the

show notes down below.

592

: 00:24:04

It's called the Data

Analytics Accelerator.

593

: 00:24:05

I'll be your coach and my team will

help you land that First Data job.

594

: 00:24:08

We're super excited to help you.