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

Speaker 2: 00:00:00

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.

268

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Probably the most common place

to have a portfolio is GitHub.

269

00:11:20,215 --> 00:11:23,725

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.

285

00:11:55,530 --> 00:11:57,209

So why not be where they are?

286

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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.

290

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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.

293

00:12:06,390 --> 00:12:09,360

We like to use LinkedIn articles

too, to make these projects go.

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And that's what I suggest.

295

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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.

300

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Um, I think it costs like nine to

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

301

00:12:26,995 --> 00:12:31,255

You guys, your portfolio looks, looks so

good and you can build it pretty quickly.

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00:12:31,255 --> 00:12:34,074

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

304

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with their information so it doesn't

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

305

00:12:40,155 --> 00:12:41,895

hours that it might take you to set up.

306

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But, uh, I really like card.

307

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I really like LinkedIn.

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00:12:44,385 --> 00:12:47,895

You could do it on Medium, you could

do it on any sort of Squarespace

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00:12:47,895 --> 00:12:50,025

or Wix or other website builder.

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00:12:50,275 --> 00:12:54,084

Also, if you like GitHub, there is

an alternative called GitHub pages.

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00:12:54,145 --> 00:12:56,875

GitHub realize, Hey, people

are using this as a portfolio.

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00:12:56,875 --> 00:13:00,745

We're not really built to be a portfolio,

so let's build a like separate product

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00:13:00,805 --> 00:13:03,625

that makes portfolios really well,

and that's called GitHub pages.

314

00:13:03,625 --> 00:13:05,845

And I really recommend that it's

just a little bit of a steep

315

00:13:05,845 --> 00:13:07,944

learning curve if you're not really.

316

00:13:08,135 --> 00:13:12,185

Knowing about GitHub or you don't

know about markdown, markdowns kind

317

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of like a programming language.

318

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It's kind of not, but uh, regardless

it's a little bit more technical, so

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I'd wanna make sure I have a portfolio.

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00:13:18,935 --> 00:13:24,005

Ideally in LinkedIn or card step five,

I'd need to make sure that my resume

321

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and LinkedIn are working for me.

322

00:13:26,315 --> 00:13:30,005

And these are really the only two tools

you get when you're trying to land a

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00:13:30,005 --> 00:13:32,225

data job and you need to invest in them.

324

00:13:32,435 --> 00:13:34,205

They need to be like little mini.

325

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Employees running around working for you.

326

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Okay.

327

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And let me talk about what I mean by that.

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00:13:38,935 --> 00:13:41,995

Number one, when you're applying for

jobs, your resume either is going

329

00:13:41,995 --> 00:13:45,715

to pass what's called the a TS, the

applicant tracking system, or it's not

330

00:13:45,865 --> 00:13:48,295

every time, it does not pass the a TS.

331

00:13:48,385 --> 00:13:49,855

There's kind of two scenarios.

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00:13:49,975 --> 00:13:53,335

One, your resume couldn't really

be read very well, and it's not.

333

00:13:53,550 --> 00:13:57,300

A TS compliant, meaning there's some

formatting issues on it, or two, you

334

00:13:57,300 --> 00:14:00,450

didn't fit what the job description

or the a TS was looking for.

335

00:14:00,600 --> 00:14:02,820

Number one, you wanna just make

sure that you have a really

336

00:14:02,820 --> 00:14:04,290

good a TS friendly resume.

337

00:14:04,290 --> 00:14:07,080

We give our students all a bunch of

templates that they can choose from, but

338

00:14:07,230 --> 00:14:12,330

the key here is basically no pictures,

one column, no tables, and make sure

339

00:14:12,330 --> 00:14:15,210

it's like pretty simple, like don't

try to do too much with your resume.

340

00:14:15,525 --> 00:14:19,185

Next, these ATSs, they're

honestly not very smart.

341

00:14:19,185 --> 00:14:20,745

Even with ai, they're kind of dumb.

342

00:14:20,835 --> 00:14:23,205

Basically what they're looking for

is they're looking at your resume

343

00:14:23,205 --> 00:14:25,005

and they're looking at the job

description, and they're trying to

344

00:14:25,005 --> 00:14:26,535

figure out if you're a match or not.

345

00:14:26,625 --> 00:14:28,635

Now, what would make you a match?

346

00:14:28,665 --> 00:14:29,325

Think about it.

347

00:14:29,385 --> 00:14:32,535

Whatever's on the job description

should match your resume, and so if

348

00:14:32,535 --> 00:14:34,335

you're applying for a data analyst role.

349

00:14:34,395 --> 00:14:34,995

Well, I'm sorry.

350

00:14:34,995 --> 00:14:37,454

You live in a world where they want

to hire someone with experience.

351

00:14:37,454 --> 00:14:39,824

There is no non-zero

experience jobs anymore.

352

00:14:39,975 --> 00:14:42,045

The lucky thing is we talked about

earlier how to create experience.

353

00:14:42,045 --> 00:14:44,204

So if you're applying for data

analyst jobs and you don't

354

00:14:44,204 --> 00:14:45,405

have the term data analyst.

355

00:14:45,704 --> 00:14:48,944

On your resume anywhere, you're

probably not gonna pass the a s, so

356

00:14:48,944 --> 00:14:50,385

you can kind of hack the system here.

357

00:14:50,535 --> 00:14:52,755

You can put it next to your

name at the top of your resume.

358

00:14:52,814 --> 00:14:56,115

You can put it in like your objective

statement at the top and or you

359

00:14:56,115 --> 00:14:59,235

can put it in your experience

section and have a data analyst job.

360

00:14:59,444 --> 00:15:01,995

That could be one that it's just

you making projects on your own.

361

00:15:02,055 --> 00:15:03,735

You could hire yourself,

start your own company.

362

00:15:03,735 --> 00:15:06,464

All of a sudden you're doing data,

freelance, data analytics, just you

363

00:15:06,464 --> 00:15:08,925

need to have the word data analyst, or

whatever role you're trying to apply

364

00:15:08,925 --> 00:15:12,225

for financial analysts, marketing

analysts, business intelligence engineer.

365

00:15:12,314 --> 00:15:14,175

You need to have that

somewhere on your resume.

366

00:15:14,220 --> 00:15:16,050

And if you don't, you're not

likely to get called back.

367

00:15:16,050 --> 00:15:19,080

So I'd wanna make sure that my

resume said data analyst like

368

00:15:19,320 --> 00:15:20,460

three or four different times.

369

00:15:20,520 --> 00:15:24,240

Now, on a similar note, if the

job description is asking for sql,

370

00:15:24,300 --> 00:15:27,630

I'll wanna make sure that I have

SQL on my resume multiple times.

371

00:15:27,630 --> 00:15:30,210

So once again, I wanna put

it in my skill section.

372

00:15:30,300 --> 00:15:33,720

Maybe I put it in my statement,

my objective at the top, uh, maybe

373

00:15:33,720 --> 00:15:36,660

I tried to put it in my bullet

points in my experience section.

374

00:15:36,720 --> 00:15:38,760

Maybe I have a project

section now on my resume.

375

00:15:38,760 --> 00:15:39,600

I'd want to put it there.

376

00:15:39,665 --> 00:15:43,505

You want to add as many

keywords as you can.

377

00:15:43,655 --> 00:15:47,555

If you don't have the word Excel, the

word sql, the word Tableau, power, bi,

378

00:15:47,555 --> 00:15:50,795

python, whatever, whatever terms you're

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

379

00:15:50,795 --> 00:15:52,265

resume, you're not gonna get interviews.

380

00:15:52,265 --> 00:15:56,344

So I wanna make sure that I

put SQL, Tableau in Excel, and

381

00:15:56,344 --> 00:15:57,694

in many places I possibly can.

382

00:15:57,960 --> 00:16:00,210

On my resume along with

a data analyst tile.

383

00:16:00,540 --> 00:16:02,340

Next, I'd wanna do the

same thing with LinkedIn.

384

00:16:02,430 --> 00:16:04,350

I wanna make sure that all

of my experience section

385

00:16:04,350 --> 00:16:05,580

on LinkedIn is filled out.

386

00:16:05,610 --> 00:16:07,200

I wanna make sure it has bullet points.

387

00:16:07,260 --> 00:16:09,180

I wanna make sure I have a

really good about section.

388

00:16:09,180 --> 00:16:12,870

I have a really good headline, a

clear profile picture, a good cover

389

00:16:12,870 --> 00:16:17,130

photo on LinkedIn, and make sure every

single part of my LinkedIn profile.

390

00:16:17,370 --> 00:16:18,480

Has information.

391

00:16:18,630 --> 00:16:19,140

Why?

392

00:16:19,200 --> 00:16:22,470

Because once again, 97% of recruiters,

these are the people who hire

393

00:16:22,470 --> 00:16:23,970

you, are on LinkedIn every day.

394

00:16:24,180 --> 00:16:26,160

And if they're on LinkedIn every

day, I think I should probably

395

00:16:26,160 --> 00:16:27,510

be on LinkedIn every day as well.

396

00:16:27,540 --> 00:16:30,810

I can't tell you how many times people

go through my program and they do

397

00:16:30,810 --> 00:16:33,180

our LinkedIn section, they update

their LinkedIn, and all of a sudden

398

00:16:33,180 --> 00:16:35,460

they have people reaching out to

them, recruiters, Hey, would you be

399

00:16:35,460 --> 00:16:36,540

interested to interview for this role?

400

00:16:36,540 --> 00:16:38,010

Would you be interested to

interview for that role?

401

00:16:38,070 --> 00:16:40,410

And all it does is take

some LinkedIn optimization.

402

00:16:40,655 --> 00:16:43,325

Once again, you want to keyword

stuff on your LinkedIn in as

403

00:16:43,325 --> 00:16:44,735

many places as you possibly can.

404

00:16:44,795 --> 00:16:48,275

Add skills, add whatever's in the job

description, put that on your LinkedIn.

405

00:16:48,855 --> 00:16:51,315

The other thing to kind of consider

on your resume in LinkedIn, and

406

00:16:51,315 --> 00:16:54,285

this is a little controversial,

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

407

00:16:54,285 --> 00:16:55,845

sorry, but this honestly helps you.

408

00:16:55,965 --> 00:16:58,725

Can you change any of

your previous titles?

409

00:16:58,815 --> 00:17:02,475

Can you go through your titles and can

you make them sound more data analyst?

410

00:17:02,715 --> 00:17:04,275

Can you add the word analyst anywhere?

411

00:17:04,335 --> 00:17:05,805

Can you add the word data anywhere?

412

00:17:05,954 --> 00:17:09,345

The more that you have data and

analyst on your resume in your

413

00:17:09,345 --> 00:17:10,950

title section of your experience?

414

00:17:11,415 --> 00:17:12,044

The better.

415

00:17:12,104 --> 00:17:14,145

So maybe you are a program specialist.

416

00:17:14,204 --> 00:17:16,544

Can we substitute the word

analyst for specialist?

417

00:17:16,574 --> 00:17:17,895

Would that be the end of the world?

418

00:17:17,983 --> 00:17:21,013

The term analyst is pretty broad,

so I feel like it's safe to do.

419

00:17:21,194 --> 00:17:24,284

And honestly like most titles

are all over the place.

420

00:17:24,284 --> 00:17:27,013

Like a title at one company

does not mean the same as what

421

00:17:27,013 --> 00:17:28,003

it would be at another company.

422

00:17:28,034 --> 00:17:28,875

They're all made up.

423

00:17:28,875 --> 00:17:31,215

There's no such thing as like

real titles, to be honest.

424

00:17:31,215 --> 00:17:32,745

So I think if you can do this.

425

00:17:32,755 --> 00:17:35,035

You should, and I honestly,

I would elect to do that.

426

00:17:35,035 --> 00:17:38,515

So chemical lab technician, maybe

I'd be chemical lab analyst.

427

00:17:38,575 --> 00:17:41,185

That feels like a little bit of

a stretch, but here's the key.

428

00:17:41,185 --> 00:17:45,595

If it feels like a stretch, just

remember you're just tricking the a TS.

429

00:17:45,685 --> 00:17:46,885

You could explain it to a human.

430

00:17:46,915 --> 00:17:50,515

Oh, that was actually more of

like, uh, lab like technician role.

431

00:17:50,545 --> 00:17:52,855

But I did do a little bit of

Excel analysis on that job.

432

00:17:53,005 --> 00:17:57,955

Humans can understand nuanced computers,

ATSs cannot, so I'd probably update

433

00:17:57,955 --> 00:17:59,455

my LinkedIn and resume those ways.

434

00:17:59,940 --> 00:18:02,670

Step six is I would need

to start applying for jobs.

435

00:18:02,700 --> 00:18:06,600

Um, obviously this might be really

obvious, but I'm not going to land

436

00:18:06,600 --> 00:18:08,400

a job if I don't apply for jobs.

437

00:18:08,490 --> 00:18:09,810

And the same is true for you.

438

00:18:09,870 --> 00:18:13,530

So if you're applying to only a few jobs

and you're not getting any bites and

439

00:18:13,530 --> 00:18:14,670

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

440

00:18:14,940 --> 00:18:16,590

The answer is apply for more jobs.

441

00:18:16,905 --> 00:18:19,905

Now, I hate saying that because I'm

also not a fan of just the spray and

442

00:18:19,905 --> 00:18:23,085

pray method where you're literally,

you know, bombing your resume out

443

00:18:23,085 --> 00:18:24,855

to hundreds of thousands of people.

444

00:18:24,855 --> 00:18:26,715

Like I don't think that

is a good method either.

445

00:18:26,775 --> 00:18:29,445

I think that there is kind of a

middle ground where you're applying,

446

00:18:29,505 --> 00:18:32,805

probably unfortunately, in today's

economy for hundreds of roles.

447

00:18:32,815 --> 00:18:37,705

But you're doing so in a targeted manner

with human-centric motion in mind.

448

00:18:37,735 --> 00:18:42,265

And what I mean by that is 67% of jobs

come from being recruited or referred.

449

00:18:42,325 --> 00:18:44,485

So that's why I really wanted

to update my LinkedIn earlier.

450

00:18:44,485 --> 00:18:44,695

Right.

451

00:18:44,695 --> 00:18:47,815

So I can get recruited, but let's

talk about referrals, referrals.

452

00:18:47,860 --> 00:18:48,850

Are amazing.

453

00:18:48,909 --> 00:18:53,050

This is when someone at a company will

refer you to a role at that company

454

00:18:53,139 --> 00:18:56,080

and hiring managers and recruiters

love that because if your friend's at

455

00:18:56,080 --> 00:18:59,290

a company and they're doing good work,

they probably like your friend and they

456

00:18:59,290 --> 00:19:02,709

would probably be glad to hire more

people like your friend, and hopefully

457

00:19:02,709 --> 00:19:03,939

you're just as good as your friend.

458

00:19:03,939 --> 00:19:03,949

So.

459

00:19:04,285 --> 00:19:05,905

Networking is really key here.

460

00:19:05,965 --> 00:19:08,995

You need, you need, you

need to be networking.

461

00:19:09,115 --> 00:19:12,535

If you're not networking, your job

hunt will take, I'm not even being

462

00:19:12,535 --> 00:19:13,945

dramatic here, 10 times longer.

463

00:19:14,065 --> 00:19:17,005

Networking is literally the key

to landing a data job quickly.

464

00:19:17,065 --> 00:19:17,755

Now, how do you do that?

465

00:19:17,755 --> 00:19:19,945

We talked about updating

our LinkedIn profile.

466

00:19:19,945 --> 00:19:20,725

That's a great start.

467

00:19:20,815 --> 00:19:23,395

I would also tell you to start

documenting your journey on

468

00:19:23,395 --> 00:19:25,135

LinkedIn via posts and comments.

469

00:19:25,225 --> 00:19:26,785

Um, that's what we teach our students.

470

00:19:26,785 --> 00:19:28,345

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

471

00:19:28,390 --> 00:19:31,780

But I've literally seen it work wonders

for so many students who had zero

472

00:19:31,780 --> 00:19:34,270

job experience and they were able

to land a data job because of that.

473

00:19:34,330 --> 00:19:36,100

If that sounds scary, no worries.

474

00:19:36,310 --> 00:19:39,460

You can go to your neighbor, you

can go to your cousin, you can go to

475

00:19:39,520 --> 00:19:42,310

your mom's friend's aunt and just be

like, Hey, what do you do for work?

476

00:19:42,310 --> 00:19:43,090

Pull out your phone.

477

00:19:43,150 --> 00:19:44,740

Go through every contact in your phone.

478

00:19:44,800 --> 00:19:47,230

Write down what every single

person does for work and

479

00:19:47,230 --> 00:19:48,550

where they work, and then ask.

480

00:19:48,610 --> 00:19:50,290

Would they ever hire a data analyst?

481

00:19:50,350 --> 00:19:52,719

Do they, do they have data analysts

working at their company now?

482

00:19:52,870 --> 00:19:54,310

If so, send them a message.

483

00:19:54,370 --> 00:19:57,070

Start with the people who in

your network already are in the

484

00:19:57,070 --> 00:19:58,240

data world or in the tech world.

485

00:19:58,270 --> 00:20:00,459

They can be really good resources

for you and if they're actually your

486

00:20:00,459 --> 00:20:02,889

friends, if they're actually your

family, they're willing to help you.

487

00:20:02,979 --> 00:20:04,149

They will be willing to help you.

488

00:20:04,240 --> 00:20:05,469

You just need to ask the right way.

489

00:20:05,530 --> 00:20:08,800

So a really easy way to not be intrusive,

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

490

00:20:08,800 --> 00:20:10,510

you're, you know, a program manager.

491

00:20:10,975 --> 00:20:12,745

At IBM, do you enjoy it?

492

00:20:12,895 --> 00:20:14,305

Just start the conversation that way.

493

00:20:14,455 --> 00:20:15,445

Oh, like, yeah, it's great.

494

00:20:15,445 --> 00:20:16,495

Yeah, it's awesome.

495

00:20:16,555 --> 00:20:17,635

You can be like, yeah, cool.

496

00:20:17,635 --> 00:20:19,285

I'm like looking to become a data analyst.

497

00:20:19,285 --> 00:20:20,785

Do you know any data analyst at IBM?

498

00:20:21,025 --> 00:20:22,045

Oh yeah, I know this guy.

499

00:20:22,075 --> 00:20:22,615

That's very cool.

500

00:20:22,615 --> 00:20:23,845

I can introduce you if you'd like.

501

00:20:23,965 --> 00:20:24,715

Oh yeah, that'd be great.

502

00:20:24,775 --> 00:20:27,235

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

even ask for anything right in that

503

00:20:27,235 --> 00:20:28,885

scenario, but I got what I wanted.

504

00:20:29,035 --> 00:20:31,255

So if you're not networking,

it's gonna be hard.

505

00:20:31,375 --> 00:20:32,575

You need to be applying for jobs.

506

00:20:32,800 --> 00:20:35,560

Also I recommend varying

where you apply for jobs.

507

00:20:35,739 --> 00:20:39,250

LinkedIn, great place to apply for

jobs, maybe check your local listings.

508

00:20:39,250 --> 00:20:42,040

Those will don't get as many

applicants and could be really,

509

00:20:42,040 --> 00:20:43,479

really easy to land interviews.

510

00:20:43,540 --> 00:20:45,010

Also, try other job platforms.

511

00:20:45,010 --> 00:20:46,899

I'm not gonna list them

all, but I'm biased.

512

00:20:46,899 --> 00:20:49,060

You can try find a data job.com.

513

00:20:49,060 --> 00:20:52,479

This is my free data job board where

I post a lot of different data jobs.

514

00:20:52,540 --> 00:20:54,010

I also have another one that is premium.

515

00:20:54,010 --> 00:20:54,610

It is paid.

516

00:20:54,790 --> 00:20:56,469

It's called premium data jobs.com.

517

00:20:56,500 --> 00:20:56,810

Those ones.

518

00:20:57,270 --> 00:21:00,270

Always have a recruiter or hiring manager

that you could reach out to today.

519

00:21:00,360 --> 00:21:01,590

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

520

00:21:01,590 --> 00:21:02,220

That's why it's paid.

521

00:21:02,280 --> 00:21:04,710

Check out both those, but just make

sure you're going to different job

522

00:21:04,710 --> 00:21:08,190

boards and trying different application

methods because it is a little bit of

523

00:21:08,190 --> 00:21:09,780

a luck, a little bit of a numbers game.

524

00:21:09,870 --> 00:21:13,050

Now, if I've done steps one through

six, I'm probably ready for steps

525

00:21:13,050 --> 00:21:16,889

seven, which is start landing

and preparing for interviews and.

526

00:21:17,135 --> 00:21:19,024

Interviews are how you seal the deal.

527

00:21:19,024 --> 00:21:20,885

That's how you actually

get job offers, right?

528

00:21:20,915 --> 00:21:22,445

But you shouldn't be stressed.

529

00:21:22,475 --> 00:21:26,165

I shouldn't be stressed about interviews

until I start landing them because there's

530

00:21:26,165 --> 00:21:27,725

two different separate skills here.

531

00:21:27,905 --> 00:21:32,435

The skills and the process of landing

interviews, and then the process of

532

00:21:32,435 --> 00:21:35,254

passing interviews, and those are

two different things, and you should

533

00:21:35,254 --> 00:21:38,879

prepare for them and work on them at

different times and in different ways.

534

00:21:39,465 --> 00:21:43,125

So I would not be stressed about an

interview until I've landed an interview.

535

00:21:43,274 --> 00:21:45,014

Once I landed an interview, I will cram.

536

00:21:45,794 --> 00:21:47,955

Uh, and there's lots of different things

you have to think about in an interview,

537

00:21:47,955 --> 00:21:52,274

but basically most data interviews

have two main parts, the behavioral

538

00:21:52,274 --> 00:21:53,925

part and then the technical part.

539

00:21:54,044 --> 00:21:54,824

The behavioral part.

540

00:21:54,824 --> 00:21:57,435

They're gonna be asking questions

that usually start with, tell me about

541

00:21:57,435 --> 00:21:59,415

a time, tell me about a time you.

542

00:21:59,875 --> 00:22:00,775

Had to be a leader.

543

00:22:00,835 --> 00:22:04,075

You had an issue with a coworker, and

these questions are basically like, let's

544

00:22:04,075 --> 00:22:08,035

look in their behavior in the past to

predict what they might do in the future.

545

00:22:08,095 --> 00:22:11,785

It's like, once again, the recruiter and

hiring manager here are trying to figure

546

00:22:11,785 --> 00:22:15,355

out how risky you are and hopefully

not how risky you are once you've.

547

00:22:15,440 --> 00:22:17,540

You've shown that, hey,

I'm a normal human being.

548

00:22:17,540 --> 00:22:18,140

I can work.

549

00:22:18,200 --> 00:22:20,810

They might ask more technical

questions, and a lot of the times

550

00:22:20,870 --> 00:22:24,650

this will be maybe Excel specific

questions or SQL specific questions.

551

00:22:24,680 --> 00:22:26,780

It kind of just depends on

the role and the company.

552

00:22:26,810 --> 00:22:30,050

There's so many platforms you

can try to prepare for these,

553

00:22:30,110 --> 00:22:31,160

these technical interviews.

554

00:22:31,220 --> 00:22:35,105

Just to list a few analyst builders,

strato, scratch, uh, data lemur.

555

00:22:35,105 --> 00:22:38,630

There's like so many different data

analyst prep, interview prep courses

556

00:22:38,630 --> 00:22:41,600

and classes and online things that I

don't wanna talk about it right now

557

00:22:41,630 --> 00:22:42,740

and you shouldn't worry about it.

558

00:22:42,740 --> 00:22:45,230

I'm not worrying about it until I

land interviews, but once you do.

559

00:22:45,525 --> 00:22:47,835

Those are right there for you to practice.

560

00:22:47,895 --> 00:22:51,255

So that's how I would hopefully land

my first data job if I was starting

561

00:22:51,255 --> 00:22:53,235

from absolute scratch this year.

562

00:22:53,295 --> 00:22:55,875

And if you joined this method,

we call it the SPN method.

563

00:22:55,875 --> 00:22:59,175

And what it means is it is not

just learning skills, that's

564

00:22:59,175 --> 00:23:00,705

the s part of the SPN method.

565

00:23:00,915 --> 00:23:02,025

If you're just learning skills.

566

00:23:02,070 --> 00:23:04,800

You're not gonna land interviews, you're

not gonna land jobs 'cause you're missing

567

00:23:04,800 --> 00:23:08,370

out on the other two thirds of the

equation for landing your first data job.

568

00:23:08,399 --> 00:23:11,370

The P in the N, the P stands

for projects in a portfolio.

569

00:23:11,520 --> 00:23:12,510

So that's what we talked about earlier.

570

00:23:12,510 --> 00:23:14,250

You need to have projects,

you need to have that proof

571

00:23:14,310 --> 00:23:15,419

and have it in a portfolio.

572

00:23:15,480 --> 00:23:19,110

And the last part is the N, which

is the networking, which is if, like

573

00:23:19,110 --> 00:23:21,330

I said, if you're not networking,

you're not gonna land a job.

574

00:23:21,360 --> 00:23:23,730

So if you like this roadmap and

you actually wanna follow it,

575

00:23:23,820 --> 00:23:26,490

please watch this video over and

over again until you can finally

576

00:23:26,490 --> 00:23:27,750

figure out exactly what I said.

577

00:23:27,840 --> 00:23:30,000

If you'd like a hand by hand guide.

578

00:23:30,135 --> 00:23:32,415

Walking you through all the

steps, literally giving you

579

00:23:32,565 --> 00:23:35,504

step-by-step instructions on this

is how you network, this is what

580

00:23:35,504 --> 00:23:36,495

your LinkedIn should look like.

581

00:23:36,555 --> 00:23:38,145

Here's a bunch of

projects that you can do.

582

00:23:38,175 --> 00:23:40,695

Here's a template for the

resume and for the portfolio.

583

00:23:40,905 --> 00:23:43,155

Then consider joining the

data analytics accelerator.

584

00:23:43,215 --> 00:23:46,245

This is my all-inclusive data

analytics bootcamp, where I'll

585

00:23:46,245 --> 00:23:48,585

take you from zero to data analyst.

586

00:23:48,645 --> 00:23:51,105

Literally, this has worked for so

many different people in my program

587

00:23:51,105 --> 00:23:52,185

from so many different backgrounds.

588

00:23:52,265 --> 00:23:55,985

We've helped teachers, truck drivers, Uber

drivers, warehouse workers, accountants,

589

00:23:55,985 --> 00:23:59,225

therapists, music therapists, like

whatever your current role is, we can

590

00:23:59,225 --> 00:24:02,495

probably help you transition into a data

analyst if you wanna check that out.

591

00:24:02,495 --> 00:24:04,085

I have a link in the

show notes down below.

592

00:24:04,235 --> 00:24:05,855

It's called the Data

Analytics Accelerator.

593

00:24:05,885 --> 00:24:08,855

I'll be your coach and my team will

help you land that First Data job.

594

00:24:08,945 --> 00:24:10,115

We're super excited to help you.

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