November 11, 2025 • 25 mins
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⌚ TIMESTAMPS
00:19 - Step 1: Skills
02:33 - Step 2: Data Roles
06:38 - Step 3: Projects
10:22 - Step 4: Portfolio
13:20 - Step 5: Resume & LinkedIn
17:59 - Step 6: Job Hunting
21:12 - Step 7: Interviews
22:53 - The SPN Method
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 2 (00:00):
Here's exactly how I would become a data analyst if I
had to start all over again in 2026.
Now I'm low key, pretty lazy,and I'm also very impatient.
So I'd want to choose the fastestroadmap with the least amount of work
required to actually land a data job.
That roadmap is called the SPN method,but it still has a lot of work.
Step one, I'd wanna figure outexactly what skills are required
(00:23):
because there's literally.
Thousands of different datatools and skills that you
could possibly be learning.
And if you're gonna master themall, it's gonna take you so long.
It's gonna take you decades beforeyou even feel close to ready.
Once again, remember, I'm verylazy and I'm very impatient.
So I want to learn the bare minimum ofskills required to land my first data job.
So which skills and whattools would I focus on?
(00:45):
Ideally, I choose the skills thathave the biggest bang for your
buck, the lowest hanging fruit.
So basically what that means are theones that are used the most in industry.
But also the ones that are the easiestto learn, so I can learn them quickly.
That way I could have employable in-demandskills really, really, really fast.
Uh, so what are those skills?
You're probably wondering, well, youcan do the research for yourself by
going through like hundreds, thousandsof different job descriptions and
(01:07):
keeping tallies and track of what datatools are mentioned the most often.
But obviously that'sgonna be a lot of work.
The good news is I already did all thatresearch and work for you, so here you go.
The most in demand tools that arealso pretty easy to learn are Excel.
Tableau sql.
Literally, that's it in that order.
These are the top three data skillsthat you should be learning when you're
(01:29):
just starting out in data analytics.
And if you need any help rememberingthat I came up with something called
a pneumonic, I think is what it'scalled to make it kind of easy.
It's every turtle swims.
E for Excel, T fourTableau and S four sql.
And that's where I'd personally start.
If I had to start all over, I wouldn'treally study anything else until
after landing that first data job.
Now I can hear everyonein the comments already.
(01:50):
Well, what about Pythonand what about Power bi?
And here's the truth, I love Python.
It's literally my favorite data tool.
But honestly, there is a littlebit of a steep learning curve,
and it's only required in like.
13% of data analyst jobs.
It just takes so freaking long to learn.
And remember, I'm not trying to bein this job hunting mode forever.
I'm trying to land a data job quickly.
So learning Python, it's gonnatake a freaking long time.
(02:13):
And to me, it's just not worth thetime investment at the beginning
because it's not the most in demandskill and it's not the easiest.
So it makes sense for me toleave it till later, and at that
point I can probably learn it.
On the job, so I'm gonna be gettingpaid to learn and I'm all about
that, so sign me up for that.
In fact, I did a video in thepast about how to get paid to
learn stuff in data analytics.
You can check that out right there.
(02:34):
Step two, I'd wanna make sure I understandall the different data jobs available.
Obviously there's data analyst andthat is a great place to start.
In fact, I think it's the bestplace to start, but there's actually
so many more jobs than just.
That they all have slightly differentnames and slightly different
responsibilities, but a lot of the timesthey're doing pretty similar stuff to
what you'd be doing as a data analyst.
(02:54):
So the first two I wanna talk aboutare data scientists and data engineer.
If you're just getting started,I would not try to get those jobs
because it is hard to land those roles.
It requires a lot of programming knowledgeand math knowledge land, those roles.
And I just think they'rereally hard to land.
So instead, I'd focus on things likedata analyst, financial analyst,
healthcare analyst, marketing analyst.
Almost anything that has the word analystin it, or that might have the word
(03:16):
data in it, I would at least consider.
Now, there's so many differentjobs here and I can't possibly
tell you every single one, butlet's just start with the big one.
So financial analyst and businessanalysts are two of the most
common analyst roles I've beenseeing on job boards quite a bit.
In fact, I run my own data job board.
We'll talk about it here in asecond, but on that job board.
Financial analyst and businessanalyst roles are pretty much more
(03:37):
common than data analyst roles.
The financial analyst roles you'regoing to be dealing with, like p and
ls, a little bit more profit and lossstatements, uh, a little bit like more
kind of data plus accounting, e uh, alittle bit about forecasting and just
like how much cash you have on hand.
A business analyst role, that's likehalf business, half data analyst
kind of meet in the middle, sotheir jobs can be quite varied,
(03:58):
um, in what they're actually doing.
But a lot of the times they're just like.
Approaching business problemswith like Excel or with Tableau or
with SQL or something like that.
The next most common one is healthcareanalyst, and it is kind of self-evident,
but basically you're doing dataanalytics with healthcare data.
A lot of the times you'd think that thisis like looking at medical charts and.
Different medicines andprocedures and stuff like that.
(04:20):
But honestly, unfortunately, a lot ofthe healthcare analyst roles are more
about the operations of healthcare,like appointments and billing, uh,
and scheduling and stuff like that.
There's a huge demand for healthcareanalyst roles, and I don't see that
demand going away anytime soon.
So this is a great role, especiallyif you have healthcare experience
in the past, if you've workedmaybe as a nurse or some sort of.
(04:41):
Medical tech, this couldbe a great fit for you.
Marketing analyst, once again,very self-evident in the name,
but basically you're doing dataanalytics on marketing data.
If you've ever worked as amarketer, if you know anything
about ads, if you know anythingabout social media or like website
analytics, this is a great place to.
For you to start now.
There's so many more jobs I can't eventalk about right now in this video.
So here's a big list on the screenright here, and if you're listening
(05:03):
to the audio version, I'll have alink in the show notes down below.
But there's so manydifferent data jobs you guys.
So pause this video, take a screenshot ofthis, and start looking for these jobs.
The reason you wanna start looking forthese roles instead of data analyst roles
is one less people know about these roles,so they're going to have less applicants.
And two, a lot of the time.
Your domain experience is going tobe very valuable for these roles.
(05:24):
So for example, if you've beenan accountant before, a financial
analyst role is a really goodfit for you because you already
have that accounting experience.
So when you go to apply to financialanalyst jobs, they can look at
your resume and be like, oh, thisperson's already been an accountant.
They're gonna understand thisdata set better than most.
And that's something thatI'd have to take in as well.
So in my previous life, I was a chemicallab technician, so I'd be probably
looking for data jobs that maybe haveto do with laboratory data or companies
(05:48):
that deal with some sort of chemicals.
Now there's also a bunch of like thesein-between jobs that are like half
data jobs, half domain jobs, um, andthey're a little bit more entry level.
They require less skills.
Maybe they only requireExcel, for example.
You've probably never heard ofthese jobs and that's totally okay.
I made a whole separate video,so you can watch that on YouTube
right here, or we'll have a link toit and the show notes down below.
(06:09):
And that will basically explain theseroles that are a little bit more entry
level than even a data analyst role.
They don't pay as well as dataanalyst role, but you could probably
land them today if you know Excel.
So once again, check that out.
And honestly, if I had to start allover again, I might go for one of
these roles first because when Iwas a chemical lab technician, I was
making like $15 an hour, and theseroles are like closer to $25 an hour.
(06:29):
So I might wanna start with one of theseroles, get the word data on my resume,
and then start applying for data analystjobs after I get data on my resume.
Step three is I need to figureout a way to convince a hiring
manager to actually hire me.
Why would anyone wanna hire me?
I'm a chemical lab technician.
I've never been a data analyst.
I don't have very many data skills,like why on earth would someone hire me?
(06:50):
Um, and you've maybe felt this way before.
I call it the circle of doom.
It's basically like I can'tget data experience because I
can't get a data job because.
I can't get data experience.
And so this never ending cycle of doomwhere it's like, how the heck am I ever
supposed to get a job when I don't haveexperience, but I can't get experience?
'cause no one's gonna gimme a job.
And honestly, it's the absolute worst.
If you're in the circle ofdoom right now, let me know in
(07:12):
the comments and I'm so sorry.
That is not a fun place to be.
But here's the truth, is you couldactually create your own experience
and you do that by building projects.
Now a project is basically.
A real world life exampleof you analyzing data.
It's almost like you have some sort ofproof that like, hey, not only does my
resume say that I can do Excel, that Ican analyze data in sql, that I can make
(07:34):
a Tableau dashboard, but here's sometangible proof via project that I can.
And it's one thing to know the skills.
It's another thing to showthat you know the skills.
And those are different things.
So think about it, if I'mlike interviewing with a
hiring manager and I'm.
Tell the hiring manager, Hey, yeah,I know sql, I've been learning sql.
They're gonna be like, well,can you prove it to me?
Right?
And if I can have a project wherelike, I'm like, yes, I can look it.
(07:57):
Here's some healthcaredata that I analyzed.
You know, here's some financialtransactions that I analyzed.
Here's some manufacturing sensor datathat I actually analyzed, and I created
this dashboard for you in Tableau.
See how powerful that is.
All of a sudden, the hiring manager islike on the defense at the beginning,
like, I don't know if this personactually can do what we need them to do.
Two, oh my gosh, this person alreadyhas done what I need them to do.
(08:18):
Here's the evidence.
I like this person.
I mean, it's hard to do, but putyourself in the hiring manager's shoes.
Let's say that you were a hiring manager.
For like the next Fast and theFurious movie that's coming out and
you need to hire a stunt double.
Let's say you get two applicants.
Applicant, a, you know, on their resumeit says that they can jump over a car.
Great.
Uh, applicant B'S resume alsosays they can jump over a car.
Fantastic, but they also send avideo of them jumping over a car.
(08:42):
Who are you more likely to hire?
Uh, option A or option.
It's option B, right?
Why?
Think about it for a second, becausethey gave evidence that they can
do what the job description says.
They took the risk out of itbecause now that I'm on the other
side of, I hire people, right?
I'm a hiring manager now and Ihired some wrong people this year
and it has bit me in the butt.
It has cost me honestlythousands of dollars, uh,
(09:04):
because I didn't hire correctly.
And so when you are, you know, tryingto convince a hiring manager that
you are the right person, if youcan lower that risk with projects.
All of a sudden you'rebreaking the circle of doom.
You have experience and you'reletting the hiring manager know in a
undeniable way, Hey, I've got this.
Don't worry about me.
So I would need tostart building projects.
And if I didn't know where to go or howto start building projects, you always
(09:27):
gotta start with a dataset and yougotta find a dataset somewhere online.
So one of the best places youcan find data sets, well, there's
a bunch of different options.
I actually did a whole nothervideo about it right here, you
can find in the show notes.
Um, but the short answer is Kaggle.
Kaggle is a great place tofind, uh, a data set like.
90% of the time, and usuallythat's like good enough.
So that's where I'd start.
And then in terms of like whatto do in the project, first
(09:49):
pick, should you do it in Excel?
Should you do it in sql?
Should you do it in Tableau?
Uh, just pick whatever one you're maybethe best at, and then start to answer some
business questions about the data set.
Think about how many, what'sthe max, what's the average?
What's the relationshipbetween these two columns?
What happens over time?
Those are some of the questions that youcan ask at the beginning, and you can just
answer maybe two or three or four of 'em,and all of a sudden you have a project.
(10:11):
You have evidence, all of asudden you have experience.
And I would be qualified, or atleast I would be able to talk to a
hiring manager with like some sortof defense like, no, I am good.
You should hire me.
So I need to build projects.
Step four, I would need to createa home for these projects, right?
Because if you do these projects.
But they're not tangible, then.
They're not tangible.
And how are you gonna convince the hiringmanager that you're the person, right?
(10:34):
So if your project is just in yourhead, it doesn't really count.
If it's just on your desktop,it doesn't really count.
That doesn't do you any good.
You need this to be public.
You need this to be easily shareable.
You need this to look good and lookpretty and make yourself look good, right?
This is really key to have a portfolio.
So a portfolio is basically a home.
For your projects, and you'll want to havemaybe one to, I don't know, 10 different
(10:56):
projects that that's a big order.
It depends on the, thequality of your projects.
One really, really, really goodproject could be better than
like seven mediocre projects.
It really just depends.
So where should you build your portfolio?
There's a couple different options.
And I teach all these different optionsinside of my program, the data Analytics
accelerator, and I actually give themtemplates to just do this really easily.
(11:16):
Probably the most common placeto have a portfolio is GitHub.
Uh, but I don't like GitHub asa portfolio for data analysts.
Um, I can hear you guys in the comments.
Oh, GitHub's awesome for data scientistsand data engineers and programmers.
Yeah, I get it.
Okay.
But a lot of you guys at the beginning.
You're not gonna be writing code.
GitHub is literally meant for code.
Now you can kind of reverse engineer,hack it and make it for anything, and
(11:38):
it, it could work as a good portfolio,but it's really hard to navigate and it's
really hard to look good inside of GitHub.
Just trust me on this and try oneof these other things instead.
I really like to use LinkedIn.
LinkedIn.
That's a great place whererecruiters are right?
Like it's like 97% of recruiters areactively using LinkedIn every single day.
So why not be where they are?
Right?
Because those are the peoplethat can change your life.
(11:58):
Those are the people thatcan all of a sudden reach out
to you and offer you a job.
So I like using LinkedIn.
There's a featured section on there.
There's a project section on there.
We like to use LinkedIn articlestoo, to make these projects go.
And that's what I suggest.
That's one of the things Iteach inside of my bootcamp.
The next thing I also do insidethe bootcamp is card dot, uh, co.
I think.
I'll, I'll put a link, uh, right hereand in the show notes down below.
(12:20):
But basically it's just a websitebuilder, a simple website builder.
Um, I think it costs like nine to$20 a year and it's so worth it.
You guys, your portfolio looks, looks sogood and you can build it pretty quickly.
So, uh, our students inside ofour bootcamp actually just get.
This template from us right here, thatthey can literally just fill in the blanks
with their information so it doesn'ttake them like the, I don't know, couple
hours that it might take you to set up.
(12:41):
But, uh, I really like card.
I really like LinkedIn.
You could do it on Medium, you coulddo it on any sort of Squarespace
or Wix or other website builder.
Also, if you like GitHub, there isan alternative called GitHub pages.
GitHub realize, Hey, peopleare using this as a portfolio.
We're not really built to be a portfolio,so let's build a like separate product
that makes portfolios really well,and that's called GitHub pages.
(13:03):
And I really recommend that it'sjust a little bit of a steep
learning curve if you're not really.
Knowing about GitHub or you don'tknow about markdown, markdowns kind
of like a programming language.
It's kind of not, but uh, regardlessit's a little bit more technical, so
I'd wanna make sure I have a portfolio.
Ideally in LinkedIn or card step five,I'd need to make sure that my resume
(13:24):
and LinkedIn are working for me.
And these are really the only two toolsyou get when you're trying to land a
data job and you need to invest in them.
They need to be like little mini.
Employees running around working for you.
Okay.
And let me talk about what I mean by that.
Number one, when you're applying forjobs, your resume either is going
to pass what's called the a TS, theapplicant tracking system, or it's not
(13:45):
every time, it does not pass the a TS.
There's kind of two scenarios.
One, your resume couldn't reallybe read very well, and it's not.
A TS compliant, meaning there's someformatting issues on it, or two, you
didn't fit what the job descriptionor the a TS was looking for.
Number one, you wanna just makesure that you have a really
good a TS friendly resume.
We give our students all a bunch oftemplates that they can choose from, but
(14:07):
the key here is basically no pictures,one column, no tables, and make sure
it's like pretty simple, like don'ttry to do too much with your resume.
Next, these ATSs, they'rehonestly not very smart.
Even with ai, they're kind of dumb.
Basically what they're looking foris they're looking at your resume
and they're looking at the jobdescription, and they're trying to
figure out if you're a match or not.
Now, what would make you a match?
(14:28):
Think about it.
Whatever's on the job descriptionshould match your resume, and so if
you're applying for a data analyst role.
Well, I'm sorry.
You live in a world where they wantto hire someone with experience.
There is no non-zeroexperience jobs anymore.
The lucky thing is we talked aboutearlier how to create experience.
So if you're applying for dataanalyst jobs and you don't
have the term data analyst.
On your resume anywhere, you'reprobably not gonna pass the a s, so
(14:48):
you can kind of hack the system here.
You can put it next to yourname at the top of your resume.
You can put it in like your objectivestatement at the top and or you
can put it in your experiencesection and have a data analyst job.
That could be one that it's justyou making projects on your own.
You could hire yourself,start your own company.
All of a sudden you're doing data,freelance, data analytics, just you
need to have the word data analyst, orwhatever role you're trying to apply
for financial analysts, marketinganalysts, business intelligence engineer.
(15:12):
You need to have thatsomewhere on your resume.
And if you don't, you're notlikely to get called back.
So I'd wanna make sure that myresume said data analyst like
three or four different times.
Now, on a similar note, if thejob description is asking for sql,
I'll wanna make sure that I haveSQL on my resume multiple times.
So once again, I wanna putit in my skill section.
Maybe I put it in my statement,my objective at the top, uh, maybe
(15:33):
I tried to put it in my bulletpoints in my experience section.
Maybe I have a projectsection now on my resume.
I'd want to put it there.
You want to add as manykeywords as you can.
If you don't have the word Excel, theword sql, the word Tableau, power, bi,
python, whatever, whatever terms you'retrying to go for, if those aren't on your
resume, you're not gonna get interviews.
So I wanna make sure that Iput SQL, Tableau in Excel, and
(15:56):
in many places I possibly can.
On my resume along witha data analyst tile.
Next, I'd wanna do thesame thing with LinkedIn.
I wanna make sure that allof my experience section
on LinkedIn is filled out.
I wanna make sure it has bullet points.
I wanna make sure I have areally good about section.
I have a really good headline, aclear profile picture, a good cover
photo on LinkedIn, and make sure everysingle part of my LinkedIn profile.
(16:17):
Has information.
Why?
Because once again, 97% of recruiters,these are the people who hire
you, are on LinkedIn every day.
And if they're on LinkedIn everyday, I think I should probably
be on LinkedIn every day as well.
I can't tell you how many times peoplego through my program and they do
our LinkedIn section, they updatetheir LinkedIn, and all of a sudden
they have people reaching out tothem, recruiters, Hey, would you be
interested to interview for this role?
Would you be interested tointerview for that role?
(16:38):
And all it does is takesome LinkedIn optimization.
Once again, you want to keywordstuff on your LinkedIn in as
many places as you possibly can.
Add skills, add whatever's in the jobdescription, put that on your LinkedIn.
The other thing to kind of consideron your resume in LinkedIn, and
this is a little controversial,so uh, if you don't like it, I'm
sorry, but this honestly helps you.
Can you change any ofyour previous titles?
(16:58):
Can you go through your titles and canyou make them sound more data analyst?
Can you add the word analyst anywhere?
Can you add the word data anywhere?
The more that you have data andanalyst on your resume in your
title section of your experience?
The better.
So maybe you are a program specialist.
Can we substitute the wordanalyst for specialist?
Would that be the end of the world?
The term analyst is pretty broad,so I feel like it's safe to do.
(17:21):
And honestly like most titlesare all over the place.
Like a title at one companydoes not mean the same as what
it would be at another company.
They're all made up.
There's no such thing as likereal titles, to be honest.
So I think if you can do this.
You should, and I honestly,I would elect to do that.
So chemical lab technician, maybeI'd be chemical lab analyst.
That feels like a little bit ofa stretch, but here's the key.
If it feels like a stretch, justremember you're just tricking the a TS.
(17:45):
You could explain it to a human.
Oh, that was actually more oflike, uh, lab like technician role.
But I did do a little bit ofExcel analysis on that job.
Humans can understand nuanced computers,ATSs cannot, so I'd probably update
my LinkedIn and resume those ways.
Step six is I would needto start applying for jobs.
Um, obviously this might be reallyobvious, but I'm not going to land
(18:06):
a job if I don't apply for jobs.
And the same is true for you.
So if you're applying to only a few jobsand you're not getting any bites and
you're like, why can't I land a job?
The answer is apply for more jobs.
Now, I hate saying that because I'malso not a fan of just the spray and
pray method where you're literally,you know, bombing your resume out
to hundreds of thousands of people.
Like I don't think thatis a good method either.
(18:26):
I think that there is kind of amiddle ground where you're applying,
probably unfortunately, in today'seconomy for hundreds of roles.
But you're doing so in a targeted mannerwith human-centric motion in mind.
And what I mean by that is 67% of jobscome from being recruited or referred.
So that's why I really wantedto update my LinkedIn earlier.
Right.
So I can get recruited, but let'stalk about referrals, referrals.
(18:47):
Are amazing.
This is when someone at a company willrefer you to a role at that company
and hiring managers and recruiterslove that because if your friend's at
a company and they're doing good work,they probably like your friend and they
would probably be glad to hire morepeople like your friend, and hopefully
you're just as good as your friend.
So.
Networking is really key here.
You need, you need, youneed to be networking.
(19:09):
If you're not networking, your jobhunt will take, I'm not even being
dramatic here, 10 times longer.
Networking is literally the keyto landing a data job quickly.
Now, how do you do that?
We talked about updatingour LinkedIn profile.
That's a great start.
I would also tell you to startdocumenting your journey on
LinkedIn via posts and comments.
Um, that's what we teach our students.
I know that's scary for a lot of you.
But I've literally seen it work wondersfor so many students who had zero
(19:31):
job experience and they were ableto land a data job because of that.
If that sounds scary, no worries.
You can go to your neighbor, youcan go to your cousin, you can go to
your mom's friend's aunt and just belike, Hey, what do you do for work?
Pull out your phone.
Go through every contact in your phone.
Write down what every singleperson does for work and
where they work, and then ask.
Would they ever hire a data analyst?
Do they, do they have data analystsworking at their company now?
(19:52):
If so, send them a message.
Start with the people who inyour network already are in the
data world or in the tech world.
They can be really good resourcesfor you and if they're actually your
friends, if they're actually yourfamily, they're willing to help you.
They will be willing to help you.
You just need to ask the right way.
So a really easy way to not be intrusive,it's just to be like, Hey, I know that
you're, you know, a program manager.
At IBM, do you enjoy it?
(20:12):
Just start the conversation that way.
Oh, like, yeah, it's great.
Yeah, it's awesome.
You can be like, yeah, cool.
I'm like looking to become a data analyst.
Do you know any data analyst at IBM?
Oh yeah, I know this guy.
That's very cool.
I can introduce you if you'd like.
Oh yeah, that'd be great.
See, I didn't even ask, I didn'teven ask for anything right in that
scenario, but I got what I wanted.
So if you're not networking,it's gonna be hard.
You need to be applying for jobs.
Also I recommend varyingwhere you apply for jobs.
(20:35):
LinkedIn, great place to apply forjobs, maybe check your local listings.
Those will don't get as manyapplicants and could be really,
really easy to land interviews.
Also, try other job platforms.
I'm not gonna list themall, but I'm biased.
You can try find a data job.com.
This is my free data job board whereI post a lot of different data jobs.
I also have another one that is premium.
It is paid.
It's called premium data jobs.com.
(20:56):
Those ones.
Always have a recruiter or hiring managerthat you could reach out to today.
So that's why it's a little bit special.
That's why it's paid.
Check out both those, but just makesure you're going to different job
boards and trying different applicationmethods because it is a little bit of
a luck, a little bit of a numbers game.
Now, if I've done steps one throughsix, I'm probably ready for steps
seven, which is start landingand preparing for interviews and.
(21:17):
Interviews are how you seal the deal.
That's how you actuallyget job offers, right?
But you shouldn't be stressed.
I shouldn't be stressed about interviewsuntil I start landing them because there's
two different separate skills here.
The skills and the process of landinginterviews, and then the process of
passing interviews, and those aretwo different things, and you should
prepare for them and work on them atdifferent times and in different ways.
(21:39):
So I would not be stressed about aninterview until I've landed an interview.
Once I landed an interview, I will cram.
Uh, and there's lots of different thingsyou have to think about in an interview,
but basically most data interviewshave two main parts, the behavioral
part and then the technical part.
The behavioral part.
They're gonna be asking questionsthat usually start with, tell me about
a time, tell me about a time you.
(21:59):
Had to be a leader.
You had an issue with a coworker, andthese questions are basically like, let's
look in their behavior in the past topredict what they might do in the future.
It's like, once again, the recruiter andhiring manager here are trying to figure
out how risky you are and hopefullynot how risky you are once you've.
You've shown that, hey,I'm a normal human being.
I can work.
They might ask more technicalquestions, and a lot of the times
(22:20):
this will be maybe Excel specificquestions or SQL specific questions.
It kind of just depends onthe role and the company.
There's so many platforms youcan try to prepare for these,
these technical interviews.
Just to list a few analyst builders,strato, scratch, uh, data lemur.
There's like so many different dataanalyst prep, interview prep courses
and classes and online things that Idon't wanna talk about it right now
(22:41):
and you shouldn't worry about it.
I'm not worrying about it until Iland interviews, but once you do.
Those are right there for you to practice.
So that's how I would hopefully landmy first data job if I was starting
from absolute scratch this year.
And if you joined this method,we call it the SPN method.
And what it means is it is notjust learning skills, that's
the s part of the SPN method.
If you're just learning skills.
(23:02):
You're not gonna land interviews, you'renot gonna land jobs 'cause you're missing
out on the other two thirds of theequation for landing your first data job.
The P in the N, the P standsfor projects in a portfolio.
So that's what we talked about earlier.
You need to have projects,you need to have that proof
and have it in a portfolio.
And the last part is the N, whichis the networking, which is if, like
I said, if you're not networking,you're not gonna land a job.
So if you like this roadmap andyou actually wanna follow it,
(23:23):
please watch this video over andover again until you can finally
figure out exactly what I said.
If you'd like a hand by hand guide.
Walking you through all thesteps, literally giving you
step-by-step instructions on thisis how you network, this is what
your LinkedIn should look like.
Here's a bunch ofprojects that you can do.
Here's a template for theresume and for the portfolio.
Then consider joining thedata analytics accelerator.
This is my all-inclusive dataanalytics bootcamp, where I'll
(23:46):
take you from zero to data analyst.
Literally, this has worked for somany different people in my program
from so many different backgrounds.
We've helped teachers, truck drivers, Uberdrivers, warehouse workers, accountants,
therapists, music therapists, likewhatever your current role is, we can
probably help you transition into a dataanalyst if you wanna check that out.
I have a link in theshow notes down below.
It's called the DataAnalytics Accelerator.
I'll be your coach and my team willhelp you land that First Data job.
(24:08):
We're super excited to help you.
Here's exactly how I would become a data analyst if I
had to start all over again in 2026.
Now I'm low key, pretty lazy,and I'm also very impatient.
So I'd want to choose the fastestroadmap with the least amount of work
required to actually land a data job.
That roadmap is called the SPN method,but it still has a lot of work.
Step one, I'd wanna figure outexactly what skills are required
(00:23):
because there's literally.
Thousands of different datatools and skills that you
could possibly be learning.
And if you're gonna master themall, it's gonna take you so long.
It's gonna take you decades beforeyou even feel close to ready.
Once again, remember, I'm verylazy and I'm very impatient.
So I want to learn the bare minimum ofskills required to land my first data job.
So which skills and whattools would I focus on?
(00:45):
Ideally, I choose the skills thathave the biggest bang for your
buck, the lowest hanging fruit.
So basically what that means are theones that are used the most in industry.
But also the ones that are the easiestto learn, so I can learn them quickly.
That way I could have employable in-demandskills really, really, really fast.
Uh, so what are those skills?
You're probably wondering, well, youcan do the research for yourself by
going through like hundreds, thousandsof different job descriptions and
(01:07):
keeping tallies and track of what datatools are mentioned the most often.
But obviously that'sgonna be a lot of work.
The good news is I already did all thatresearch and work for you, so here you go.
The most in demand tools that arealso pretty easy to learn are Excel.
Tableau sql.
Literally, that's it in that order.
These are the top three data skillsthat you should be learning when you're
(01:29):
just starting out in data analytics.
And if you need any help rememberingthat I came up with something called
a pneumonic, I think is what it'scalled to make it kind of easy.
It's every turtle swims.
E for Excel, T fourTableau and S four sql.
And that's where I'd personally start.
If I had to start all over, I wouldn'treally study anything else until
after landing that first data job.
Now I can hear everyonein the comments already.
(01:50):
Well, what about Pythonand what about Power bi?
And here's the truth, I love Python.
It's literally my favorite data tool.
But honestly, there is a littlebit of a steep learning curve,
and it's only required in like.
13% of data analyst jobs.
It just takes so freaking long to learn.
And remember, I'm not trying to bein this job hunting mode forever.
I'm trying to land a data job quickly.
So learning Python, it's gonnatake a freaking long time.
(02:13):
And to me, it's just not worth thetime investment at the beginning
because it's not the most in demandskill and it's not the easiest.
So it makes sense for me toleave it till later, and at that
point I can probably learn it.
On the job, so I'm gonna be gettingpaid to learn and I'm all about
that, so sign me up for that.
In fact, I did a video in thepast about how to get paid to
learn stuff in data analytics.
You can check that out right there.
(02:34):
Step two, I'd wanna make sure I understandall the different data jobs available.
Obviously there's data analyst andthat is a great place to start.
In fact, I think it's the bestplace to start, but there's actually
so many more jobs than just.
That they all have slightly differentnames and slightly different
responsibilities, but a lot of the timesthey're doing pretty similar stuff to
what you'd be doing as a data analyst.
(02:54):
So the first two I wanna talk aboutare data scientists and data engineer.
If you're just getting started,I would not try to get those jobs
because it is hard to land those roles.
It requires a lot of programming knowledgeand math knowledge land, those roles.
And I just think they'rereally hard to land.
So instead, I'd focus on things likedata analyst, financial analyst,
healthcare analyst, marketing analyst.
Almost anything that has the word analystin it, or that might have the word
(03:16):
data in it, I would at least consider.
Now, there's so many differentjobs here and I can't possibly
tell you every single one, butlet's just start with the big one.
So financial analyst and businessanalysts are two of the most
common analyst roles I've beenseeing on job boards quite a bit.
In fact, I run my own data job board.
We'll talk about it here in asecond, but on that job board.
Financial analyst and businessanalyst roles are pretty much more
(03:37):
common than data analyst roles.
The financial analyst roles you'regoing to be dealing with, like p and
ls, a little bit more profit and lossstatements, uh, a little bit like more
kind of data plus accounting, e uh, alittle bit about forecasting and just
like how much cash you have on hand.
A business analyst role, that's likehalf business, half data analyst
kind of meet in the middle, sotheir jobs can be quite varied,
(03:58):
um, in what they're actually doing.
But a lot of the times they're just like.
Approaching business problemswith like Excel or with Tableau or
with SQL or something like that.
The next most common one is healthcareanalyst, and it is kind of self-evident,
but basically you're doing dataanalytics with healthcare data.
A lot of the times you'd think that thisis like looking at medical charts and.
Different medicines andprocedures and stuff like that.
(04:20):
But honestly, unfortunately, a lot ofthe healthcare analyst roles are more
about the operations of healthcare,like appointments and billing, uh,
and scheduling and stuff like that.
There's a huge demand for healthcareanalyst roles, and I don't see that
demand going away anytime soon.
So this is a great role, especiallyif you have healthcare experience
in the past, if you've workedmaybe as a nurse or some sort of.
(04:41):
Medical tech, this couldbe a great fit for you.
Marketing analyst, once again,very self-evident in the name,
but basically you're doing dataanalytics on marketing data.
If you've ever worked as amarketer, if you know anything
about ads, if you know anythingabout social media or like website
analytics, this is a great place to.
For you to start now.
There's so many more jobs I can't eventalk about right now in this video.
So here's a big list on the screenright here, and if you're listening
(05:03):
to the audio version, I'll have alink in the show notes down below.
But there's so manydifferent data jobs you guys.
So pause this video, take a screenshot ofthis, and start looking for these jobs.
The reason you wanna start looking forthese roles instead of data analyst roles
is one less people know about these roles,so they're going to have less applicants.
And two, a lot of the time.
Your domain experience is going tobe very valuable for these roles.
(05:24):
So for example, if you've beenan accountant before, a financial
analyst role is a really goodfit for you because you already
have that accounting experience.
So when you go to apply to financialanalyst jobs, they can look at
your resume and be like, oh, thisperson's already been an accountant.
They're gonna understand thisdata set better than most.
And that's something thatI'd have to take in as well.
So in my previous life, I was a chemicallab technician, so I'd be probably
looking for data jobs that maybe haveto do with laboratory data or companies
(05:48):
that deal with some sort of chemicals.
Now there's also a bunch of like thesein-between jobs that are like half
data jobs, half domain jobs, um, andthey're a little bit more entry level.
They require less skills.
Maybe they only requireExcel, for example.
You've probably never heard ofthese jobs and that's totally okay.
I made a whole separate video,so you can watch that on YouTube
right here, or we'll have a link toit and the show notes down below.
(06:09):
And that will basically explain theseroles that are a little bit more entry
level than even a data analyst role.
They don't pay as well as dataanalyst role, but you could probably
land them today if you know Excel.
So once again, check that out.
And honestly, if I had to start allover again, I might go for one of
these roles first because when Iwas a chemical lab technician, I was
making like $15 an hour, and theseroles are like closer to $25 an hour.
(06:29):
So I might wanna start with one of theseroles, get the word data on my resume,
and then start applying for data analystjobs after I get data on my resume.
Step three is I need to figureout a way to convince a hiring
manager to actually hire me.
Why would anyone wanna hire me?
I'm a chemical lab technician.
I've never been a data analyst.
I don't have very many data skills,like why on earth would someone hire me?
(06:50):
Um, and you've maybe felt this way before.
I call it the circle of doom.
It's basically like I can'tget data experience because I
can't get a data job because.
I can't get data experience.
And so this never ending cycle of doomwhere it's like, how the heck am I ever
supposed to get a job when I don't haveexperience, but I can't get experience?
'cause no one's gonna gimme a job.
And honestly, it's the absolute worst.
If you're in the circle ofdoom right now, let me know in
(07:12):
the comments and I'm so sorry.
That is not a fun place to be.
But here's the truth, is you couldactually create your own experience
and you do that by building projects.
Now a project is basically.
A real world life exampleof you analyzing data.
It's almost like you have some sort ofproof that like, hey, not only does my
resume say that I can do Excel, that Ican analyze data in sql, that I can make
(07:34):
a Tableau dashboard, but here's sometangible proof via project that I can.
And it's one thing to know the skills.
It's another thing to showthat you know the skills.
And those are different things.
So think about it, if I'mlike interviewing with a
hiring manager and I'm.
Tell the hiring manager, Hey, yeah,I know sql, I've been learning sql.
They're gonna be like, well,can you prove it to me?
Right?
And if I can have a project wherelike, I'm like, yes, I can look it.
(07:57):
Here's some healthcaredata that I analyzed.
You know, here's some financialtransactions that I analyzed.
Here's some manufacturing sensor datathat I actually analyzed, and I created
this dashboard for you in Tableau.
See how powerful that is.
All of a sudden, the hiring manager islike on the defense at the beginning,
like, I don't know if this personactually can do what we need them to do.
Two, oh my gosh, this person alreadyhas done what I need them to do.
(08:18):
Here's the evidence.
I like this person.
I mean, it's hard to do, but putyourself in the hiring manager's shoes.
Let's say that you were a hiring manager.
For like the next Fast and theFurious movie that's coming out and
you need to hire a stunt double.
Let's say you get two applicants.
Applicant, a, you know, on their resumeit says that they can jump over a car.
Great.
Uh, applicant B'S resume alsosays they can jump over a car.
Fantastic, but they also send avideo of them jumping over a car.
(08:42):
Who are you more likely to hire?
Uh, option A or option.
It's option B, right?
Why?
Think about it for a second, becausethey gave evidence that they can
do what the job description says.
They took the risk out of itbecause now that I'm on the other
side of, I hire people, right?
I'm a hiring manager now and Ihired some wrong people this year
and it has bit me in the butt.
It has cost me honestlythousands of dollars, uh,
(09:04):
because I didn't hire correctly.
And so when you are, you know, tryingto convince a hiring manager that
you are the right person, if youcan lower that risk with projects.
All of a sudden you'rebreaking the circle of doom.
You have experience and you'reletting the hiring manager know in a
undeniable way, Hey, I've got this.
Don't worry about me.
So I would need tostart building projects.
And if I didn't know where to go or howto start building projects, you always
(09:27):
gotta start with a dataset and yougotta find a dataset somewhere online.
So one of the best places youcan find data sets, well, there's
a bunch of different options.
I actually did a whole nothervideo about it right here, you
can find in the show notes.
Um, but the short answer is Kaggle.
Kaggle is a great place tofind, uh, a data set like.
90% of the time, and usuallythat's like good enough.
So that's where I'd start.
And then in terms of like whatto do in the project, first
(09:49):
pick, should you do it in Excel?
Should you do it in sql?
Should you do it in Tableau?
Uh, just pick whatever one you're maybethe best at, and then start to answer some
business questions about the data set.
Think about how many, what'sthe max, what's the average?
What's the relationshipbetween these two columns?
What happens over time?
Those are some of the questions that youcan ask at the beginning, and you can just
answer maybe two or three or four of 'em,and all of a sudden you have a project.
(10:11):
You have evidence, all of asudden you have experience.
And I would be qualified, or atleast I would be able to talk to a
hiring manager with like some sortof defense like, no, I am good.
You should hire me.
So I need to build projects.
Step four, I would need to createa home for these projects, right?
Because if you do these projects.
But they're not tangible, then.
They're not tangible.
And how are you gonna convince the hiringmanager that you're the person, right?
(10:34):
So if your project is just in yourhead, it doesn't really count.
If it's just on your desktop,it doesn't really count.
That doesn't do you any good.
You need this to be public.
You need this to be easily shareable.
You need this to look good and lookpretty and make yourself look good, right?
This is really key to have a portfolio.
So a portfolio is basically a home.
For your projects, and you'll want to havemaybe one to, I don't know, 10 different
(10:56):
projects that that's a big order.
It depends on the, thequality of your projects.
One really, really, really goodproject could be better than
like seven mediocre projects.
It really just depends.
So where should you build your portfolio?
There's a couple different options.
And I teach all these different optionsinside of my program, the data Analytics
accelerator, and I actually give themtemplates to just do this really easily.
(11:16):
Probably the most common placeto have a portfolio is GitHub.
Uh, but I don't like GitHub asa portfolio for data analysts.
Um, I can hear you guys in the comments.
Oh, GitHub's awesome for data scientistsand data engineers and programmers.
Yeah, I get it.
Okay.
But a lot of you guys at the beginning.
You're not gonna be writing code.
GitHub is literally meant for code.
Now you can kind of reverse engineer,hack it and make it for anything, and
(11:38):
it, it could work as a good portfolio,but it's really hard to navigate and it's
really hard to look good inside of GitHub.
Just trust me on this and try oneof these other things instead.
I really like to use LinkedIn.
LinkedIn.
That's a great place whererecruiters are right?
Like it's like 97% of recruiters areactively using LinkedIn every single day.
So why not be where they are?
Right?
Because those are the peoplethat can change your life.
(11:58):
Those are the people thatcan all of a sudden reach out
to you and offer you a job.
So I like using LinkedIn.
There's a featured section on there.
There's a project section on there.
We like to use LinkedIn articlestoo, to make these projects go.
And that's what I suggest.
That's one of the things Iteach inside of my bootcamp.
The next thing I also do insidethe bootcamp is card dot, uh, co.
I think.
I'll, I'll put a link, uh, right hereand in the show notes down below.
(12:20):
But basically it's just a websitebuilder, a simple website builder.
Um, I think it costs like nine to$20 a year and it's so worth it.
You guys, your portfolio looks, looks sogood and you can build it pretty quickly.
So, uh, our students inside ofour bootcamp actually just get.
This template from us right here, thatthey can literally just fill in the blanks
with their information so it doesn'ttake them like the, I don't know, couple
hours that it might take you to set up.
(12:41):
But, uh, I really like card.
I really like LinkedIn.
You could do it on Medium, you coulddo it on any sort of Squarespace
or Wix or other website builder.
Also, if you like GitHub, there isan alternative called GitHub pages.
GitHub realize, Hey, peopleare using this as a portfolio.
We're not really built to be a portfolio,so let's build a like separate product
that makes portfolios really well,and that's called GitHub pages.
(13:03):
And I really recommend that it'sjust a little bit of a steep
learning curve if you're not really.
Knowing about GitHub or you don'tknow about markdown, markdowns kind
of like a programming language.
It's kind of not, but uh, regardlessit's a little bit more technical, so
I'd wanna make sure I have a portfolio.
Ideally in LinkedIn or card step five,I'd need to make sure that my resume
(13:24):
and LinkedIn are working for me.
And these are really the only two toolsyou get when you're trying to land a
data job and you need to invest in them.
They need to be like little mini.
Employees running around working for you.
Okay.
And let me talk about what I mean by that.
Number one, when you're applying forjobs, your resume either is going
to pass what's called the a TS, theapplicant tracking system, or it's not
(13:45):
every time, it does not pass the a TS.
There's kind of two scenarios.
One, your resume couldn't reallybe read very well, and it's not.
A TS compliant, meaning there's someformatting issues on it, or two, you
didn't fit what the job descriptionor the a TS was looking for.
Number one, you wanna just makesure that you have a really
good a TS friendly resume.
We give our students all a bunch oftemplates that they can choose from, but
(14:07):
the key here is basically no pictures,one column, no tables, and make sure
it's like pretty simple, like don'ttry to do too much with your resume.
Next, these ATSs, they'rehonestly not very smart.
Even with ai, they're kind of dumb.
Basically what they're looking foris they're looking at your resume
and they're looking at the jobdescription, and they're trying to
figure out if you're a match or not.
Now, what would make you a match?
(14:28):
Think about it.
Whatever's on the job descriptionshould match your resume, and so if
you're applying for a data analyst role.
Well, I'm sorry.
You live in a world where they wantto hire someone with experience.
There is no non-zeroexperience jobs anymore.
The lucky thing is we talked aboutearlier how to create experience.
So if you're applying for dataanalyst jobs and you don't
have the term data analyst.
On your resume anywhere, you'reprobably not gonna pass the a s, so
(14:48):
you can kind of hack the system here.
You can put it next to yourname at the top of your resume.
You can put it in like your objectivestatement at the top and or you
can put it in your experiencesection and have a data analyst job.
That could be one that it's justyou making projects on your own.
You could hire yourself,start your own company.
All of a sudden you're doing data,freelance, data analytics, just you
need to have the word data analyst, orwhatever role you're trying to apply
for financial analysts, marketinganalysts, business intelligence engineer.
(15:12):
You need to have thatsomewhere on your resume.
And if you don't, you're notlikely to get called back.
So I'd wanna make sure that myresume said data analyst like
three or four different times.
Now, on a similar note, if thejob description is asking for sql,
I'll wanna make sure that I haveSQL on my resume multiple times.
So once again, I wanna putit in my skill section.
Maybe I put it in my statement,my objective at the top, uh, maybe
(15:33):
I tried to put it in my bulletpoints in my experience section.
Maybe I have a projectsection now on my resume.
I'd want to put it there.
You want to add as manykeywords as you can.
If you don't have the word Excel, theword sql, the word Tableau, power, bi,
python, whatever, whatever terms you'retrying to go for, if those aren't on your
resume, you're not gonna get interviews.
So I wanna make sure that Iput SQL, Tableau in Excel, and
(15:56):
in many places I possibly can.
On my resume along witha data analyst tile.
Next, I'd wanna do thesame thing with LinkedIn.
I wanna make sure that allof my experience section
on LinkedIn is filled out.
I wanna make sure it has bullet points.
I wanna make sure I have areally good about section.
I have a really good headline, aclear profile picture, a good cover
photo on LinkedIn, and make sure everysingle part of my LinkedIn profile.
(16:17):
Has information.
Why?
Because once again, 97% of recruiters,these are the people who hire
you, are on LinkedIn every day.
And if they're on LinkedIn everyday, I think I should probably
be on LinkedIn every day as well.
I can't tell you how many times peoplego through my program and they do
our LinkedIn section, they updatetheir LinkedIn, and all of a sudden
they have people reaching out tothem, recruiters, Hey, would you be
interested to interview for this role?
Would you be interested tointerview for that role?
(16:38):
And all it does is takesome LinkedIn optimization.
Once again, you want to keywordstuff on your LinkedIn in as
many places as you possibly can.
Add skills, add whatever's in the jobdescription, put that on your LinkedIn.
The other thing to kind of consideron your resume in LinkedIn, and
this is a little controversial,so uh, if you don't like it, I'm
sorry, but this honestly helps you.
Can you change any ofyour previous titles?
(16:58):
Can you go through your titles and canyou make them sound more data analyst?
Can you add the word analyst anywhere?
Can you add the word data anywhere?
The more that you have data andanalyst on your resume in your
title section of your experience?
The better.
So maybe you are a program specialist.
Can we substitute the wordanalyst for specialist?
Would that be the end of the world?
The term analyst is pretty broad,so I feel like it's safe to do.
(17:21):
And honestly like most titlesare all over the place.
Like a title at one companydoes not mean the same as what
it would be at another company.
They're all made up.
There's no such thing as likereal titles, to be honest.
So I think if you can do this.
You should, and I honestly,I would elect to do that.
So chemical lab technician, maybeI'd be chemical lab analyst.
That feels like a little bit ofa stretch, but here's the key.
If it feels like a stretch, justremember you're just tricking the a TS.
(17:45):
You could explain it to a human.
Oh, that was actually more oflike, uh, lab like technician role.
But I did do a little bit ofExcel analysis on that job.
Humans can understand nuanced computers,ATSs cannot, so I'd probably update
my LinkedIn and resume those ways.
Step six is I would needto start applying for jobs.
Um, obviously this might be reallyobvious, but I'm not going to land
(18:06):
a job if I don't apply for jobs.
And the same is true for you.
So if you're applying to only a few jobsand you're not getting any bites and
you're like, why can't I land a job?
The answer is apply for more jobs.
Now, I hate saying that because I'malso not a fan of just the spray and
pray method where you're literally,you know, bombing your resume out
to hundreds of thousands of people.
Like I don't think thatis a good method either.
(18:26):
I think that there is kind of amiddle ground where you're applying,
probably unfortunately, in today'seconomy for hundreds of roles.
But you're doing so in a targeted mannerwith human-centric motion in mind.
And what I mean by that is 67% of jobscome from being recruited or referred.
So that's why I really wantedto update my LinkedIn earlier.
Right.
So I can get recruited, but let'stalk about referrals, referrals.
(18:47):
Are amazing.
This is when someone at a company willrefer you to a role at that company
and hiring managers and recruiterslove that because if your friend's at
a company and they're doing good work,they probably like your friend and they
would probably be glad to hire morepeople like your friend, and hopefully
you're just as good as your friend.
So.
Networking is really key here.
You need, you need, youneed to be networking.
(19:09):
If you're not networking, your jobhunt will take, I'm not even being
dramatic here, 10 times longer.
Networking is literally the keyto landing a data job quickly.
Now, how do you do that?
We talked about updatingour LinkedIn profile.
That's a great start.
I would also tell you to startdocumenting your journey on
LinkedIn via posts and comments.
Um, that's what we teach our students.
I know that's scary for a lot of you.
But I've literally seen it work wondersfor so many students who had zero
(19:31):
job experience and they were ableto land a data job because of that.
If that sounds scary, no worries.
You can go to your neighbor, youcan go to your cousin, you can go to
your mom's friend's aunt and just belike, Hey, what do you do for work?
Pull out your phone.
Go through every contact in your phone.
Write down what every singleperson does for work and
where they work, and then ask.
Would they ever hire a data analyst?
Do they, do they have data analystsworking at their company now?
(19:52):
If so, send them a message.
Start with the people who inyour network already are in the
data world or in the tech world.
They can be really good resourcesfor you and if they're actually your
friends, if they're actually yourfamily, they're willing to help you.
They will be willing to help you.
You just need to ask the right way.
So a really easy way to not be intrusive,it's just to be like, Hey, I know that
you're, you know, a program manager.
At IBM, do you enjoy it?
(20:12):
Just start the conversation that way.
Oh, like, yeah, it's great.
Yeah, it's awesome.
You can be like, yeah, cool.
I'm like looking to become a data analyst.
Do you know any data analyst at IBM?
Oh yeah, I know this guy.
That's very cool.
I can introduce you if you'd like.
Oh yeah, that'd be great.
See, I didn't even ask, I didn'teven ask for anything right in that
scenario, but I got what I wanted.
So if you're not networking,it's gonna be hard.
You need to be applying for jobs.
Also I recommend varyingwhere you apply for jobs.
(20:35):
LinkedIn, great place to apply forjobs, maybe check your local listings.
Those will don't get as manyapplicants and could be really,
really easy to land interviews.
Also, try other job platforms.
I'm not gonna list themall, but I'm biased.
You can try find a data job.com.
This is my free data job board whereI post a lot of different data jobs.
I also have another one that is premium.
It is paid.
It's called premium data jobs.com.
(20:56):
Those ones.
Always have a recruiter or hiring managerthat you could reach out to today.
So that's why it's a little bit special.
That's why it's paid.
Check out both those, but just makesure you're going to different job
boards and trying different applicationmethods because it is a little bit of
a luck, a little bit of a numbers game.
Now, if I've done steps one throughsix, I'm probably ready for steps
seven, which is start landingand preparing for interviews and.
(21:17):
Interviews are how you seal the deal.
That's how you actuallyget job offers, right?
But you shouldn't be stressed.
I shouldn't be stressed about interviewsuntil I start landing them because there's
two different separate skills here.
The skills and the process of landinginterviews, and then the process of
passing interviews, and those aretwo different things, and you should
prepare for them and work on them atdifferent times and in different ways.
(21:39):
So I would not be stressed about aninterview until I've landed an interview.
Once I landed an interview, I will cram.
Uh, and there's lots of different thingsyou have to think about in an interview,
but basically most data interviewshave two main parts, the behavioral
part and then the technical part.
The behavioral part.
They're gonna be asking questionsthat usually start with, tell me about
a time, tell me about a time you.
(21:59):
Had to be a leader.
You had an issue with a coworker, andthese questions are basically like, let's
look in their behavior in the past topredict what they might do in the future.
It's like, once again, the recruiter andhiring manager here are trying to figure
out how risky you are and hopefullynot how risky you are once you've.
You've shown that, hey,I'm a normal human being.
I can work.
They might ask more technicalquestions, and a lot of the times
(22:20):
this will be maybe Excel specificquestions or SQL specific questions.
It kind of just depends onthe role and the company.
There's so many platforms youcan try to prepare for these,
these technical interviews.
Just to list a few analyst builders,strato, scratch, uh, data lemur.
There's like so many different dataanalyst prep, interview prep courses
and classes and online things that Idon't wanna talk about it right now
(22:41):
and you shouldn't worry about it.
I'm not worrying about it until Iland interviews, but once you do.
Those are right there for you to practice.
So that's how I would hopefully landmy first data job if I was starting
from absolute scratch this year.
And if you joined this method,we call it the SPN method.
And what it means is it is notjust learning skills, that's
the s part of the SPN method.
If you're just learning skills.
(23:02):
You're not gonna land interviews, you'renot gonna land jobs 'cause you're missing
out on the other two thirds of theequation for landing your first data job.
The P in the N, the P standsfor projects in a portfolio.
So that's what we talked about earlier.
You need to have projects,you need to have that proof
and have it in a portfolio.
And the last part is the N, whichis the networking, which is if, like
I said, if you're not networking,you're not gonna land a job.
So if you like this roadmap andyou actually wanna follow it,
(23:23):
please watch this video over andover again until you can finally
figure out exactly what I said.
If you'd like a hand by hand guide.
Walking you through all thesteps, literally giving you
step-by-step instructions on thisis how you network, this is what
your LinkedIn should look like.
Here's a bunch ofprojects that you can do.
Here's a template for theresume and for the portfolio.
Then consider joining thedata analytics accelerator.
This is my all-inclusive dataanalytics bootcamp, where I'll
(23:46):
take you from zero to data analyst.
Literally, this has worked for somany different people in my program
from so many different backgrounds.
We've helped teachers, truck drivers, Uberdrivers, warehouse workers, accountants,
therapists, music therapists, likewhatever your current role is, we can
probably help you transition into a dataanalyst if you wanna check that out.
I have a link in theshow notes down below.
It's called the DataAnalytics Accelerator.
I'll be your coach and my team willhelp you land that First Data job.
(24:08):
We're super excited to help you.
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