July 23, 2025 • 25 mins
I’ve spent the last 10 years working as a data analyst, data scientist, and data engineer for some pretty cool companies like ExxonMobil, MIT, the Utah Jazz, and others. And the last 4, I’ve spent them teaching others how to land their first data job. My students now work at Apple, Amazon, Rivian, Tesla, and other cool companies.
Let me share the 13 things I wish I knew when I was getting started.
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⌚ TIMESTAMPS
00:00 Introduction
00:28 - 1. Your Skills Aren't Holding You Back
01:56 - 2. You Will Get Paid to Learn on the Job
03:25 - 3. You Don't Have to Know Everything
04:27 - 4. Who You Know Matters More Than What You Do
07:08 - 5. Your Domain Expertise Matters
09:20 - 6. Don't Take Job Rejections Personally
12:07 - 7. Data Job Titles Are Confusing
13:29 - 8. Data Tools Matter Less Than You Think
14:38 - 9. The Bookends of Analysis Are Most Important
16:14 - 10. How You Present Your Digital Self Is Important
17:42 - 11. All Industries Experience Cycles
20:11 - 12. Mentorship is the Shortcut to Results
22:11 - 13. You'll Never Stop Learning
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
I've spent the last 10 years workingas a data analyst, data scientist,
(00:03):
and data engineer for some prettycool companies like ExxonMobil,
MIT, the Utah Jazz, and others.
In the last four years, I'vededicated my time to teaching others,
learning how to land their firstdata job, and now my students work
at Apple, Amazon, Rivian, Tesla, andsome other pretty cool companies.
Let me share 13 things I wish Iknew when I was getting started.
(00:28):
Number one, your skills aren'tholding you back properly when it
comes to landing your first day job.
It's a very frustrating process,especially in today's market.
In today's economy.
There's lots of rejection, there's lotsof frustration, there's a lot to learn.
But the majority of your time, employersdon't even know how skilled you are.
(00:49):
Like if they're hiring for a data analystposition that requires sql, and you
think SQL is what's holding you back.
The odds are SQL's not holding youback because how does the employer
know how good you are at sql?
They really don't.
Unless you've taken some sortof a technical interview.
If you're getting rejected and youthink it's your skills, it's actually
probably something like your resumeor your LinkedIn or your experience
(01:12):
that you're portraying on either ofthose, and you'll want to try to make
it look like you know more than youdo probably, if I'm being honest.
That is what's probably holding you back.
Unless you're failing technicalinterviews or you're doing technical
interviews and you're not gettinghired, your skills aren't going to
get you more technical interviews.
The better you are at SQL does notequal how many SQL interviews you have.
(01:36):
It's the perception, so you need tomake sure that you have a good LinkedIn
in a good resume highlighting sql.
But to be honest, like if someone's reallyskilled at SQL and has a bad resume and
someone's okay at SQL but has a goodresume, this person's going to get.
More interviews a lot of the time.
I know it's unfair, but that'sjust how it's number two.
(01:57):
You will get paid to learn on the job.
I promise that it'llhappen in your career.
It's happened many times in my career.
I've learned power, bi,Tableau, sql, Python, Excel.
I pretty much learnedeverything on the job.
Now, that does mean you need tohave a base, like you need to know
something that will get you hired.
Like you can't know nothing, but theodds are you're going to be learning
(02:19):
on the job quite a bit because one.
It's really hard toknow everything in data.
Like there's so many different things.
Two, it's always expanding.
So even if you did know everything today,you will not know everything a year from
now, especially with how ai, uh, and justrapid technology change and data is going.
Um, and number three, a lot ofthe times there's like more niche
softwares that you'll use that likeyou probably haven't even heard of.
(02:42):
So for example, when I was atExxonMobil, we used a tool to do
data analytics, you could say.
Um, and it was calledpims, and I'm sure like.
No one watching this hasever heard of pims, PIMS.
Uh, if you have heard of PIMS foroil, crude basket selection and
optimization, let me know in thecomments, but my guess is 99.9%
(03:03):
of you guys have never heard of it,and it's something I used every day.
And there's the equivalent of PIMS forall different industries and all sorts
of different niches inside of industries.
There's so many tools out there thatyou've never even heard of that like you
wouldn't even bother learning, but youwill be using those on the job, maybe as
like your primary data analytics tool.
So eventually you are going to getpaid to learn tools that you don't
(03:25):
know, which brings me number three.
You don't have to know everything.
You don't have to know everythingto land your first aid job.
I definitely don't know everything now.
I'm on, like whatever, my 10th datajob or whatever, however you wanna
count all my different experiences.
Like I definitely don't know everything.
Um, I even taught a data engineeringcourse at MIT and I am not
that great of a data engineer.
(03:46):
I did not know that much about dataengineering when I took that role.
Uh, and the truth is like, it'sokay to not know everything.
You won't know everything.
And you don't have to know everything.
Now you do.
You have to know something.
Yeah.
You have to know something.
But the idea that you have to like knowevery single thing before you can even
start applying is just holding you back.
So the quicker you realize, Hey,I don't know everything, and
(04:09):
that's okay, I'll figure it out.
What I do need to know, uh, thesooner you'll be better off.
Because that is like the biggestmindset change that will allow you
to apply for more jobs, more stretchjobs, things that feel like you're
not going to land, but you might land.
You never know.
Just know.
You don't have to know it all.
That's it.
Four, and this one kind ofsucks, but who, you know, matters
(04:30):
way more than what you do.
Uh, it's not, it's not what youknow, it's who you know, right?
Like that old adage.
Uh, that is so true.
Um, I think most of the successI've had in my career has
really been to who I know.
Um, now I didn't know allthose people to start.
I made a lot of those connections,uh, from the ground up.
And if you don't know anyone, you can makethose connections from the ground up too.
(04:54):
But like.
We just live in a society anda world where opportunity is
given to people and people.
It's not necessarily merit based.
It's more risk free based.
And let me explain because it's likewhen you're trying to fill a position
or when you're looking for a leaderin a project or you're looking to
promote someone, a lot of the timesit's like, Hey, well who do we know?
(05:15):
Right?
Uh, how talented you are or what you'vedone is only as valuable as the people
in power who know about those things.
I, I work in a shed in my backyard,and let's say like I cured cancer
back here, like I solved thebiggest mystery in the world.
If no one knows about it, itdoesn't really make a difference.
Now that is such an accomplishment thatif I told someone, it would probably
(05:38):
go through the grapevine and then I getinterviewed by the local news and I get
interviewed by the national news, and thenwho knows, maybe I'm winning like a Nobel
Prize and everyone knows my name and I'mthe most famous person on planet Earth.
That's definitely possible.
Like your accomplishments can be so good.
That it makes you known to the wholeworld, but for the majority of us,
that's probably not gonna be the case.
And so it's important that the good workwe do, do gets recognized by people.
(06:04):
And you have to know people inorder to get recognized by people.
So spend time at work, getting toknow your coworkers, getting to know
your boss, getting know, getting toknow your boss's boss, getting to know
your, like boss's, like equivalent ona different organization or a different
division or something like that.
Like who you know, reallymatters in your career and will
make a really big difference.
(06:24):
And if you're not quite in a careerthat you wanna be in right now,
that can be true for networkingbefore you get into a career.
So for example, all of myaccelerator students, you know,
they're new to data analytics.
They're transferring from being ateacher or being a delivery driver.
Uh, or I don't know, beinga scientist or something.
(06:45):
Getting to know me is valuable,to be honest, because I
have a lot of connections.
I have like 150.
A thousand connections orfollowers on LinkedIn, right?
I have this YouTubechannel, I have my podcast.
I know people who are hiring.
I can, you know, talk aboutpeople in my newsletter.
I can talk about people, my studentson LinkedIn, so on and so forth.
So, oftentimes it is importantwho you know, and you can start
(07:06):
from scratch, I promise you.
Number five, your domain expertisematters more than you think,
especially in the future with, uh, ai.
Um, doing data analyticsis really important.
We never just do data analyticsfor data analytics sake.
It's not for funsies thatwe're analyzing data.
It's always to make an organizationdecision to make a business decision, to
(07:28):
save money, to save time, to save lives.
We're doing data analytics for the theends, not for just doing it, right.
There's not like just arollout there in the world.
That's just like doing dataanalytics on data analytics.
That's very meta, right?
All the data analytics jobs areon healthcare data, on financial
data, on manufacturing data.
(07:51):
And so whatever you've donein the past is really valuable
because you understand the domain.
You have what's called domain expertise,and if you just brought in like a
random data analyst, they would notunderstand your domain as well as you do.
When I worked at ExxonMobil, I havea chemical engineering background,
so I studied chemistry, I studiedengineering, I studied manufacturing.
(08:11):
So every once in a while we'd have these.
Company-wide analytics on competitionsand anyone could, could enter and
you, they give you a data set andthey'd say, analyze this data set.
And at the time I was pretty, pretty newto the data world and wasn't necessarily
the best data scientists or data analysts.
I was competing against peoplewho had PhDs in data science,
(08:32):
who had PhDs in computer science.
We had PhDs in mathematics and I was ableto outperform them in these competitions
a lot of the time, not 'cause I wassmarter than them, or I could make better
models or I could code better than them.
Because I could relate what littleof data analytics I knew to the
business problem, to the actualdomain better than they could.
(08:52):
I understood the rulesof like the business.
I understood like the rules ofscience, of, of manufacturing, of
engineering, and that really helpedme craft better analysis and craft
better explanations of my analysis.
If you have a background that's notdata analytics, that's not statistics,
that's actually a good thing.
Like your domain can really matter.
(09:14):
Now you can transfer domains.
That's a thing.
People do it all the time.
But I just wanna tell you, your domain isvaluable and you shouldn't give up on it.
Number six, don't take job rejections.
So personally, no one likesgetting rejected, right?
It's never fun whether it'slike getting rejected on.
A date that you ask or like you ask a girlfor her number, uh, or you apply for a
(09:34):
job and they reject you, but don't takeit personally, especially job rejections
in today's economy, because like there'shundreds of applicants for every job.
So like every job you applyfor, let's just assume there's
like 200, 300 applicants.
That means like if we say 300 applicants,299 people are gonna get rejections.
So it's gonna happen moreoften than you think.
(09:56):
A lot of the time nowadays with theA TS that stands to our applicant
tracking system, it's the suite of toolsthat recruiters and hiring managers
use to try to make it easier for themto decide who's the right candidate.
The A TS sucks, youguys, it's not very good.
It's like not a verygood piece of technology.
I'm looking forward to seeing over thenext five, 10 years how it becomes better.
(10:18):
But right now it kind of sucks anda lot of the times you're not even
getting your resume seen by human.
It's just a computer, a sillycomputer who's looking at your
resume and is like, eh, I don'tthink this resume is very good.
But it doesn't really knowwhat a good resume is.
We're in this world where we're gettingrejected all the time by computers
and it makes us feel bad, but likethe truth is that like these computers
(10:40):
aren't very smart, uh, and they're notmaking good decisions to be honest.
They're making decisions thatlimit that help hiring managers and
recruiters spend less time, but notnecessarily make the optimal decision.
And the truth is that like outof, um, I dunno, 300 candidates, I
interviewed a hiring manager one timewho I think had like 250 applicants
(11:02):
and she was like, yeah, I think50 of them would've been great.
So like literally I'll say 20% would'vebeen great candidates and if they would've
hired them, it would've worked out.
So like even, even a lot oftimes you're getting rejected.
You could have done the job and youcould have done really great at it.
It still sucks, I realize that.
But I think what sucks more is whenwe take the rejection so personally
(11:23):
that we kinda get depressed and westop applying for jobs and then we
never actually change our career.
I don't want that to happen to you.
So like, please stoptaking the rejections.
So personally and just realize it's justa silly computer making a silly decision.
That's why networking, what wetalked about earlier, who you
know is really important because.
If you know the right people, you canskip the whole a TS altogether and just
(11:45):
get an an interview and then show yourpersonality there and explain everything.
Humans can like understand the totalityof a candidate of a human candidate,
but computers, they just really look atresume and it's like they're only seeing,
I don't know, 10% of who you actuallyare and what you're actually capable of.
It's just silly.
(12:05):
You guys don't take it personally.
Number seven, data tiles.
They're super confusing.
The titles of different datajobs are all over the place.
Obviously.
There's like data scientists,data analysts, data engineer.
Those are like pretty cementedand pretty straightforward.
But I've seen data science analysts,I've seen data analytics scientists,
(12:25):
like I've seen so many different roles.
My job at ExxonMobil was for awhile was optimization engineer.
That doesn't sound data E at all,but I really just built models and.
Power BI dashboards the wholetime I was in that role.
So like you just can't judgea job off of the job title.
(12:46):
Sometimes job titles are are weird becausethe company just doesn't know better
and they're kind of just making it up.
Other times, like there'sjust no industry standards.
So it's just kind of all over the place.
But just know that like you need tobe looking at the like requirements
and making a judgment yourself onwhat type of job this actually is.
So.
(13:07):
Be looking for keywords like sql,Excel visualizations, mathematical
models, machine learning and stuffinside of the description, and not
just taking the title for what it is.
Like.
You need to be coming up with yourown titles for every job description
that you read because they aregoing to be quite different.
So they're really confusing.
Don't stress it.
(13:27):
Just know that that exists.
Number eight, data toolsmatter less than you think.
What I mean by that is I thinknow, um, I'm pretty decent at
a lot of different data tools.
I think my best data toolpersonally is Python.
I'm pretty good at Python.
Next might be R for me, andthen after that it might be
Power bi, Tableau, sql, Excel.
(13:50):
But there's other ones that I can do.
I can do matlab, I can do JMP.
I could do JavaScript if Ihad to, I could do D three.
I could do pencil andpaper, like I could analyze.
So I could use a, I could analyze datawith like all sorts of different tools.
And what I mean by this is like ifyou're given a business task, like,
okay, we need to, we wanna know how manyproducts we're gonna sell next December.
(14:14):
I think I could do that in Tableau,sql, Excel, Python, r Matlab, jump.
Like I, I could do itbasically in a data tool.
So I would focus maybe a little bit lesson the data tools and more about concepts.
If you get really good at one datatool, you could probably just use that
data tool to pretty much do everything.
So I don't think data, tools,learning them all especially is as
(14:36):
important as you think they are.
Number nine, the bookends ofanalysis are the most important.
And what I mean by bookends isthink of it, think of data analytics
as like a sandwich, bread, meat,bunch of other stuff, vegetables,
condiments, bread, right?
The breads, I think arethe most important part.
I think they're going to becomemore important with the explosion
(14:58):
of AI and data analysis.
What I mean by the bread is like talkingto stakeholders at the beginning and
talking to stakeholders at the end.
Because once again, we'renot doing data analysis for
funsies, for data analysis sake.
We're doing it to make impact and tochange lives, save money, save time.
If we don't do a good job at thebeginning of talking to stakeholders,
(15:20):
we're gonna do analysis in vain.
We're gonna probably do the wronganalysis for the wrong reasons
and it's not gonna be useful.
So the more we talk to stakeholders or,or let's say that it is even useful,
it might not be adopted very well,it might not be used like so many.
You hear so many people aboutbuilding dashboards that go
on to die, never be used.
And I think a lot of the times it's not.
(15:40):
It's because they didn't spendenough time upfront explaining to the
stakeholders, okay, what do you want?
Why do you want that?
Let me create the system or servicethat works best to solve your problem.
Then secondarily the ending whereyou've actually done the analysis, you
need to tie it back to the business,show them how to use it, make sure
that they can trust it, because ifyou don't do that, once again, you're
(16:00):
gonna be doing data analysis in vain.
All the work you've done is just gonnanot go anywhere, and that happens a lot.
Don't feel bad if it happens, butif it is happening, spending more
time on the front end or the backend is probably the solution.
Number 10, how you present yourdigital self matters more than
you present your physical self.
And what I mean by that is.
Your perception is really important.
(16:22):
How you're perceived is probably moreimportant than how you actually are.
And once again, I kindof hate saying this.
I'm not saying to cheat, I'm notsaying to lie, I'm not saying
looks matter, but they kind of do.
Um, in, before you get a job, likeon a resume in LinkedIn, as well as
when you're in a company, the workthat you do is probably less important
than how your work is perceived.
(16:45):
That sucks, but it's just the game you'regoing to have to play in your career.
And if you choose not to playthat game, I think you'll suffer.
So I think it's a game worth playing.
So that means like you need topresent yourself well on LinkedIn.
You need to present yourselfwell on your resume.
You need to make sure that your bosslikes you and your boss's cousin likes
you, and you need to make sure that likeyou're talking and you're, you're getting
(17:06):
seen because that's what's important.
If your work is important, but if itdoesn't get seen, it doesn't get used.
It's honestly not important.
And in today's economy, you have totake care of you and your family.
And if that means you need to beperceived as being a good actor, a
good professional, as a net positiveto your team and organization, then
(17:28):
you should spend the time and theresources necessarily to do that.
So that would be when you'redoing something, make it known,
tell people about it, share it.
Don't just do your work in silence.
If you do your work in silence, Ithink you and your family suffer.
Number 11, all industriesexperience cycles.
Uh, I think we're in a cycle right now.
I think we're in a veryrevolutionary cycle.
(17:50):
I think AI is really changing the game,but all industries go through disruption
and they go through peaks and valleys.
Let me kind of explain.
I worked at ExxonMobil during 2020.
Now, what happened in 2020?
Everyone.
Oh, COVID.
Good job.
Class COVID happened in 2020, right?
And what happens?
(18:11):
Especially the beginning part ofCOVID March, April, may, June.
We stopped going places.
We stopped going to work.
We stopped going to the movies,we stopped going to sports games.
We stopped traveling.
What do you need to travel?
Oh, gasoline, jet fuel.
Who makes gasoline and jet fuel?
ExxonMobil.
So it was a really bad time to work atExxonMobil because our, no one was buying
(18:32):
oil and no one was buying gasoline.
So those prices went down quite a bit.
If you remember, I lived inTexas when I worked for Exxon.
I think one time I got gas in Texasduring COVID for less than $2 a
gallon, which was like very, very low.
Now in Utah, I'm paying like 3, 3, 5, Ithink per gallon, so almost half, right?
(18:54):
That was not good for ExxonMobil.
There was layoffs.
The future felt really grim.
Life was not good.
It seemed like things were,were not going very well now.
Compare that with a company like Meta.
Well, if we couldn't go travel,we couldn't go to sports games.
What did we do to entertain ourselves?
We sat on TikTok and Instagram andscrolled all day, uh, which was
(19:14):
awesome because that meant that theycould charge a lot more money and
get a lot more advertisers, a lotmore eyeballs were on their apps.
And so meta stock went up as Exxons wentdown, and, uh, yeah, that meant they
hired a lot, they hired a lot more people.
Um, then like two or three years later.
When things were back to normal,there was less eyeballs on Instagram
(19:36):
'cause more people were driving,more people were flying on vacation.
So Exxon Stock came back up and metastock went down and they did layoffs.
So here's the truth.
Data analysts, data scientists, dataengineers, they work in all different
industries and there's gonna be peaksand valleys for different industries.
And you sometimes just have to waitand be patient and not freak out.
(19:57):
And so that's what I'mtrying to do right now.
I don't think it's worth freaking out.
I think it's just worth beingpatient for all these ai.
Dust to settle and figure outwhere we'll be in one to two years.
I think AI is a big change, butI kind of just see it as a cycle.
Alright, number 12, mentorship is theshortcut to results, and this is kind
of going back to the who you know isreally important, but in my career,
(20:21):
having mentors has made a really bigdifference because mentors are people
who have gone through what you'vealready gone through and can tell
you the path that you should take.
For example, I've been doing YouTubevideos for about four years now.
Um, but you probably, if you'rewatching this or listening to this as
a podcast, you're probably listeningto me for the first time in 2025.
(20:42):
If I had to guess.
Lemme know in the comments if I'm wrong,and when I say lemme know in the comments,
I'm really talking to my YouTube people.
Where are you guys at?
Go to the comments right now.
But also if you're listening onSpotify, Spotify has comments too.
You guys should try those there.
If you're listening to another podcast,there's probably not podcasts or
probably not comments there, but, um.
I wanna know, like, areyou new to my worlds?
(21:02):
Because you probably are.
And one of the reasons is,is I got a mentor last year.
His name's Jay Klaus, uh, and hemakes a lot of YouTube videos.
And over the last year or so thatI've kind of been in his world, I
think I've gone from like 15,000to 45,000 subscribers on YouTube.
So that's like 30,000.
So I've basically doubled, no, I, I guess,tripled my YouTube in the last year.
(21:27):
It was mostly 'cause I talked tosomeone who knew what they were
doing and they gave me good tips.
And the same is true for youin your, your career as well.
Like if you can find someone who'salready been there, done that, I
think they'll have a big impact.
It's so funny because it's not likesomething that people talk about very
much and it's not like, it's kindalike an abstract thought, but I think
it will make a really big differencein your career if you have a mentor.
(21:48):
So my suggestion is if you're trying toland your first data job, find a mentor.
Um, if you want mentorship, Ihave the accelerator program.
That helps people landtheir first data jobs.
If you're already in your role, findsomeone at work, find someone at work
who's one to two steps ahead of you.
Taking to coffee, takingto lunch, talk to them.
Ask them like what they would dodifferently, like what they've done well
(22:09):
and what they maybe had done poorly.
Tip number 13 is you'llnever stop learning.
Data analytics is constantlyevolving, and if you stop learning,
that is when your career will die.
But as long as you're willing tolearn, I think you're going to
do really well in this career.
And I think that's one of the thingsthat's made a big difference in my
career is I'm always willing to learn.
(22:30):
In fact, I read five pagesevery single day, so I am
constantly learning something.
Uh, and I spend a lot of my timeeven at work trying to read, watch
videos of things that are coming out.
I also experimenting a lot.
I'm a big experimenter where it's like,okay, I've kinda heard about this thing.
I don't really know it yet.
I'm just gonna try to open itup and see if I can use it.
I did that recently.
(22:50):
With MCP, I didn'treally know what MCP was.
Model context, protocol.
And then I, I tried basically usingClaude to build some, uh, data pipelines
and I was like, oh, I totally get MCPand I totally get why it's awesome.
So when you hear about something,like, for me, the best way to
learn about it is to like get handson experience actually doing it.
(23:11):
So there you guys have it.
If you enjoyed this, please hitsubscribe and uh, we have a new
video coming out every single week.
Thank you guys for watching.
We'll talk to you soon.
I've spent the last 10 years workingas a data analyst, data scientist,
(00:03):
and data engineer for some prettycool companies like ExxonMobil,
MIT, the Utah Jazz, and others.
In the last four years, I'vededicated my time to teaching others,
learning how to land their firstdata job, and now my students work
at Apple, Amazon, Rivian, Tesla, andsome other pretty cool companies.
Let me share 13 things I wish Iknew when I was getting started.
(00:28):
Number one, your skills aren'tholding you back properly when it
comes to landing your first day job.
It's a very frustrating process,especially in today's market.
In today's economy.
There's lots of rejection, there's lotsof frustration, there's a lot to learn.
But the majority of your time, employersdon't even know how skilled you are.
(00:49):
Like if they're hiring for a data analystposition that requires sql, and you
think SQL is what's holding you back.
The odds are SQL's not holding youback because how does the employer
know how good you are at sql?
They really don't.
Unless you've taken some sortof a technical interview.
If you're getting rejected and youthink it's your skills, it's actually
probably something like your resumeor your LinkedIn or your experience
(01:12):
that you're portraying on either ofthose, and you'll want to try to make
it look like you know more than youdo probably, if I'm being honest.
That is what's probably holding you back.
Unless you're failing technicalinterviews or you're doing technical
interviews and you're not gettinghired, your skills aren't going to
get you more technical interviews.
The better you are at SQL does notequal how many SQL interviews you have.
(01:36):
It's the perception, so you need tomake sure that you have a good LinkedIn
in a good resume highlighting sql.
But to be honest, like if someone's reallyskilled at SQL and has a bad resume and
someone's okay at SQL but has a goodresume, this person's going to get.
More interviews a lot of the time.
I know it's unfair, but that'sjust how it's number two.
(01:57):
You will get paid to learn on the job.
I promise that it'llhappen in your career.
It's happened many times in my career.
I've learned power, bi,Tableau, sql, Python, Excel.
I pretty much learnedeverything on the job.
Now, that does mean you need tohave a base, like you need to know
something that will get you hired.
Like you can't know nothing, but theodds are you're going to be learning
(02:19):
on the job quite a bit because one.
It's really hard toknow everything in data.
Like there's so many different things.
Two, it's always expanding.
So even if you did know everything today,you will not know everything a year from
now, especially with how ai, uh, and justrapid technology change and data is going.
Um, and number three, a lot ofthe times there's like more niche
softwares that you'll use that likeyou probably haven't even heard of.
(02:42):
So for example, when I was atExxonMobil, we used a tool to do
data analytics, you could say.
Um, and it was calledpims, and I'm sure like.
No one watching this hasever heard of pims, PIMS.
Uh, if you have heard of PIMS foroil, crude basket selection and
optimization, let me know in thecomments, but my guess is 99.9%
(03:03):
of you guys have never heard of it,and it's something I used every day.
And there's the equivalent of PIMS forall different industries and all sorts
of different niches inside of industries.
There's so many tools out there thatyou've never even heard of that like you
wouldn't even bother learning, but youwill be using those on the job, maybe as
like your primary data analytics tool.
So eventually you are going to getpaid to learn tools that you don't
(03:25):
know, which brings me number three.
You don't have to know everything.
You don't have to know everythingto land your first aid job.
I definitely don't know everything now.
I'm on, like whatever, my 10th datajob or whatever, however you wanna
count all my different experiences.
Like I definitely don't know everything.
Um, I even taught a data engineeringcourse at MIT and I am not
that great of a data engineer.
(03:46):
I did not know that much about dataengineering when I took that role.
Uh, and the truth is like, it'sokay to not know everything.
You won't know everything.
And you don't have to know everything.
Now you do.
You have to know something.
Yeah.
You have to know something.
But the idea that you have to like knowevery single thing before you can even
start applying is just holding you back.
So the quicker you realize, Hey,I don't know everything, and
(04:09):
that's okay, I'll figure it out.
What I do need to know, uh, thesooner you'll be better off.
Because that is like the biggestmindset change that will allow you
to apply for more jobs, more stretchjobs, things that feel like you're
not going to land, but you might land.
You never know.
Just know.
You don't have to know it all.
That's it.
Four, and this one kind ofsucks, but who, you know, matters
(04:30):
way more than what you do.
Uh, it's not, it's not what youknow, it's who you know, right?
Like that old adage.
Uh, that is so true.
Um, I think most of the successI've had in my career has
really been to who I know.
Um, now I didn't know allthose people to start.
I made a lot of those connections,uh, from the ground up.
And if you don't know anyone, you can makethose connections from the ground up too.
(04:54):
But like.
We just live in a society anda world where opportunity is
given to people and people.
It's not necessarily merit based.
It's more risk free based.
And let me explain because it's likewhen you're trying to fill a position
or when you're looking for a leaderin a project or you're looking to
promote someone, a lot of the timesit's like, Hey, well who do we know?
(05:15):
Right?
Uh, how talented you are or what you'vedone is only as valuable as the people
in power who know about those things.
I, I work in a shed in my backyard,and let's say like I cured cancer
back here, like I solved thebiggest mystery in the world.
If no one knows about it, itdoesn't really make a difference.
Now that is such an accomplishment thatif I told someone, it would probably
(05:38):
go through the grapevine and then I getinterviewed by the local news and I get
interviewed by the national news, and thenwho knows, maybe I'm winning like a Nobel
Prize and everyone knows my name and I'mthe most famous person on planet Earth.
That's definitely possible.
Like your accomplishments can be so good.
That it makes you known to the wholeworld, but for the majority of us,
that's probably not gonna be the case.
And so it's important that the good workwe do, do gets recognized by people.
(06:04):
And you have to know people inorder to get recognized by people.
So spend time at work, getting toknow your coworkers, getting to know
your boss, getting know, getting toknow your boss's boss, getting to know
your, like boss's, like equivalent ona different organization or a different
division or something like that.
Like who you know, reallymatters in your career and will
make a really big difference.
(06:24):
And if you're not quite in a careerthat you wanna be in right now,
that can be true for networkingbefore you get into a career.
So for example, all of myaccelerator students, you know,
they're new to data analytics.
They're transferring from being ateacher or being a delivery driver.
Uh, or I don't know, beinga scientist or something.
(06:45):
Getting to know me is valuable,to be honest, because I
have a lot of connections.
I have like 150.
A thousand connections orfollowers on LinkedIn, right?
I have this YouTubechannel, I have my podcast.
I know people who are hiring.
I can, you know, talk aboutpeople in my newsletter.
I can talk about people, my studentson LinkedIn, so on and so forth.
So, oftentimes it is importantwho you know, and you can start
(07:06):
from scratch, I promise you.
Number five, your domain expertisematters more than you think,
especially in the future with, uh, ai.
Um, doing data analyticsis really important.
We never just do data analyticsfor data analytics sake.
It's not for funsies thatwe're analyzing data.
It's always to make an organizationdecision to make a business decision, to
(07:28):
save money, to save time, to save lives.
We're doing data analytics for the theends, not for just doing it, right.
There's not like just arollout there in the world.
That's just like doing dataanalytics on data analytics.
That's very meta, right?
All the data analytics jobs areon healthcare data, on financial
data, on manufacturing data.
(07:51):
And so whatever you've donein the past is really valuable
because you understand the domain.
You have what's called domain expertise,and if you just brought in like a
random data analyst, they would notunderstand your domain as well as you do.
When I worked at ExxonMobil, I havea chemical engineering background,
so I studied chemistry, I studiedengineering, I studied manufacturing.
(08:11):
So every once in a while we'd have these.
Company-wide analytics on competitionsand anyone could, could enter and
you, they give you a data set andthey'd say, analyze this data set.
And at the time I was pretty, pretty newto the data world and wasn't necessarily
the best data scientists or data analysts.
I was competing against peoplewho had PhDs in data science,
(08:32):
who had PhDs in computer science.
We had PhDs in mathematics and I was ableto outperform them in these competitions
a lot of the time, not 'cause I wassmarter than them, or I could make better
models or I could code better than them.
Because I could relate what littleof data analytics I knew to the
business problem, to the actualdomain better than they could.
(08:52):
I understood the rulesof like the business.
I understood like the rules ofscience, of, of manufacturing, of
engineering, and that really helpedme craft better analysis and craft
better explanations of my analysis.
If you have a background that's notdata analytics, that's not statistics,
that's actually a good thing.
Like your domain can really matter.
(09:14):
Now you can transfer domains.
That's a thing.
People do it all the time.
But I just wanna tell you, your domain isvaluable and you shouldn't give up on it.
Number six, don't take job rejections.
So personally, no one likesgetting rejected, right?
It's never fun whether it'slike getting rejected on.
A date that you ask or like you ask a girlfor her number, uh, or you apply for a
(09:34):
job and they reject you, but don't takeit personally, especially job rejections
in today's economy, because like there'shundreds of applicants for every job.
So like every job you applyfor, let's just assume there's
like 200, 300 applicants.
That means like if we say 300 applicants,299 people are gonna get rejections.
So it's gonna happen moreoften than you think.
(09:56):
A lot of the time nowadays with theA TS that stands to our applicant
tracking system, it's the suite of toolsthat recruiters and hiring managers
use to try to make it easier for themto decide who's the right candidate.
The A TS sucks, youguys, it's not very good.
It's like not a verygood piece of technology.
I'm looking forward to seeing over thenext five, 10 years how it becomes better.
(10:18):
But right now it kind of sucks anda lot of the times you're not even
getting your resume seen by human.
It's just a computer, a sillycomputer who's looking at your
resume and is like, eh, I don'tthink this resume is very good.
But it doesn't really knowwhat a good resume is.
We're in this world where we're gettingrejected all the time by computers
and it makes us feel bad, but likethe truth is that like these computers
(10:40):
aren't very smart, uh, and they're notmaking good decisions to be honest.
They're making decisions thatlimit that help hiring managers and
recruiters spend less time, but notnecessarily make the optimal decision.
And the truth is that like outof, um, I dunno, 300 candidates, I
interviewed a hiring manager one timewho I think had like 250 applicants
(11:02):
and she was like, yeah, I think50 of them would've been great.
So like literally I'll say 20% would'vebeen great candidates and if they would've
hired them, it would've worked out.
So like even, even a lot oftimes you're getting rejected.
You could have done the job and youcould have done really great at it.
It still sucks, I realize that.
But I think what sucks more is whenwe take the rejection so personally
(11:23):
that we kinda get depressed and westop applying for jobs and then we
never actually change our career.
I don't want that to happen to you.
So like, please stoptaking the rejections.
So personally and just realize it's justa silly computer making a silly decision.
That's why networking, what wetalked about earlier, who you
know is really important because.
If you know the right people, you canskip the whole a TS altogether and just
(11:45):
get an an interview and then show yourpersonality there and explain everything.
Humans can like understand the totalityof a candidate of a human candidate,
but computers, they just really look atresume and it's like they're only seeing,
I don't know, 10% of who you actuallyare and what you're actually capable of.
It's just silly.
(12:05):
You guys don't take it personally.
Number seven, data tiles.
They're super confusing.
The titles of different datajobs are all over the place.
Obviously.
There's like data scientists,data analysts, data engineer.
Those are like pretty cementedand pretty straightforward.
But I've seen data science analysts,I've seen data analytics scientists,
(12:25):
like I've seen so many different roles.
My job at ExxonMobil was for awhile was optimization engineer.
That doesn't sound data E at all,but I really just built models and.
Power BI dashboards the wholetime I was in that role.
So like you just can't judgea job off of the job title.
(12:46):
Sometimes job titles are are weird becausethe company just doesn't know better
and they're kind of just making it up.
Other times, like there'sjust no industry standards.
So it's just kind of all over the place.
But just know that like you need tobe looking at the like requirements
and making a judgment yourself onwhat type of job this actually is.
So.
(13:07):
Be looking for keywords like sql,Excel visualizations, mathematical
models, machine learning and stuffinside of the description, and not
just taking the title for what it is.
Like.
You need to be coming up with yourown titles for every job description
that you read because they aregoing to be quite different.
So they're really confusing.
Don't stress it.
(13:27):
Just know that that exists.
Number eight, data toolsmatter less than you think.
What I mean by that is I thinknow, um, I'm pretty decent at
a lot of different data tools.
I think my best data toolpersonally is Python.
I'm pretty good at Python.
Next might be R for me, andthen after that it might be
Power bi, Tableau, sql, Excel.
(13:50):
But there's other ones that I can do.
I can do matlab, I can do JMP.
I could do JavaScript if Ihad to, I could do D three.
I could do pencil andpaper, like I could analyze.
So I could use a, I could analyze datawith like all sorts of different tools.
And what I mean by this is like ifyou're given a business task, like,
okay, we need to, we wanna know how manyproducts we're gonna sell next December.
(14:14):
I think I could do that in Tableau,sql, Excel, Python, r Matlab, jump.
Like I, I could do itbasically in a data tool.
So I would focus maybe a little bit lesson the data tools and more about concepts.
If you get really good at one datatool, you could probably just use that
data tool to pretty much do everything.
So I don't think data, tools,learning them all especially is as
(14:36):
important as you think they are.
Number nine, the bookends ofanalysis are the most important.
And what I mean by bookends isthink of it, think of data analytics
as like a sandwich, bread, meat,bunch of other stuff, vegetables,
condiments, bread, right?
The breads, I think arethe most important part.
I think they're going to becomemore important with the explosion
(14:58):
of AI and data analysis.
What I mean by the bread is like talkingto stakeholders at the beginning and
talking to stakeholders at the end.
Because once again, we'renot doing data analysis for
funsies, for data analysis sake.
We're doing it to make impact and tochange lives, save money, save time.
If we don't do a good job at thebeginning of talking to stakeholders,
(15:20):
we're gonna do analysis in vain.
We're gonna probably do the wronganalysis for the wrong reasons
and it's not gonna be useful.
So the more we talk to stakeholders or,or let's say that it is even useful,
it might not be adopted very well,it might not be used like so many.
You hear so many people aboutbuilding dashboards that go
on to die, never be used.
And I think a lot of the times it's not.
(15:40):
It's because they didn't spendenough time upfront explaining to the
stakeholders, okay, what do you want?
Why do you want that?
Let me create the system or servicethat works best to solve your problem.
Then secondarily the ending whereyou've actually done the analysis, you
need to tie it back to the business,show them how to use it, make sure
that they can trust it, because ifyou don't do that, once again, you're
(16:00):
gonna be doing data analysis in vain.
All the work you've done is just gonnanot go anywhere, and that happens a lot.
Don't feel bad if it happens, butif it is happening, spending more
time on the front end or the backend is probably the solution.
Number 10, how you present yourdigital self matters more than
you present your physical self.
And what I mean by that is.
Your perception is really important.
(16:22):
How you're perceived is probably moreimportant than how you actually are.
And once again, I kindof hate saying this.
I'm not saying to cheat, I'm notsaying to lie, I'm not saying
looks matter, but they kind of do.
Um, in, before you get a job, likeon a resume in LinkedIn, as well as
when you're in a company, the workthat you do is probably less important
than how your work is perceived.
(16:45):
That sucks, but it's just the game you'regoing to have to play in your career.
And if you choose not to playthat game, I think you'll suffer.
So I think it's a game worth playing.
So that means like you need topresent yourself well on LinkedIn.
You need to present yourselfwell on your resume.
You need to make sure that your bosslikes you and your boss's cousin likes
you, and you need to make sure that likeyou're talking and you're, you're getting
(17:06):
seen because that's what's important.
If your work is important, but if itdoesn't get seen, it doesn't get used.
It's honestly not important.
And in today's economy, you have totake care of you and your family.
And if that means you need to beperceived as being a good actor, a
good professional, as a net positiveto your team and organization, then
(17:28):
you should spend the time and theresources necessarily to do that.
So that would be when you'redoing something, make it known,
tell people about it, share it.
Don't just do your work in silence.
If you do your work in silence, Ithink you and your family suffer.
Number 11, all industriesexperience cycles.
Uh, I think we're in a cycle right now.
I think we're in a veryrevolutionary cycle.
(17:50):
I think AI is really changing the game,but all industries go through disruption
and they go through peaks and valleys.
Let me kind of explain.
I worked at ExxonMobil during 2020.
Now, what happened in 2020?
Everyone.
Oh, COVID.
Good job.
Class COVID happened in 2020, right?
And what happens?
(18:11):
Especially the beginning part ofCOVID March, April, may, June.
We stopped going places.
We stopped going to work.
We stopped going to the movies,we stopped going to sports games.
We stopped traveling.
What do you need to travel?
Oh, gasoline, jet fuel.
Who makes gasoline and jet fuel?
ExxonMobil.
So it was a really bad time to work atExxonMobil because our, no one was buying
(18:32):
oil and no one was buying gasoline.
So those prices went down quite a bit.
If you remember, I lived inTexas when I worked for Exxon.
I think one time I got gas in Texasduring COVID for less than $2 a
gallon, which was like very, very low.
Now in Utah, I'm paying like 3, 3, 5, Ithink per gallon, so almost half, right?
(18:54):
That was not good for ExxonMobil.
There was layoffs.
The future felt really grim.
Life was not good.
It seemed like things were,were not going very well now.
Compare that with a company like Meta.
Well, if we couldn't go travel,we couldn't go to sports games.
What did we do to entertain ourselves?
We sat on TikTok and Instagram andscrolled all day, uh, which was
(19:14):
awesome because that meant that theycould charge a lot more money and
get a lot more advertisers, a lotmore eyeballs were on their apps.
And so meta stock went up as Exxons wentdown, and, uh, yeah, that meant they
hired a lot, they hired a lot more people.
Um, then like two or three years later.
When things were back to normal,there was less eyeballs on Instagram
(19:36):
'cause more people were driving,more people were flying on vacation.
So Exxon Stock came back up and metastock went down and they did layoffs.
So here's the truth.
Data analysts, data scientists, dataengineers, they work in all different
industries and there's gonna be peaksand valleys for different industries.
And you sometimes just have to waitand be patient and not freak out.
(19:57):
And so that's what I'mtrying to do right now.
I don't think it's worth freaking out.
I think it's just worth beingpatient for all these ai.
Dust to settle and figure outwhere we'll be in one to two years.
I think AI is a big change, butI kind of just see it as a cycle.
Alright, number 12, mentorship is theshortcut to results, and this is kind
of going back to the who you know isreally important, but in my career,
(20:21):
having mentors has made a really bigdifference because mentors are people
who have gone through what you'vealready gone through and can tell
you the path that you should take.
For example, I've been doing YouTubevideos for about four years now.
Um, but you probably, if you'rewatching this or listening to this as
a podcast, you're probably listeningto me for the first time in 2025.
(20:42):
If I had to guess.
Lemme know in the comments if I'm wrong,and when I say lemme know in the comments,
I'm really talking to my YouTube people.
Where are you guys at?
Go to the comments right now.
But also if you're listening onSpotify, Spotify has comments too.
You guys should try those there.
If you're listening to another podcast,there's probably not podcasts or
probably not comments there, but, um.
I wanna know, like, areyou new to my worlds?
(21:02):
Because you probably are.
And one of the reasons is,is I got a mentor last year.
His name's Jay Klaus, uh, and hemakes a lot of YouTube videos.
And over the last year or so thatI've kind of been in his world, I
think I've gone from like 15,000to 45,000 subscribers on YouTube.
So that's like 30,000.
So I've basically doubled, no, I, I guess,tripled my YouTube in the last year.
(21:27):
It was mostly 'cause I talked tosomeone who knew what they were
doing and they gave me good tips.
And the same is true for youin your, your career as well.
Like if you can find someone who'salready been there, done that, I
think they'll have a big impact.
It's so funny because it's not likesomething that people talk about very
much and it's not like, it's kindalike an abstract thought, but I think
it will make a really big differencein your career if you have a mentor.
(21:48):
So my suggestion is if you're trying toland your first data job, find a mentor.
Um, if you want mentorship, Ihave the accelerator program.
That helps people landtheir first data jobs.
If you're already in your role, findsomeone at work, find someone at work
who's one to two steps ahead of you.
Taking to coffee, takingto lunch, talk to them.
Ask them like what they would dodifferently, like what they've done well
(22:09):
and what they maybe had done poorly.
Tip number 13 is you'llnever stop learning.
Data analytics is constantlyevolving, and if you stop learning,
that is when your career will die.
But as long as you're willing tolearn, I think you're going to
do really well in this career.
And I think that's one of the thingsthat's made a big difference in my
career is I'm always willing to learn.
(22:30):
In fact, I read five pagesevery single day, so I am
constantly learning something.
Uh, and I spend a lot of my timeeven at work trying to read, watch
videos of things that are coming out.
I also experimenting a lot.
I'm a big experimenter where it's like,okay, I've kinda heard about this thing.
I don't really know it yet.
I'm just gonna try to open itup and see if I can use it.
I did that recently.
(22:50):
With MCP, I didn'treally know what MCP was.
Model context, protocol.
And then I, I tried basically usingClaude to build some, uh, data pipelines
and I was like, oh, I totally get MCPand I totally get why it's awesome.
So when you hear about something,like, for me, the best way to
learn about it is to like get handson experience actually doing it.
(23:11):
So there you guys have it.
If you enjoyed this, please hitsubscribe and uh, we have a new
video coming out every single week.
Thank you guys for watching.
We'll talk to you soon.
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