March 4, 2025 • 16 mins
In this episode, I uncover the nine biggest LIES about landing a data job. Maybe what's stopping you from pursuing a data career is just a big lie.
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⌚ TIMESTAMPS
00:00 Introduction
00:05 You Need a Computer Science or Math Degree
01:20 You Have to Be Good at Math and Statistics
03:00 You Must Know Everything About Data Analytics
04:27 Certifications Matter
05:35 Skills Are Enough
07:20 AI Will Take Your Job
09:24 You'll Spend 80% of Your Time Cleaning Data
10:08 Data Titles
11:44 There Are Lots of Remote Jobs
13:17 The "Self-Taught" Data Analyst
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Avery (00:00):
Here are the nine biggest lies about landing a data job
that are being told this year.
Lie number one, you need acomputer science or a math degree.
There's lots of people and organizationsthat will tell you that in order to land
a data job, you need to have studiedcomputer science, math, or economics
in college, but that's not the case.
Take me for example.
I studied chemical engineeringand became a data analyst and
(00:22):
then became a data scientist.
But even then, chemicalengineering is pretty technical.
There's a lot of people whohave less technical degrees
than chemical engineering whohave landed into the data world.
For example, I've intervieweda lot of them on this channel.
We had Alex Sanchez who was a high schoolmath teacher and he pivoted into data.
We had Aaron Sheena who was a musictherapist who landed a financial
(00:44):
data analyst job at Humana.
We had Rachel Finch who studied biologyand now has a business intelligence job.
And then there was Trevor Maxwellwho doesn't even have a college
degree and ended up landinga technical data analyst job.
You don't need a computer sciencedegree and you don't need a math degree.
Whatever degree you havenow is probably good enough.
And if you don't have any collegedegree, you can probably do it as well.
(01:06):
It's just a little bitmore of an uphill battle.
I have a whole YouTube playlistwhere I talk to people who land
jobs without college degrees.
I'll have that in theshow notes down below.
Truth be told, you don't need a computerscience degree and you don't need a math
degree to break into data analytics.
Lie number two that they tellyou is that you have to be
good at math and statistics.
And honestly, you don't reallyhave to be good at either.
(01:27):
Now I am going to caveat hereand say if you want to be like
a deep research data scientist.
You probably want to be a littlebit good at math, but for the rest
of you guys who just want like anormal data analyst job, you honestly
don't have to be that good at math.
Like honestly, most of my students,when they actually land a data
job, the math that they're reallydoing is mostly aggregations.
That's like some average max min.
(01:48):
This stuff isn't complicated.
You honestly probably learnedmost of it in high school.
You may have forgotten now, but honestly,it's kind of like riding a bike.
Once you review it, you'll beable to catch up very quickly.
Now, I can already hear all of youpeople commenting and being like,
well, isn't statistics important?
There's statistics in data analytics.
And sure, there's definitely somestatistics in data analytics, but I
think most people overblow the amountof statistics you have to know.
(02:11):
In fact, a lot of programs like dataanalytics master's degrees will say
that you're supposed to know calculusand linear algebra in order to even
like, Start the program, and that'sjust a flat out lie, like the amount
of calculus and linear algebra that Iuse as a data analyst is very minimal.
Can those concepts potentially help you?
Sure, but it's not worth theamount of time that it takes to
(02:31):
actually learn all that stuff.
It's not worth it.
Like you're not really going to benefitthe return on investment, the ROI.
Is not very high.
Of course, there's things like ABtesting, hypothesis testing and
regression that are going to beuseful for a lot of data analysts.
But honestly, that stuff'snot super hard to learn.
And the majority of the time,like you're not doing the math,
the computer's doing the math.
(02:52):
So as long as you know what a hypothesis.
test is and how to set it up and howto interpret the results, you're good.
And honestly, I think you canlearn that in one to two weeks.
Lie number three is that you have toknow everything about data analytics
in order to land a data jump.
That you have to know Python, youhave to know Excel, you have to know
SQL, you have to know Tableau, youhave to know Looker, you have to know
Power BI, you have to know SAS, youhave to know R, you have to know Java.
(03:13):
So on and so forth,and it's just not true.
Honestly, you don't have to even knowthat much to be a data analyst, and
maybe just one of those skills is enough.
For example, I interviewed Matt Brattonon my podcast a while ago, and he is
like in the C suite of the data world,and he basically only uses Excel.
I've interviewed differentpeople on my podcast.
And sometimes they only useTableau or they only use SQL.
(03:34):
It really just depends.
So sometimes you only have to know onedata skill throughout your whole career.
Now saying your whole data career,that's a little bit dramatic.
Like you will probably use multipleskills throughout your career.
But when you land that first job,like really a lot of the time, you're
using one to two data tools, max.
That being said, it's like, well, how doI know which one to two that those are?
And you really don't.
And it's going to change from job to job.
(03:56):
But here's what I will tell youthat Python is only required 30
percent of the time for all dataanalyst jobs from junior to senior.
So personally, I don't really thinkit's worth learning to be able to
apply to those extra 30 percent of thejobs when you're just getting started.
I did an episode about this previously.
You can see it right here and I'llhave a link to it in the show notes
where I really don't think you shouldstart with Python or R to be honest.
(04:19):
The lie is that youhave to know everything.
And the truth is you don't,you can get started today.
And honestly, you can probably landa job pretty soon with The skills
you have already line number threeis that certifications matter.
I don't care if it's the IBM certificate,the power BI certificates, the
Google data analytics certificate.
The truth is for the majority ofdata jobs, your cert does not matter.
I know that might hurt to hear, andyou might not want to believe me, but I
(04:42):
actually run my own job board, find a job.
com.
And I analyze the 2000 plus jobs thatI've posted on there the last four months.
And not once did any ofthe jobs posted on there.
Ask for any sort of certificate.
I know like the badges look cooland like the certificate looks cool.
The truth is no one really cares.
At least employers don't really care.
I have a lot of people who messageme and they'll say, Hey, Avery,
(05:04):
I don't need your bootcamp.
I'm already data analyst certified.
And that is like the biggestlie that you could ever say.
And I understand that someone did.
Certify you as a data analyst, butthere's nothing in the industry that's
standardized that makes you data analystcertified It's not like a nurse or a
teacher where like you have a license.
That's the wild west out here in the dataworld We don't care about that stuff.
(05:24):
So having a certificate.
It's not a bad thing necessarily But it'snot like all that you might think it is.
It's not your golden ticket into thedata world It takes a lot more than
that and that leads me to my next lielie Number four is that skills are
enough now you think that like If youwant to be data analyst, you have to
learn these X amount of things, and thenyou can become a data analyst, right?
(05:44):
Wrong.
Skills aren't enough when you'retrying to land a data analyst
position for multiple reasons.
One, as data analyst, like you'reactually not just spending your whole
time using those technical skills.
Like you're not just in Excel all day.
One of the most important things you'll bedoing as a data analyst is communicating,
is working with stakeholders.
Is talking to teams and leaders andunderstanding, you know, what the data
is, where the data is at, what, howyou should analyze it, what's important
(06:07):
for them to know, so on and so forth.
But two, anytime you're trying toland a data job, it's not the most
skilled person who lands the job.
Like think about it.
I'm down here in my office.
If I spent the next 240 years of mylife just studying data analytics,
but I didn't have a resume, wouldI land many data analytics jobs?
Probably not because it takesmore than just your skills.
There's a whole variety of thingsthat will actually help you get hired.
(06:30):
I create a little mnemonicfor you to remember.
It's called the SPN method, andit's the easiest and fastest
way to become a data analyst.
S stands for skills, and that'sone third of the equation.
But it's only one third of the equation.
You need the P and the N.
The P stands for projects or portfolios.
And these are basically opportunitiesfor you to showcase your skills because
anyone can say that they know SQL, butyou want to back that up with tangible
(06:51):
evidence to a recruiter or hiringmanager via project on your portfolio.
The N stands for networking andreally like 70 percent of jobs
are done through networking.
You're really gettingrecruited or referred.
And so there's a lot of differentways you can network and a lot of
different things that you can do toincrease your chance of getting hired.
That is totally irrelevant andnot even related to your skills.
There is no correlation to how skilled youare, how quickly you land a data job, and
(07:14):
how much you get paid as a data analyst.
If you want to learn more aboutthe SVN method, I'll have a link
in the show notes down below.
Lie number five is that AIis going to take your job.
It's really interesting becausea lot of people are nervous about
becoming a data analyst because theydon't feel like it's very AI proof.
And one thing I've beenthinking to myself is Okay.
Well, what careers are AI proof?
In fact, I had one perspective student.
(07:35):
He was messaging me and saying that hisfriend was kind of making fun of him
because like data analysts are goingto be replaced by AI and he had like a
blue collar, more like mechanical job.
And that was never goingto be replaced by AI.
I think that's interesting becauselike throughout history, haven't
we seen like more of the mechanicaljobs being replaced by AI?
previously.
So like, I think those jobs aren't safe.
(07:56):
And then I thought, oh, maybelike a doctor that I was like,
well, aren't like a bunch of likerobots doing surgeries nowadays.
And like, can't you just kind of likeuse web MD or whatever chat, GBT to like
ask what's wrong and get a diagnosis.
Obviously there's going to be somejobs like nurses, for example,
where I think that is basicallyimpossible to have a robot or AI do.
But honestly, I've used AIto try to analyze data and
(08:18):
it's definitely not great.
Another thing you should realizeis the difference between
augmentation and automation using AI.
Augmentation is almost like you canthink of it like putting on like the
like glove in Iron Man or something?
I don't know.
I'm not good at Marvel, you guys.
Uh, like, like the InfinityStones in that one movie, right?
Like, that changes who youcan be and the powers that you
have, but you're still yourself.
(08:40):
And then the other one would be like,no, I create a robot that's super
powerful and it replaces me completely.
And honestly, AI is going to augment you.
That's for sure.
It's going to change how work is done.
But it's still you doingthe work a lot of the time.
I've seen a lot of these companiestry to come out with like the auto
analyzing data and it's not great so far.
Is it going to get better in the future?
(09:00):
Yes, definitely.
But I definitely don't seethe human element getting
taken out of it anytime soon.
The ability to reason to actually findlike what's relevant to the business and
then explain all that back to someone Ithink is something that's very valuable.
I'm a data analyst, right?
I teach people how tobecome data analysts.
So my future is very heavilytied in this and I honestly
am not that worried about it.
I think that, AI is going tohelp us be better data analysts,
(09:22):
and that's about the gist of it.
So lie number five is thatAI is going to take your job.
Lie number six is that you'regoing to spend 80 percent of
your time cleaning your data.
I don't know where this came from, andI don't know who made it, and I don't
really know who propagates it further.
Personally, in the roles that I've beenin, sure, data cleaning is important,
and it does take a significant amountof time, but it's nowhere close to 80%.
(09:44):
Honestly, if you're spending 80 percent ofyour time cleaning data, You're probably
spending your time on the wrong things.
I honestly think that like 80 percentof your time should be spent talking
to people as a data analyst beforeyou start a project, when you're in
the project and after the project,I think communication is actually
way underplayed in the data world.
But I don't know who's sayingthat 80 percent of your time is
(10:05):
cleaning data because that's.
A huge exaggeration.
Data lie number seven is all datatitles, uh, and I'm just so sorry
for all you job seekers out there.
This is the most frustrating thingon planet earth, but once again,
the data world is the wild wild westand basically job titles are all
kind of made up in the data world.
There's kind of like the big three.
There's the data engineer, the dataanalyst, and the data scientist.
(10:27):
But there's so many more positions inbetween that overlap and that are the
same and that are misclassified andcompanies will call something, you know,
a data analyst one place, but that'sreally a data scientist other places.
And it's really confusing.
So all the data titles you're readingon the job board are probably lies.
And you should try to base it off ofwhat's like in the requirements section
(10:47):
of the job description to actuallyknow what the job is going to entail
and what the actual title kind of is.
For instance, there's somethingcalled a data science analyst.
I don't know what the heck that is.
I've even seen data analytics scientist.
Technically, my role at Exxon for a longtime was optimization engineer, but I was
really doing the work of a data scientist.
And even at my first job, I wastechnically a data analyst, but you could
(11:09):
have also called me a chemometrician.
There's so many different titles.
They're so confusing.
Honestly, I've CEO reach out to meone time and ask me to look over.
Their job description forhiring their first data analyst.
I looked it over and I waslike, this is a data scientist
job, not a data analyst job.
And he replied, well,what's the difference.
And this is like, nota super small company.
Like this is definitely acompany you've heard of before.
(11:31):
I guess it was technically likea general manager, not the CEO.
It was like the president of alocal area anyways, but still
like that is pretty crazy.
Right.
The people who are writing these jobdescriptions maybe don't necessarily know.
a hundred percent whatthey're talking about.
Lie number eight is thatthere is lots of remote jobs.
And now this one's super interestingbecause anecdotally, it does feel
like there is a lot of remote jobs.
(11:52):
Most of my friends who work indata have pretty flexible schedules
and lives for the most part.
And most of my students in myprogram get pretty flexible jobs.
But when I went and actuallydid the research myself and I
started web scraping job listings.
I found that remote jobs only makeabout 16 percent of all the jobs on
the market, meaning the other jobs, theremaining 85 percent ish are not remote.
(12:14):
And obviously most of you guys watchingprobably are interested in a remote job.
So let's say that 95 percent ofpeople are interested in a remote job.
That means there's a demand 95percent for a low supply of 15 percent
of jobs that are actually remote.
And this is one of the reasons why thejob market is so crazy right now and
really frustrating and it feels likeit's impossible to land a day job.
The truth is there's just not as manyremote jobs as you may think there
(12:37):
is, but there's actually equallythe same amount of hybrid jobs.
So there's about 15 to 16 percent ofjobs in the market that are hybrid.
And the cool thing about hybrid jobsis it's on a spectrum of being in
the office and working from home, andevery hybrid job is somewhere on that
spectrum, but in different places.
Some of my students work from theoffice four times a week and then
work remotely one day of the week.
(12:57):
Sometimes it's reversed.
Like for instance, some of my studentswho work at Humana, they work from
home four days a week and theywork in the office one day a week.
I even have one student who ishybrid, but she's only required to
go in the office once a quarter.
Now, to me that's more remotethan it is hybrid, but it
was still labeled as hybrid.
So I think the biggest play andwhat you guys should be focusing
on right now is hybrid jobs.
(13:17):
Lie number nine is the self taught dataanalyst or the self taught data scientist.
So many people will say I'm self taught.
And first off, what theheck does that even mean?
Like you're learning from somewhere.
It's not like you just like wentout into your yard and like really
thought hard and you're like, Oh, yes.
What if I like Excel and Vlookups wouldmake a lot of sense in a pivot table?
(13:38):
Yes.
Oh, and joins and SQL.
That makes a lot like you're not justlike divinely absorbing this knowledge.
You're learning from somewhere,whether it's a book, whether
it's online, so on and so forth.
I think most people say self taughtbecause they maybe don't have a
formal degree or something like that.
I would consider myself.
Self taught, but I eventuallygot a master's degree in
data analytics in college.
(13:59):
I took statistics classes thatgot me really interested in data.
I had a really goodmentor at my first job.
He taught me a lot.
So I think the concept of, I want to be aself taught data analyst is kind of silly.
It's also like, you don't get senta trophy for being a self taught
data analyst, like who caresif you're self taught or not?
Like you don't get to wear like a badge.
It's like, Oh wow.
Like she's self taught.
(14:19):
He's self taught likenow, like it's okay to be.
You know, not self taught likethat's totally acceptable.
And honestly, maybe you shouldwear that as a badge of honor.
It's like, no, I didn't do thison my own because I knew I needed
help or I wanted to do this faster.
So I sought help like there'snothing wrong with that.
That's plenty cool as doing it yourself.
So there you have it.
The nine biggest lies of becoming adata analyst and landing a data job.
(14:43):
Are there any myths that I missed?
Put them in the comments down below andI'll try to respond to every comment.
Here are the nine biggest lies about landing a data job
that are being told this year.
Lie number one, you need acomputer science or a math degree.
There's lots of people and organizationsthat will tell you that in order to land
a data job, you need to have studiedcomputer science, math, or economics
in college, but that's not the case.
Take me for example.
I studied chemical engineeringand became a data analyst and
(00:22):
then became a data scientist.
But even then, chemicalengineering is pretty technical.
There's a lot of people whohave less technical degrees
than chemical engineering whohave landed into the data world.
For example, I've intervieweda lot of them on this channel.
We had Alex Sanchez who was a high schoolmath teacher and he pivoted into data.
We had Aaron Sheena who was a musictherapist who landed a financial
(00:44):
data analyst job at Humana.
We had Rachel Finch who studied biologyand now has a business intelligence job.
And then there was Trevor Maxwellwho doesn't even have a college
degree and ended up landinga technical data analyst job.
You don't need a computer sciencedegree and you don't need a math degree.
Whatever degree you havenow is probably good enough.
And if you don't have any collegedegree, you can probably do it as well.
(01:06):
It's just a little bitmore of an uphill battle.
I have a whole YouTube playlistwhere I talk to people who land
jobs without college degrees.
I'll have that in theshow notes down below.
Truth be told, you don't need a computerscience degree and you don't need a math
degree to break into data analytics.
Lie number two that they tellyou is that you have to be
good at math and statistics.
And honestly, you don't reallyhave to be good at either.
(01:27):
Now I am going to caveat hereand say if you want to be like
a deep research data scientist.
You probably want to be a littlebit good at math, but for the rest
of you guys who just want like anormal data analyst job, you honestly
don't have to be that good at math.
Like honestly, most of my students,when they actually land a data
job, the math that they're reallydoing is mostly aggregations.
That's like some average max min.
(01:48):
This stuff isn't complicated.
You honestly probably learnedmost of it in high school.
You may have forgotten now, but honestly,it's kind of like riding a bike.
Once you review it, you'll beable to catch up very quickly.
Now, I can already hear all of youpeople commenting and being like,
well, isn't statistics important?
There's statistics in data analytics.
And sure, there's definitely somestatistics in data analytics, but I
think most people overblow the amountof statistics you have to know.
(02:11):
In fact, a lot of programs like dataanalytics master's degrees will say
that you're supposed to know calculusand linear algebra in order to even
like, Start the program, and that'sjust a flat out lie, like the amount
of calculus and linear algebra that Iuse as a data analyst is very minimal.
Can those concepts potentially help you?
Sure, but it's not worth theamount of time that it takes to
(02:31):
actually learn all that stuff.
It's not worth it.
Like you're not really going to benefitthe return on investment, the ROI.
Is not very high.
Of course, there's things like ABtesting, hypothesis testing and
regression that are going to beuseful for a lot of data analysts.
But honestly, that stuff'snot super hard to learn.
And the majority of the time,like you're not doing the math,
the computer's doing the math.
(02:52):
So as long as you know what a hypothesis.
test is and how to set it up and howto interpret the results, you're good.
And honestly, I think you canlearn that in one to two weeks.
Lie number three is that you have toknow everything about data analytics
in order to land a data jump.
That you have to know Python, youhave to know Excel, you have to know
SQL, you have to know Tableau, youhave to know Looker, you have to know
Power BI, you have to know SAS, youhave to know R, you have to know Java.
(03:13):
So on and so forth,and it's just not true.
Honestly, you don't have to even knowthat much to be a data analyst, and
maybe just one of those skills is enough.
For example, I interviewed Matt Brattonon my podcast a while ago, and he is
like in the C suite of the data world,and he basically only uses Excel.
I've interviewed differentpeople on my podcast.
And sometimes they only useTableau or they only use SQL.
(03:34):
It really just depends.
So sometimes you only have to know onedata skill throughout your whole career.
Now saying your whole data career,that's a little bit dramatic.
Like you will probably use multipleskills throughout your career.
But when you land that first job,like really a lot of the time, you're
using one to two data tools, max.
That being said, it's like, well, how doI know which one to two that those are?
And you really don't.
And it's going to change from job to job.
(03:56):
But here's what I will tell youthat Python is only required 30
percent of the time for all dataanalyst jobs from junior to senior.
So personally, I don't really thinkit's worth learning to be able to
apply to those extra 30 percent of thejobs when you're just getting started.
I did an episode about this previously.
You can see it right here and I'llhave a link to it in the show notes
where I really don't think you shouldstart with Python or R to be honest.
(04:19):
The lie is that youhave to know everything.
And the truth is you don't,you can get started today.
And honestly, you can probably landa job pretty soon with The skills
you have already line number threeis that certifications matter.
I don't care if it's the IBM certificate,the power BI certificates, the
Google data analytics certificate.
The truth is for the majority ofdata jobs, your cert does not matter.
I know that might hurt to hear, andyou might not want to believe me, but I
(04:42):
actually run my own job board, find a job.
com.
And I analyze the 2000 plus jobs thatI've posted on there the last four months.
And not once did any ofthe jobs posted on there.
Ask for any sort of certificate.
I know like the badges look cooland like the certificate looks cool.
The truth is no one really cares.
At least employers don't really care.
I have a lot of people who messageme and they'll say, Hey, Avery,
(05:04):
I don't need your bootcamp.
I'm already data analyst certified.
And that is like the biggestlie that you could ever say.
And I understand that someone did.
Certify you as a data analyst, butthere's nothing in the industry that's
standardized that makes you data analystcertified It's not like a nurse or a
teacher where like you have a license.
That's the wild west out here in the dataworld We don't care about that stuff.
(05:24):
So having a certificate.
It's not a bad thing necessarily But it'snot like all that you might think it is.
It's not your golden ticket into thedata world It takes a lot more than
that and that leads me to my next lielie Number four is that skills are
enough now you think that like If youwant to be data analyst, you have to
learn these X amount of things, and thenyou can become a data analyst, right?
(05:44):
Wrong.
Skills aren't enough when you'retrying to land a data analyst
position for multiple reasons.
One, as data analyst, like you'reactually not just spending your whole
time using those technical skills.
Like you're not just in Excel all day.
One of the most important things you'll bedoing as a data analyst is communicating,
is working with stakeholders.
Is talking to teams and leaders andunderstanding, you know, what the data
is, where the data is at, what, howyou should analyze it, what's important
(06:07):
for them to know, so on and so forth.
But two, anytime you're trying toland a data job, it's not the most
skilled person who lands the job.
Like think about it.
I'm down here in my office.
If I spent the next 240 years of mylife just studying data analytics,
but I didn't have a resume, wouldI land many data analytics jobs?
Probably not because it takesmore than just your skills.
There's a whole variety of thingsthat will actually help you get hired.
(06:30):
I create a little mnemonicfor you to remember.
It's called the SPN method, andit's the easiest and fastest
way to become a data analyst.
S stands for skills, and that'sone third of the equation.
But it's only one third of the equation.
You need the P and the N.
The P stands for projects or portfolios.
And these are basically opportunitiesfor you to showcase your skills because
anyone can say that they know SQL, butyou want to back that up with tangible
(06:51):
evidence to a recruiter or hiringmanager via project on your portfolio.
The N stands for networking andreally like 70 percent of jobs
are done through networking.
You're really gettingrecruited or referred.
And so there's a lot of differentways you can network and a lot of
different things that you can do toincrease your chance of getting hired.
That is totally irrelevant andnot even related to your skills.
There is no correlation to how skilled youare, how quickly you land a data job, and
(07:14):
how much you get paid as a data analyst.
If you want to learn more aboutthe SVN method, I'll have a link
in the show notes down below.
Lie number five is that AIis going to take your job.
It's really interesting becausea lot of people are nervous about
becoming a data analyst because theydon't feel like it's very AI proof.
And one thing I've beenthinking to myself is Okay.
Well, what careers are AI proof?
In fact, I had one perspective student.
(07:35):
He was messaging me and saying that hisfriend was kind of making fun of him
because like data analysts are goingto be replaced by AI and he had like a
blue collar, more like mechanical job.
And that was never goingto be replaced by AI.
I think that's interesting becauselike throughout history, haven't
we seen like more of the mechanicaljobs being replaced by AI?
previously.
So like, I think those jobs aren't safe.
(07:56):
And then I thought, oh, maybelike a doctor that I was like,
well, aren't like a bunch of likerobots doing surgeries nowadays.
And like, can't you just kind of likeuse web MD or whatever chat, GBT to like
ask what's wrong and get a diagnosis.
Obviously there's going to be somejobs like nurses, for example,
where I think that is basicallyimpossible to have a robot or AI do.
But honestly, I've used AIto try to analyze data and
(08:18):
it's definitely not great.
Another thing you should realizeis the difference between
augmentation and automation using AI.
Augmentation is almost like you canthink of it like putting on like the
like glove in Iron Man or something?
I don't know.
I'm not good at Marvel, you guys.
Uh, like, like the InfinityStones in that one movie, right?
Like, that changes who youcan be and the powers that you
have, but you're still yourself.
(08:40):
And then the other one would be like,no, I create a robot that's super
powerful and it replaces me completely.
And honestly, AI is going to augment you.
That's for sure.
It's going to change how work is done.
But it's still you doingthe work a lot of the time.
I've seen a lot of these companiestry to come out with like the auto
analyzing data and it's not great so far.
Is it going to get better in the future?
(09:00):
Yes, definitely.
But I definitely don't seethe human element getting
taken out of it anytime soon.
The ability to reason to actually findlike what's relevant to the business and
then explain all that back to someone Ithink is something that's very valuable.
I'm a data analyst, right?
I teach people how tobecome data analysts.
So my future is very heavilytied in this and I honestly
am not that worried about it.
I think that, AI is going tohelp us be better data analysts,
(09:22):
and that's about the gist of it.
So lie number five is thatAI is going to take your job.
Lie number six is that you'regoing to spend 80 percent of
your time cleaning your data.
I don't know where this came from, andI don't know who made it, and I don't
really know who propagates it further.
Personally, in the roles that I've beenin, sure, data cleaning is important,
and it does take a significant amountof time, but it's nowhere close to 80%.
(09:44):
Honestly, if you're spending 80 percent ofyour time cleaning data, You're probably
spending your time on the wrong things.
I honestly think that like 80 percentof your time should be spent talking
to people as a data analyst beforeyou start a project, when you're in
the project and after the project,I think communication is actually
way underplayed in the data world.
But I don't know who's sayingthat 80 percent of your time is
(10:05):
cleaning data because that's.
A huge exaggeration.
Data lie number seven is all datatitles, uh, and I'm just so sorry
for all you job seekers out there.
This is the most frustrating thingon planet earth, but once again,
the data world is the wild wild westand basically job titles are all
kind of made up in the data world.
There's kind of like the big three.
There's the data engineer, the dataanalyst, and the data scientist.
(10:27):
But there's so many more positions inbetween that overlap and that are the
same and that are misclassified andcompanies will call something, you know,
a data analyst one place, but that'sreally a data scientist other places.
And it's really confusing.
So all the data titles you're readingon the job board are probably lies.
And you should try to base it off ofwhat's like in the requirements section
(10:47):
of the job description to actuallyknow what the job is going to entail
and what the actual title kind of is.
For instance, there's somethingcalled a data science analyst.
I don't know what the heck that is.
I've even seen data analytics scientist.
Technically, my role at Exxon for a longtime was optimization engineer, but I was
really doing the work of a data scientist.
And even at my first job, I wastechnically a data analyst, but you could
(11:09):
have also called me a chemometrician.
There's so many different titles.
They're so confusing.
Honestly, I've CEO reach out to meone time and ask me to look over.
Their job description forhiring their first data analyst.
I looked it over and I waslike, this is a data scientist
job, not a data analyst job.
And he replied, well,what's the difference.
And this is like, nota super small company.
Like this is definitely acompany you've heard of before.
(11:31):
I guess it was technically likea general manager, not the CEO.
It was like the president of alocal area anyways, but still
like that is pretty crazy.
Right.
The people who are writing these jobdescriptions maybe don't necessarily know.
a hundred percent whatthey're talking about.
Lie number eight is thatthere is lots of remote jobs.
And now this one's super interestingbecause anecdotally, it does feel
like there is a lot of remote jobs.
(11:52):
Most of my friends who work indata have pretty flexible schedules
and lives for the most part.
And most of my students in myprogram get pretty flexible jobs.
But when I went and actuallydid the research myself and I
started web scraping job listings.
I found that remote jobs only makeabout 16 percent of all the jobs on
the market, meaning the other jobs, theremaining 85 percent ish are not remote.
(12:14):
And obviously most of you guys watchingprobably are interested in a remote job.
So let's say that 95 percent ofpeople are interested in a remote job.
That means there's a demand 95percent for a low supply of 15 percent
of jobs that are actually remote.
And this is one of the reasons why thejob market is so crazy right now and
really frustrating and it feels likeit's impossible to land a day job.
The truth is there's just not as manyremote jobs as you may think there
(12:37):
is, but there's actually equallythe same amount of hybrid jobs.
So there's about 15 to 16 percent ofjobs in the market that are hybrid.
And the cool thing about hybrid jobsis it's on a spectrum of being in
the office and working from home, andevery hybrid job is somewhere on that
spectrum, but in different places.
Some of my students work from theoffice four times a week and then
work remotely one day of the week.
(12:57):
Sometimes it's reversed.
Like for instance, some of my studentswho work at Humana, they work from
home four days a week and theywork in the office one day a week.
I even have one student who ishybrid, but she's only required to
go in the office once a quarter.
Now, to me that's more remotethan it is hybrid, but it
was still labeled as hybrid.
So I think the biggest play andwhat you guys should be focusing
on right now is hybrid jobs.
(13:17):
Lie number nine is the self taught dataanalyst or the self taught data scientist.
So many people will say I'm self taught.
And first off, what theheck does that even mean?
Like you're learning from somewhere.
It's not like you just like wentout into your yard and like really
thought hard and you're like, Oh, yes.
What if I like Excel and Vlookups wouldmake a lot of sense in a pivot table?
(13:38):
Yes.
Oh, and joins and SQL.
That makes a lot like you're not justlike divinely absorbing this knowledge.
You're learning from somewhere,whether it's a book, whether
it's online, so on and so forth.
I think most people say self taughtbecause they maybe don't have a
formal degree or something like that.
I would consider myself.
Self taught, but I eventuallygot a master's degree in
data analytics in college.
(13:59):
I took statistics classes thatgot me really interested in data.
I had a really goodmentor at my first job.
He taught me a lot.
So I think the concept of, I want to be aself taught data analyst is kind of silly.
It's also like, you don't get senta trophy for being a self taught
data analyst, like who caresif you're self taught or not?
Like you don't get to wear like a badge.
It's like, Oh wow.
Like she's self taught.
(14:19):
He's self taught likenow, like it's okay to be.
You know, not self taught likethat's totally acceptable.
And honestly, maybe you shouldwear that as a badge of honor.
It's like, no, I didn't do thison my own because I knew I needed
help or I wanted to do this faster.
So I sought help like there'snothing wrong with that.
That's plenty cool as doing it yourself.
So there you have it.
The nine biggest lies of becoming adata analyst and landing a data job.
(14:43):
Are there any myths that I missed?
Put them in the comments down below andI'll try to respond to every comment.
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