150: 9 Huge LIES About Becoming a Data Analyst Nobody Talks About

Avery: 00:00:00

Here are the nine biggest lies about landing a data job

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that are being told this year.

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Lie number one, you need a

computer science or a math degree.

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There's lots of people and organizations

that will tell you that in order to land

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a data job, you need to have studied

computer science, math, or economics

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in college, but that's not the case.

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Take me for example.

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I studied chemical engineering

and became a data analyst and

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then became a data scientist.

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But even then, chemical

engineering is pretty technical.

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There's a lot of people who

have less technical degrees

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than chemical engineering who

have landed into the data world.

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For example, I've interviewed

a lot of them on this channel.

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We had Alex Sanchez who was a high school

math teacher and he pivoted into data.

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We had Aaron Sheena who was a music

therapist who landed a financial

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data analyst job at Humana.

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We had Rachel Finch who studied biology

and now has a business intelligence job.

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And then there was Trevor Maxwell

who doesn't even have a college

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degree and ended up landing

a technical data analyst job.

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You don't need a computer science

degree and you don't need a math degree.

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Whatever degree you have

now is probably good enough.

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And if you don't have any college

degree, you can probably do it as well.

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It's just a little bit

more of an uphill battle.

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I have a whole YouTube playlist

where I talk to people who land

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jobs without college degrees.

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I'll have that in the

show notes down below.

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Truth be told, you don't need a computer

science degree and you don't need a math

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degree to break into data analytics.

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Lie number two that they tell

you is that you have to be

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good at math and statistics.

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And honestly, you don't really

have to be good at either.

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Now I am going to caveat here

and say if you want to be like

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a deep research data scientist.

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You probably want to be a little

bit good at math, but for the rest

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of you guys who just want like a

normal data analyst job, you honestly

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don't have to be that good at math.

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Like honestly, most of my students,

when they actually land a data

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job, the math that they're really

doing is mostly aggregations.

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That's like some average max min.

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This stuff isn't complicated.

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You honestly probably learned

most of it in high school.

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You may have forgotten now, but honestly,

it's kind of like riding a bike.

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Once you review it, you'll be

able to catch up very quickly.

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Now, I can already hear all of you

people commenting and being like,

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well, isn't statistics important?

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There's statistics in data analytics.

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And sure, there's definitely some

statistics in data analytics, but I

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think most people overblow the amount

of statistics you have to know.

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In fact, a lot of programs like data

analytics master's degrees will say

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that you're supposed to know calculus

and linear algebra in order to even

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like, Start the program, and that's

just a flat out lie, like the amount

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of calculus and linear algebra that I

use as a data analyst is very minimal.

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Can those concepts potentially help you?

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Sure, but it's not worth the

amount of time that it takes to

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actually learn all that stuff.

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It's not worth it.

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Like you're not really going to benefit

the return on investment, the ROI.

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Is not very high.

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Of course, there's things like AB

testing, hypothesis testing and

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regression that are going to be

useful for a lot of data analysts.

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But honestly, that stuff's

not super hard to learn.

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And the majority of the time,

like you're not doing the math,

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the computer's doing the math.

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So as long as you know what a hypothesis.

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test is and how to set it up and how

to interpret the results, you're good.

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And honestly, I think you can

learn that in one to two weeks.

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Lie number three is that you have to

know everything about data analytics

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in order to land a data jump.

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That you have to know Python, you

have to know Excel, you have to know

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SQL, you have to know Tableau, you

have to know Looker, you have to know

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Power BI, you have to know SAS, you

have to know R, you have to know Java.

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So on and so forth,

and it's just not true.

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Honestly, you don't have to even know

that much to be a data analyst, and

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maybe just one of those skills is enough.

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For example, I interviewed Matt Bratton

on my podcast a while ago, and he is

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like in the C suite of the data world,

and he basically only uses Excel.

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I've interviewed different

people on my podcast.

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And sometimes they only use

Tableau or they only use SQL.

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It really just depends.

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So sometimes you only have to know one

data skill throughout your whole career.

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Now saying your whole data career,

that's a little bit dramatic.

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Like you will probably use multiple

skills throughout your career.

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But when you land that first job,

like really a lot of the time, you're

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using one to two data tools, max.

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That being said, it's like, well, how do

I know which one to two that those are?

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And you really don't.

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And it's going to change from job to job.

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But here's what I will tell you

that Python is only required 30

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percent of the time for all data

analyst jobs from junior to senior.

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So personally, I don't really think

it's worth learning to be able to

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apply to those extra 30 percent of the

jobs when you're just getting started.

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I did an episode about this previously.

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You can see it right here and I'll

have a link to it in the show notes

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where I really don't think you should

start with Python or R to be honest.

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The lie is that you

have to know everything.

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And the truth is you don't,

you can get started today.

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And honestly, you can probably land

a job pretty soon with The skills

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you have already line number three

is that certifications matter.

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I don't care if it's the IBM certificate,

the power BI certificates, the

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Google data analytics certificate.

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The truth is for the majority of

data jobs, your cert does not matter.

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I know that might hurt to hear, and

you might not want to believe me, but I

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actually run my own job board, find a job.

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com.

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And I analyze the 2000 plus jobs that

I've posted on there the last four months.

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And not once did any of

the jobs posted on there.

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Ask for any sort of certificate.

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I know like the badges look cool

and like the certificate looks cool.

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The truth is no one really cares.

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At least employers don't really care.

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I have a lot of people who message

me and they'll say, Hey, Avery,

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I don't need your bootcamp.

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I'm already data analyst certified.

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And that is like the biggest

lie that you could ever say.

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And I understand that someone did.

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Certify you as a data analyst, but

there's nothing in the industry that's

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standardized that makes you data analyst

certified It's not like a nurse or a

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teacher where like you have a license.

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That's the wild west out here in the data

world We don't care about that stuff.

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So having a certificate.

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It's not a bad thing necessarily But it's

not like all that you might think it is.

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It's not your golden ticket into the

data world It takes a lot more than

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that and that leads me to my next lie

lie Number four is that skills are

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enough now you think that like If you

want to be data analyst, you have to

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learn these X amount of things, and then

you can become a data analyst, right?

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Wrong.

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Skills aren't enough when you're

trying to land a data analyst

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position for multiple reasons.

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One, as data analyst, like you're

actually not just spending your whole

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time using those technical skills.

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Like you're not just in Excel all day.

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One of the most important things you'll be

doing as a data analyst is communicating,

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is working with stakeholders.

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Is talking to teams and leaders and

understanding, you know, what the data

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is, where the data is at, what, how

you should analyze it, what's important

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for them to know, so on and so forth.

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But two, anytime you're trying to

land a data job, it's not the most

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skilled person who lands the job.

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Like think about it.

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I'm down here in my office.

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If I spent the next 240 years of my

life just studying data analytics,

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but I didn't have a resume, would

I land many data analytics jobs?

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Probably not because it takes

more than just your skills.

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There's a whole variety of things

that will actually help you get hired.

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I create a little mnemonic

for you to remember.

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It's called the SPN method, and

it's the easiest and fastest

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way to become a data analyst.

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S stands for skills, and that's

one third of the equation.

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But it's only one third of the equation.

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You need the P and the N.

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The P stands for projects or portfolios.

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And these are basically opportunities

for you to showcase your skills because

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anyone can say that they know SQL, but

you want to back that up with tangible

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evidence to a recruiter or hiring

manager via project on your portfolio.

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The N stands for networking and

really like 70 percent of jobs

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are done through networking.

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You're really getting

recruited or referred.

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And so there's a lot of different

ways you can network and a lot of

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different things that you can do to

increase your chance of getting hired.

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That is totally irrelevant and

not even related to your skills.

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There is no correlation to how skilled you

are, how quickly you land a data job, and

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how much you get paid as a data analyst.

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If you want to learn more about

the SVN method, I'll have a link

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in the show notes down below.

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Lie number five is that AI

is going to take your job.

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It's really interesting because

a lot of people are nervous about

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becoming a data analyst because they

don't feel like it's very AI proof.

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And one thing I've been

thinking to myself is Okay.

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Well, what careers are AI proof?

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In fact, I had one perspective student.

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He was messaging me and saying that his

friend was kind of making fun of him

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because like data analysts are going

to be replaced by AI and he had like a

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blue collar, more like mechanical job.

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And that was never going

to be replaced by AI.

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I think that's interesting because

like throughout history, haven't

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we seen like more of the mechanical

jobs being replaced by AI?

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previously.

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So like, I think those jobs aren't safe.

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And then I thought, oh, maybe

like a doctor that I was like,

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well, aren't like a bunch of like

robots doing surgeries nowadays.

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And like, can't you just kind of like

use web MD or whatever chat, GBT to like

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ask what's wrong and get a diagnosis.

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Obviously there's going to be some

jobs like nurses, for example,

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where I think that is basically

impossible to have a robot or AI do.

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But honestly, I've used AI

to try to analyze data and

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it's definitely not great.

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Another thing you should realize

is the difference between

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augmentation and automation using AI.

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Augmentation is almost like you can

think of it like putting on like the

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like glove in Iron Man or something?

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I don't know.

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I'm not good at Marvel, you guys.

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Uh, like, like the Infinity

Stones in that one movie, right?

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Like, that changes who you

can be and the powers that you

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have, but you're still yourself.

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And then the other one would be like,

no, I create a robot that's super

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powerful and it replaces me completely.

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And honestly, AI is going to augment you.

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That's for sure.

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It's going to change how work is done.

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But it's still you doing

the work a lot of the time.

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I've seen a lot of these companies

try to come out with like the auto

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analyzing data and it's not great so far.

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Is it going to get better in the future?

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Yes, definitely.

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But I definitely don't see

the human element getting

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taken out of it anytime soon.

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The ability to reason to actually find

like what's relevant to the business and

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then explain all that back to someone I

think is something that's very valuable.

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I'm a data analyst, right?

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I teach people how to

become data analysts.

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So my future is very heavily

tied in this and I honestly

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am not that worried about it.

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I think that, AI is going to

help us be better data analysts,

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and that's about the gist of it.

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So lie number five is that

AI is going to take your job.

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Lie number six is that you're

going to spend 80 percent of

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your time cleaning your data.

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I don't know where this came from, and

I don't know who made it, and I don't

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really know who propagates it further.

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Personally, in the roles that I've been

in, sure, data cleaning is important,

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and it does take a significant amount

of time, but it's nowhere close to 80%.

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Honestly, if you're spending 80 percent of

your time cleaning data, You're probably

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spending your time on the wrong things.

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I honestly think that like 80 percent

of your time should be spent talking

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to people as a data analyst before

you start a project, when you're in

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the project and after the project,

I think communication is actually

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way underplayed in the data world.

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But I don't know who's saying

that 80 percent of your time is

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cleaning data because that's.

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A huge exaggeration.

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Data lie number seven is all data

titles, uh, and I'm just so sorry

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for all you job seekers out there.

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This is the most frustrating thing

on planet earth, but once again,

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the data world is the wild wild west

and basically job titles are all

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kind of made up in the data world.

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There's kind of like the big three.

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There's the data engineer, the data

analyst, and the data scientist.

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But there's so many more positions in

between that overlap and that are the

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same and that are misclassified and

companies will call something, you know,

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a data analyst one place, but that's

really a data scientist other places.

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And it's really confusing.

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So all the data titles you're reading

on the job board are probably lies.

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And you should try to base it off of

what's like in the requirements section

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of the job description to actually

know what the job is going to entail

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and what the actual title kind of is.

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For instance, there's something

called a data science analyst.

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I don't know what the heck that is.

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I've even seen data analytics scientist.

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Technically, my role at Exxon for a long

time was optimization engineer, but I was

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really doing the work of a data scientist.

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And even at my first job, I was

technically a data analyst, but you could

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have also called me a chemometrician.

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There's so many different titles.

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They're so confusing.

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Honestly, I've CEO reach out to me

one time and ask me to look over.

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Their job description for

hiring their first data analyst.

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I looked it over and I was

like, this is a data scientist

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job, not a data analyst job.

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And he replied, well,

what's the difference.

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And this is like, not

a super small company.

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Like this is definitely a

company you've heard of before.

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I guess it was technically like

a general manager, not the CEO.

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It was like the president of a

local area anyways, but still

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like that is pretty crazy.

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Right.

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The people who are writing these job

descriptions maybe don't necessarily know.

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a hundred percent what

they're talking about.

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Lie number eight is that

there is lots of remote jobs.

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And now this one's super interesting

because anecdotally, it does feel

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like there is a lot of remote jobs.

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Most of my friends who work in

data have pretty flexible schedules

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and lives for the most part.

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And most of my students in my

program get pretty flexible jobs.

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But when I went and actually

did the research myself and I

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started web scraping job listings.

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I found that remote jobs only make

about 16 percent of all the jobs on

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the market, meaning the other jobs, the

remaining 85 percent ish are not remote.

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And obviously most of you guys watching

probably are interested in a remote job.

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So let's say that 95 percent of

people are interested in a remote job.

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That means there's a demand 95

percent for a low supply of 15 percent

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of jobs that are actually remote.

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And this is one of the reasons why the

job market is so crazy right now and

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really frustrating and it feels like

it's impossible to land a day job.

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The truth is there's just not as many

remote jobs as you may think there

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is, but there's actually equally

the same amount of hybrid jobs.

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So there's about 15 to 16 percent of

jobs in the market that are hybrid.

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And the cool thing about hybrid jobs

is it's on a spectrum of being in

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the office and working from home, and

every hybrid job is somewhere on that

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spectrum, but in different places.

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Some of my students work from the

office four times a week and then

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work remotely one day of the week.

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Sometimes it's reversed.

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Like for instance, some of my students

who work at Humana, they work from

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home four days a week and they

work in the office one day a week.

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I even have one student who is

hybrid, but she's only required to

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go in the office once a quarter.

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Now, to me that's more remote

than it is hybrid, but it

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was still labeled as hybrid.

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So I think the biggest play and

what you guys should be focusing

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on right now is hybrid jobs.

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Lie number nine is the self taught data

analyst or the self taught data scientist.

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So many people will say I'm self taught.

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And first off, what the

heck does that even mean?

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Like you're learning from somewhere.

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It's not like you just like went

out into your yard and like really

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thought hard and you're like, Oh, yes.

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What if I like Excel and Vlookups would

make a lot of sense in a pivot table?

309

: 00:13:38

Yes.

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: 00:13:39

Oh, and joins and SQL.

311

: 00:13:40

That makes a lot like you're not just

like divinely absorbing this knowledge.

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: 00:13:43

You're learning from somewhere,

whether it's a book, whether

313

: 00:13:46

it's online, so on and so forth.

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: 00:13:47

I think most people say self taught

because they maybe don't have a

315

: 00:13:51

formal degree or something like that.

316

: 00:13:53

I would consider myself.

317

: 00:13:54

Self taught, but I eventually

got a master's degree in

318

: 00:13:57

data analytics in college.

319

: 00:13:59

I took statistics classes that

got me really interested in data.

320

: 00:14:02

I had a really good

mentor at my first job.

321

: 00:14:04

He taught me a lot.

322

: 00:14:05

So I think the concept of, I want to be a

self taught data analyst is kind of silly.

323

: 00:14:10

It's also like, you don't get sent

a trophy for being a self taught

324

: 00:14:13

data analyst, like who cares

if you're self taught or not?

325

: 00:14:15

Like you don't get to wear like a badge.

326

: 00:14:17

It's like, Oh wow.

327

: 00:14:18

Like she's self taught.

328

: 00:14:19

He's self taught like

now, like it's okay to be.

329

: 00:14:22

You know, not self taught like

that's totally acceptable.

330

: 00:14:25

And honestly, maybe you should

wear that as a badge of honor.

331

: 00:14:28

It's like, no, I didn't do this

on my own because I knew I needed

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: 00:14:32

help or I wanted to do this faster.

333

: 00:14:33

So I sought help like there's

nothing wrong with that.

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: 00:14:36

That's plenty cool as doing it yourself.

335

: 00:14:38

So there you have it.

336

: 00:14:39

The nine biggest lies of becoming a

data analyst and landing a data job.

337

: 00:14:43

Are there any myths that I missed?

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: 00:14:44

Put them in the comments down below and

I'll try to respond to every comment.