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I wouldn't try to become a data analyst next here. Here's 4 reasons why and what I'd do instead.
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⌚ TIMESTAMPS
00:32 - Reason 1 not to be data scientist
03:22 - Reason 2 not to be data scientist
04:55 - Reason 3 not to be data scientist
07:33 - Reason 4 not to be data scientist
11:28 - What to do instead
🍿 OTHER EPISODES MENTIONED
Data Analyst Roadmap: https://datacareerpodcast.com/episode/136-how-i-would-become-a-data-analyst-in-2025-if-i-had-to-start-over-again
Get Paid to Learn Data: https://datacareerpodcast.com/episode/137-get-paid-1000s-to-master-data-analytics-skills-in-2025
Get You Master's Paid For (Thomas): https://datacareerpodcast.com/episode/128-meet-the-math-teacher-who-landed-a-data-job-in-60-days-thomas-gresco
Get You Master's Paid For (Rachael): https://datacareerpodcast.com/episode/125-how-she-landed-a-business-intelligence-analyst-job-in-less-than-100-days-w-rachael-finch
My review of Georgia Tech's Master's: https://datacareerpodcast.com/episode/38-masters-in-data-analytics-from-georgia-tech-is-it-worth-it
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Transcripts
If you're starting from absolute
scratch, I don't think you
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should be a data scientist.
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00:00:04,550 --> 00:00:05,660
At least not yet.
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And let me explain why not,
and what I would do instead.
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Now I wanna make it very clear.
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I don't think data science is dead at all.
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Like you might see a
lot of YouTubers saying.
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I don't think it's dead in the least.
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I freaking love data science and I
think it's gonna continue to thrive,
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but at the current moment, I do think
there's a better path for you to
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take if you ultimately wanna become
a data scientist down the road.
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So let's first get into the reasons why
not to be a data scientist right now.
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Reason number one is it takes a
long time to learn data science and
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ultimately become a data scientist.
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And basically, in order to be a data
scientist, you have to do two things.
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Number one, you have to know some math.
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And number two, you have to be able
to do that math with programming.
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And unfortunately.
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Just the way it is.
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Math is pretty hard to learn.
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It is not easy to learn.
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And knowing things like calculus,
linear algebra obviously are important
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to do things like machine learning.
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But even if you, let's just say you ignore
linear algebra and calculus because it
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makes your job as data scientist better.
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You're a better data scientist
if you know those things.
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But it's not a necessity.
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Like everyone makes it out to
seem , it's helpful, but it's not
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like you need a hundred percent to
understand those and know those.
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A million percent.
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Even just ignoring all of that,
the algorithms, the logic behind
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the algorithms is pretty tricky.
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And it takes like, uh, a lot of
patience and understanding and a
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lot of like math logic to get these
machine learning algorithms down.
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And as a data scientist, that's like
your number one job is to be, you know,
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using and creating these machine learning
algorithms to do data science stuff,
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to predict stuff, to classify stuff.
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So you're gonna be needing to know
mathematics, which just takes a
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while to, to learn, to be honest.
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And once you even figure out the math
and you can use the machine learning
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algorithms, you have to be able to
use them pretty much via programming.
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There are some data scientists
jobs out there that probably use.
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Less programming than you might think.
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They use tools that kind of do
the programming for them, but I
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think that's few and far between.
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I think that's where knowing like R or
Python comes in, those are pretty much
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the two programming languages that you're
going to be using as a data scientist.
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To do machine learning.
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And unfortunately,
programming is also hard.
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It takes a long time to learn.
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If you've never programmed at all
or you've only done a little bit of
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programming, it's a lot of effort to
learn programming from absolute scratch.
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You're gonna have to learn about
variables, you're gonna have to
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learn about functions, you're
gonna have to learn about loops.
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You're gonna have to learn about like.
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Classes and, uh, I'll have parameters
and arguments and a bunch of other things
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that I'm probably forgetting right now.
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The point is there's actually a lot to
learn when it comes to programming and it
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doesn't really come naturally to everyone.
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Uh, it is definitely a learned skill
that takes patients in years to master.
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Um, so if you're starting from absolute
scratch and you're like not a math
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whiz, and you're not like a programming
expert, going from zero to data scientist
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is gonna take a long time because
before you're gonna be qualified.
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To land data scientist roles,
you're gonna have to get Okay.
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At the math and okay.
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At programming.
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And don't get me wrong, I love math.
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I love programming.
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If you're loving it, then maybe
it is something to pursue.
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, But I think I still think there's
a better path to get there.
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We'll talk about that here in a second.
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Um, but just know that like it's really
hard to lend a data science job to even
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be qualified to land a data science job
right now because the amount of math and
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the amount of programming is quite high.
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The number two reason that you
maybe shouldn't be a data scientist
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right now is it honestly requires
a little bit of like bons.
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I don't even know if I'm using that
word correctly, but like you need
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some, not certifications, but honestly
it's seeming like when you're applying
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data scientist jobs, most of them
are saying master's in data science.
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Now I haven't actually analyzed that
statistically, , but that's just kinda
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what I've been seeing anecdotally.
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Um, and once again.
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Masters.
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It's gonna take a long time to get.
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Uh, the good news is you probably will
learn some of the programming, some of
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the math that I talked about earlier, but
it's just gonna take a freaking long time.
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We're talking like probably two years
to get a master's in data science.
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I guess maybe if you're doing it full
time, maybe it would only take one year.
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but one, it's just taking
a long time, right?
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We're talking years, not months here.
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And two, it's also gonna
be expensive, right?
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Because masters are not cheap.
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I think the cheapest that you
can get, like a master's in data
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science is like probably, yeah.
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$13,000, if I'm gonna be honest.
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I got my master's in data
analytics from Georgia Tech.
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I have a review on it if you
want to check it out sometime.
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It costs me about $13,000
and I think that's about the
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cheapest that you could go.
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So you.
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Kind of need a master's.
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If you're gonna try to be a data
scientist at, at least you have
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a lot better shot because it is
kind of listed as a requirement.
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I don't think it is a
requirement necessarily.
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I think you could land a data scientist
job without a master's degree, but I
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think it's honestly gonna be pretty hard.
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So unless you're wanting to spend like
two years, unless just say like, on
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average $20,000 in student loans maybe
you shouldn't be a data scientist.
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Reason number three that maybe you
should avoid the data scientist role
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right now is there's actually a lot
more openings in different data roles.
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And let me explain.
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So if you wanna do this experiment,
you can, you know, uh, I did
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this experiment and I'll tell
you the results here in a second.
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But there's actually more data
engineering jobs open right now
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than there are data scientist jobs.
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Um, not by a lot about.
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10% more.
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And there's actually double data
analyst jobs open than there are
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data scientist roles open right now.
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And that's in the US
and I, I used LinkedIn.
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You can go to LinkedIn and go
to the search bar and you can
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type in data analyst or data
scientist and I did United States.
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So it might be different if
you're in a different country.
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Um, and it shows you the number of
results and I think the results for.
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Data scientist was 8,000,
data engineer was 9,000 and
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data analyst was like 17,000.
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So data scientist has like the
lowest amount of openings right
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now, and I'll talk about why.
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I think that's the case here in a second.
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Data engineering has a little bit more.
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But there's also, a lot less
barrier to entry for data engineer
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and for a data analyst as well.
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Data engineering, it requires a lot of
programming and a lot of logic, a little
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bit less math, and you're not doing as
much like machine learning necessarily,
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but honestly, probably more programming.
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So if you are kind of a programmer,
maybe that's the route you wanna go.
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Because there is more job openings right
now and there's not master's degrees.
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Like there's not really a ton of data
engineering master's degrees out.
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This degree doesn't even really exist yet.
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So that's.
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That's nice, right?
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'cause when there's a degree that
doesn't exist, you don't have
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to have it to land the roles.
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And uh, I think data engineering is
kind of exploding with AI recently
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because AI really, at the core of
it, at, I guess at the beginning
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is a data engineering problem.
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'cause it's lot of data.
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You wanna basically feed these
models and it's a lot of unstructured
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data, so it's like, how do we best
structure the unstructured data?
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Um, so data engineering roles
are, are getting more popular.
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They don't require like a master's
degree, but there is a lot of programming.
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I don't wanna make it seem like
it is a lot, it is easy to land a
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data engineering job because it's.
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But I do think it is easier to
land a data engineering job right
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now than it is a data scientist.
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Easier than both of those, I
think is a data analyst role.
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One, there's a lot more
roles open right now, right?
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About double, , and two, like the
barrier to entry is so much lower.
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You don't have to know nearly as
much programming and you don't
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have to know nearly as much math.
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So you're able to land a
role a lot more quickly.
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And that's like huge.
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So spoiler alert, that is my
whole pitch to you is like, Hey.
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Put becoming a data scientist on the
shelf for just a little bit, become a data
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analyst first, and then pivot into that.
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We'll talk about that here in a second.
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the fourth reason you should maybe
consider not being a data scientist
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right now is I do think the data
scientist jobs are at least stagnant.
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I don't think they're down necessarily.
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But data science, It takes
longer to make a business impact.
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And what I mean by that is if you're
a business and you're looking to
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make money right now, you're looking
for profits today data science is
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much more of an investment than data
analytics and data engineering, data
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engineering is a really good investment
for companies right now because.
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You can't really do data analytics or data
science without good data engineering.
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You can't really do much with
data if you don't have good data
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engineering, because it's like how do
the data scientists access the data?
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How do they know that it's clean?
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So businesses investing in data
engineering, it makes a lot of sense
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right now because you can't even really
get much return on investment spent
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in data until you have data, right?
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And data engineering's all about
having data and storing it.
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Properly and effectively.
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Once you have that data stored right,
and, and everything's all set up,
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that's when you can start doing
data science or data analytics.
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I differentiate those between data
science is Looking more towards the
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future, like predicting stuff, right?
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And data analytics, looking more towards
the past and saying what happened.
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Um, so data analytics is like
more reporting, like this is what
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happened in the past type of thing.
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Data science being this is
what will happen in the future
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or, or predicting some sort of
behavior or something like that.
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It's just a lot easier
to do data analytics.
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It's like so much easier to do, uh, data
analytics than it is data science and
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you can get those results a lot quicker.
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Like I can make a report on what
happened in the past and probably what
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a fourth of the time is that's gonna
gonna take me to predict the future.
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And as someone who's been a data analyst.
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And a data scientist, I just know, doing
the data science work takes longer,
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'cause once again, it's more complex.
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You're doing more programming
you're, you're doing more math.
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It's a harder problem to solve.
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And so it just takes longer
for the businesses to actually
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see the fruits of their labor.
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Versus a data analyst, you
can almost see the results.
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Kind of immediately.
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So I, I think you'll have more
business impact as a data analyst
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'cause the results are very clear.
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And I think as a data scientist, I
think some of their, these long-term
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projects, a lot of these long-term
projects in data science fail too.
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Like, I worked as a data
scientist for ExxonMobil.
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Uh, I worked on let's see, I don't
know, like four big initiatives.
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And I would say half of them
probably failed, if I'm being honest.
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I don't know that for a fact.
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I kind of left before some of
those, Products were finished.
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One of them that was even
considered a success.
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Like it wasn't even really being
implemented or used I basically
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built that project in what,
2018 and when I left in 2021.
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It was like everyone really liked it.
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They're like, this is awesome.
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But I don't really think we had
very many users of the tool.
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So I don't know if you even count that
as success or not, but my point here
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is like looking at that, that was two
and a half years to even get to the
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point where, oh, we think this is gonna
be a success, but it hasn't been yet.
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So I hope that gives you a little
bit of an idea of how long it might
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take to actually impact the business.
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And I think when you're
impacting business one.
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You get promoted more often, you
get raises, those types of things,
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you get more clout, I guess.
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Uh, but two more roles of
those types of roles open up.
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And I just think that the, the return
on investment for data scientists
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right now might be a little bit fuzzy.
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Now, that's not true for every company.
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Like a lot of companies make their
money with data science work and
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they have already have a really good
data engineering infrastructure.
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That might be some of the bigger
tech companies that like are.
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Bajillionaire, right?
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Like those types of companies, they
still probably make their, their
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money with, with data science,
I think it's very valuable.
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, Like social media apps like Instagram,
Facebook, those types of things.
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But I think a lot of smaller operations,
they might be getting a little bit
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tight on data scientists because
the return on investment, it's high
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risk, high reward, I guess, uh, data
engineering and data analysts, it's,
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it's a lot more sure of an investment.
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So I think that might be one
of the reasons why you should
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actually consider these roles.
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I think you should become a data
analyst because like I said earlier.
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It's easy, it's easy to learn.
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There's a lower barrier to entry
and there's a lot more roles open.
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And my whole philosophy is I think you
should become a data scientist someday
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if you want to be a hundred percent.
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But the cool thing is this data
analyst role is kind of like a
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gateway role where the, the, the
fence is not hard to get over.
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The barrier is not hard.
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You can get in this data analyst role.
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Pretty easily.
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I have a whole roadmap on how to
actually become a data analyst.
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Um, you can watch that on YouTube, uh, up
here in the card, or if you're listening
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to the podcast via audio, I'll have it
a link in the show notes down below.
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That'll kind of explain everything
that you need to do step by step.
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But my whole point is like
you can become a data analyst.
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And then you can get paid to become
a data scientist down the road.
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Because that's, that's also true.
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Companies will pay you to learn.
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I actually have another video
about like my old philosophy of
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like how to get paid to learn.
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I'll have it right here on a YouTube
card or in the show notes down below.
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Um, but just like the
short of it is this, that.
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Once you're in a company, they're
going to invest in you to learn things.
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You're gonna have access to
like free LinkedIn learning.
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You're gonna have access
to like go to conferences.
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Uh, a lot of these companies will
even pay for a master's degree.
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I know I have some students in
my accelerator bootcamp who.
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I've worked with
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I actually interviewed them both,
so I'll pop them up in, in cards
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up here and have their, their
links in the show notes down below.
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Um, but one was a math teacher
and one was in quality assurance,
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and I helped them both pivot into
like more data analyst roles.
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And now their companies are
paying for them to go get a
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data science master's degree.
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And I think that's awesome
because now instead of.
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You know, going into debt $20,000
to become a data scientist,
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you already have a data job.
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You have income coming in, data income,
you know, data analysts get paid well.
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It's not like it's a, a crappy,
uh, salary and they can get paid to
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learn on the job and have school.
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Paid for by the company.
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So I think that is like a win, win win.
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I think that is the route
that you should take.
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So I'm not saying data science is dead.
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I'm not saying don't
become a data scientist.
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I'm just saying if you want to become
a data scientist, I think you should
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become a data analyst first, then learn
to become a data scientist on the job.
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You can learn Python on the job, you
can learn machine learning on the job.
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And that way you're
getting income coming in.
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While you're learning, because I can't
tell you how many people have come to me.
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Hey, Avery, I have a
master's in data science.
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I can't land a job.
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It's so much easier once you
already have some sort of a data
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job and a current data job too.
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You can watch this video if you're
watching on YouTube next, that
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will help you learn to get started.
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Check out the show notes if
you're listening on the podcast.
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Thank you guys for listening, and
uh, I'll see you in the next episode.
