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There's a lot of advice online on breaking into data. Here's what I'd tell a beginner.
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⌚ TIMESTAMPS
00:00 – The Reddit post
01:09 – Companies interested in you
04:12 – Analysis is about money
07:00 – Bad tool advice
09:42 – My advice
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Transcripts
The data analyst role is changing,
and it can be a little bit scary,
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:especially if you're thinking about
someone breaking into the field.
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:It honestly feels pretty daunting
and honestly probably impossible.
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:But it's not impossible, and in
today's episode, I wanna break down
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:what people are saying online, what
people are saying on Reddit on how
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:the data analyst role is changing
and what you should do because of it.
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:So recently on the subreddit
DataAnalyticsCareers, someone posted this,
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:and I want to actually react and see if
this person's giving sound advice or not.
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:So let's go ahead and get into it
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:So the first thing that they say is
to start top-down, not bottom-up.
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:Most people start with tools.
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:They open up a SQL course,
memorize syntax, and then
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:wonder what to do with it.
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:That is backwards.
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:Before you learn a single tool, research
requirements and responsibilities
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:of data analyst positions in
companies you want to work at.
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:List the skills they ask for, list
the responsibilities, and then try to
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:understand what the job is actually for.
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:Ask yourself which industries
actually industry interest me,
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:what does a day-to-day look like in
the companies that I am targeting?
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:What problems does the analyst
solve for the business?
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:What is the goal of the role?
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:This will give you a global
view and the idea about the job.
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:This is more for the private sector
companies, but in the public sector,
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:some things will still be applicable.
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:Okay.
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:I totally agree with the idea of we're
not doing data analytics to analyze data,
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:and you shouldn't just learn data skills
and data tools to learn data tools.
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:Like, they should always be used to solve
business problems in some sort of a way.
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:So we don't wanna just like
memorize syntax, we don't
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:wanna just write SQL queries.
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:We're always writing SQL queries
to solve business problems.
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:And the quicker you get that
in your mind, the better.
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:So I agree with that.
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:I do think it is a little interesting
here that they're saying, "Hey,
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:go look at the companies you're
actually interested in, and list…
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:look at what skills they're requiring."
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:And I think that's important to like
actually look at job descriptions
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:and see what is actually being in
demand, 'cause you might think, "Oh,
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:Python's really important to learn."
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:Guys, it's really not.
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:Uh, like I think it's only like 80% of
data analyst jobs don't require Python.
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:So if you're gonna spend so much time
learning Python for only apply for those
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:20% of the jobs, it might not be worth it,
and that's my, you know, my take on it.
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:I've done a bunch of episodes
in the past about what skills
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:you should actually learn.
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:You know, you can go to finddatajob.com
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:and click on our s- uh, skills
report to actually see what
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:skills are in demand right now.
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:But I think that's important to do,
but I wouldn't necessarily just do it
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:for the companies you're interested in,
because that might change over time,
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:and also it's like a small subset.
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:Like let's say you really wanna
work at Meta, and their data
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:analysts might use Python.
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:I don't know.
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:If you're really set on working for Meta
or a certain company, that makes sense.
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:But if you're open to like working
for any company really, like I
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:think instead of just looking at a
few companies you're interested in,
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:you should look at the aggregate.
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:And so I think that's why our, um,
skills report at finddatajob.com
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:is really useful, 'cause that's like on
average what skills should you actually
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:learn, and the answer is Excel, SQL, and
one BI tool like Power BI or Tableau.
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:That's basically the, the short
answer, but you can go check it out,
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:the long answer, on our website.
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:Also, like what industries actually
interest me, I think that's one way
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:to look at it, but also I would say
what industries are interested in me.
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:Because it's like a lot of us, I mean
not a lot of us, but me for example,
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:and I know I've talked to a lot of you
guys, are interested in sports analytics.
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:Like, "Oh, I love sports.
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:It'd be so fun to be, you know, one
of these analytics guys for the NBA or
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:for the NFL or something like that."
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:And that would, would be fun, but the
truth is, those positions are so rare
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:and they're so in demand that they're
impossible to land, and they don't pay
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:particularly well most of the time.
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:So it's like, yes, you would be interested
in those jobs, but it's probably better
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:to reverse it and think, "Well, what
industries would be interested in me?"
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:And usually it's what
industry you're coming from.
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:Like what's your domain and what's
your degree, and those types of things.
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:So when I was a chemical lab technician,
it's like, oh yeah, I could have
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:tried to land a sports analytics
jobs and that would've been great.
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:You know, maybe their job description say
you have to learn R, and I would've spent
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:all this time learning R, but the truth is
they would probably never be interested in
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:me, and so I just wasted a bunch of time.
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:And so I think it's actually better to
reverse this and start with the, you know,
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:what companies and what industries are
interested in me, and then try to focus
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:on that, unless you're really set on,
like, a specific j- company or industry.
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:But I think most of us
aren't really that case.
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:Also, this is more true for the private
sectors versus the public sector.
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:I don't know what they're saying there.
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:Like, I don't feel like there's that
big of a difference between the two.
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:Uh, like they both post jobs online.
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:Like, I don't get why making the company
public or private, that actually changes.
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:But I don't know.
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:So overall, I'd give this b-
advice maybe like a B plus.
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:I think for the most part it was
right on, but there were some small
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:things I would make some changes on.
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:Let's move on to their next paragraph.
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:"Learn how businesses make money before
you learn how to query a database.
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:This is the whole thing nobody teaches,
and it is the thing that separates
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:analysts who get promoted from analysts
who stay stuck executing requests."
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:We have that classic AI line right
there, right, with the double dash.
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:I forget…
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:Em dash is what that's called.
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:Also, I thought this was for people
who are pivoting in and not for
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:people who are getting promoted.
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:But okay, I'm gonna…
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:I'm getting distracted here.
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:"Every business runs on the
same fundamental loop: attract
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:customers, convert them, deliver
value, retain them, grow revenue.
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:The department you work for
determines which part of the loop
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:you spend most of your time on.
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:Your analysis will always serve
one or many of these stages.
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:Once you understand that, you
can think about data as numbers.
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:You can stop thinking about data
as numbers and start thinking
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:about as evidence for decisions."
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:I do like that line.
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:That is a great line.
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:"Four questions to ask
before any piece of analysis.
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:What is the business
actually trying to achieve?
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:The real goal, not the metric.
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:Who will use the analysis?
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:A CEO and product manager need completely
different things from the same data.
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:What decision does this
analysis need to support?
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:Analysis without a decision
attached is just exploration.
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:What action will they take
after seeing these numbers?
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:If the answer cannot change anything,
the question is not worth asking.
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:Build this thinking into every project
from day one, and you'll be ahead of
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:most junior analysts within six months."
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:I think this is pretty solid advice,
especially those four questions that they
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:listed there, like what are we actually
trying to solve with this analysis?
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:Who is this analysis for?
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:How much level of detail do
we need to have in there?
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:I think that makes a lot of sense,
and he or she is right that the
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:quicker you understand that,
the better analyst you will be.
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:I think trying to study how businesses
make money is an interesting concept.
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:I don't necessarily think every
business runs on the same fundamental
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:loop of attract customers, convert
them, deliver value, retain them, grow
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:revenue, or at least those stages are
very convoluted, and I think that's an
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:oversimplification of how businesses work.
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:Like, yeah, like for instance, if
you do like a D2C, direct to consumer
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:brand, and like I'm selling, you know,
water bottles, like that makes sense.
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:But like I've worked for companies who
don't care about growing their revenue.
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:They just want to be acquired someday,
and that's how they're going to make
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:money is eventually via an acquisition.
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:And so they're not concerned
even about revenue.
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:Like that, that's a real thing,
and that's, you know, there's
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:lots of companies like that.
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:Also, like I worked for ExxonMobil, like
one of the biggest oil, uh, producers or
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:gasoline producers in the world, and In
terms of like, you know, this method that
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:they're talking about here, I don't know.
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:I was so far away from that.
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:Like, I was in research and development.
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:Like, I guess research and development
maybe would be like in deliver
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:value stage, but I think this is an
oversimplification of how business works.
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:But it is important to remember
that we're only doing analytics
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:to help the business usually make
money, and I hate to say that.
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:Like, it could be to save lives, it could
be to help people improve their lives,
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:but most of the time it is to make money.
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:And so you always need to tie your
analysis back to a dollar bill.
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:That's really important, and the
quicker you figure that out, the better.
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:So if you're trying to land a data job
and, you know, you're unable to tie
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:what you've done historically to dollar
bills, that is difficult to perform well.
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:So the quicker you can kind of
bridge that gap, the better.
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:Moving on to point number
three: learn tools to answer
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:questions, not to memorize syntax.
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:Once you understand the business
context and the types of questions
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:you'll be asked, tools become obvious.
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:I don't think there's any way for you
guys to be able to predict what, what
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:questions you'll be asked, unless,
unless from the job description.
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:Sometimes from a job description, you
can actually pull out what type of
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:questions you would be asked, but I
would say that every job description and
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:every role is so unique that, like, you
can't just b- be like, "Oh, these are
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:the type of questions I'll be asked,"
you know, different types of roles.
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:Like, it's so different
every place you go.
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:That being said, like, there
are some analyses that are very
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:common depending on where you go.
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:So like, for example, if you're gonna
work for Google or you're gonna work for
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:Amazon or you're gonna work for Meta, like
one of the things they're always thinking
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:about is AB testing or hypothesis testing.
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:Like, if we change this on our
website, do we get better results?
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:Yes or no?
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:If you work for one of those
companies, you will probably face
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:some sort of question like that.
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:So there are some generic questions
like that, but every other role
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:I've worked at has been so unique.
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:It's kind of hard to predict
just off of a job description.
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:Okay, you learn SQL because you
need to query customer data.
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:You learn Python because you need to
clean a dataset or automate a report.
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:Uh, okay, this is dumb.
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:Like, no.
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:I could clean a dataset and create
an automated report in Excel.
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:I could cr- clean a
dataset and do it in SQL.
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:Like, okay, I don't think
that's sound advice.
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:Like, you learn Power BI because a
stakeholder needs to interact with
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:a trend, not just read a table.
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:Once again, I could do that
in Python, so I don't know.
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:"Learning a tool without a question
to answer is why most people plateau.
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:The syntax does not stick because
there is nothing to attach to it."
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:I don't know, that sounded
like an AI-generated sentence.
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:"Use AI and Kaggle to find datasets
that match your target industry."
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:I like that advice.
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:It's always good to find unique datasets.
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:"Build projects around
real business questions."
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:I like that advice.
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:"What is driving churn in a subscription
business is a better starting place
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:than let me practice SQL joins."
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:That is probably true.
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:Like, I think that is sound advice
that, like, if you can actually do
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:real-world projects with real-world
data, that's, that's better than just
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:doing pointless exercises online.
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:That's one of the reasons we created
the accelerator, and we do the
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:different projects that we do with
real-world datasets to kind of get
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:you in the habit of doing this.
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:All right, next point here.
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:"Talk to people already doing the job.
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:There is more useful information in
one honest conversation with a working
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:analyst than in 10 hours of tutorials."
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:Eh, debatable.
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:"Threads, LinkedIn, Reddit, you
will find those willing to help
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:you if you ask a specific question
rather than a generic one."
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:I mean, that is true.
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:Like, the more specific
you can ask, the better.
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:Like, the more specific
advice you're going to get.
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:Like, the more context you
give someone, the better.
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:I can't tell you how many, like,
"How should I break into data?"
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:questions I get, and it's
like, "Okay, well, who are you?
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:What do you do?
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:What do you want to do?"
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:and those types of things.
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:So the more specific you are, the better.
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:"Prepare in advance, though."
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:And, uh, that's the whole post, and that
got 500 upvotes on, uh, this subreddit.
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:I mean, I think it's sound advice,
but it's not really even that amazing.
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:It's like, okay, you need to
make sure that you're tying
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:your analysis to dollar bills.
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:Okay, I agree.
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:And do real-world projects
with real-world data.
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:I mean, that's sound advice.
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:Like, you can't go wrong with that advice.
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:But what does that actually mean to you
guys who are trying to land a data job?
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:Like, you guys aren't
even in the role yet.
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:Like, how is this
actually going to change?
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:And one important thing I think
this person's really missing out on
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:is why this is actually changing,
and the truth is it's 'cause of AI.
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:AI is changing so many different
things, the way we analyze data,
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:the way we interact with data.
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:And I still think it's really important
to learn the fundamentals first because
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:otherwise I think it's going to haunt you
down the road where you can't actually
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:fix things on your own if AI gets
really expensive, or you can't actually
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:catch, like, any errors that AI makes,
'cause AI does make errors all the time.
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:So I think it's first important to
learn the fundamentals of the business
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:and actually, you know, like, okay,
how does this business operate?
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:How does data support business?
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:That's great.
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:Then make sure you know the fundamentals
of data, like columns, rows, how we
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:actually analyze data, when to use
a line chart versus a bar chart, why
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:we don't really like pie charts in
the data visualization community.
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:The basics of all things
data fundamentals.
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:And then I think it's really important
on top of that to learn how to do things
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:in AI, because I do think the future is
very AI-focused, and that's one of the
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:things that I'm trying to talk a lot about
on this episode and on this platform.
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:So if you guys want to learn
more, I try to talk about AI every
263
:single week in my newsletter.
264
:You can go to
datacareerjumpstart.com/newsletter
265
:and subscribe for absolutely free.
