226: 10 Things I Wish I Knew When Starting as a Data Analyst

Speaker: 00:00:00

These are the 10 things I wish I knew when

I was just getting into data analytics,

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having been a data analyst for 10 years

now and helped thousands of people

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transition into a data analyst role.

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Number one is there is lots of data

titles that aren't just data analyst.

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A lot of the times we're like, "Oh, I

wanna become a data analyst," but we

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don't realize that financial analyst,

business analyst, healthcare analyst,

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operations analyst, data visualization

specialist, data visualization engineer,

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business intelligence engineer, business

intelligence analyst, that all of

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these are really just the same job

resp- Description and requirements and

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responsibilities with a different fancy

title based off of what industry you're

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in and maybe what company you work for.

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A lot of these titles do the exact same

thing with just a different industry

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or maybe with a different tool.

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And really, like, you would be…

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If you would like a data

analyst job, you'd be stoked

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with any of these jobs as well.

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So don't just pigeonsh- hole

yourself into only looking at

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data analyst jobs exclusively.

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There's so many other titles than

just data analyst, and I did that at

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the beginning, and I really regretted

that, and it's really just because

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no one ever told me that there was

more roles than just data analyst.

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Number two: You don't need to

learn every single data tool,

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and there's so many out there.

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There's, like, literally thousands

that you could possibly learn, whether

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it's, you know, the ones you've heard

of, Excel, SQL, Python, Power BI,

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Tableau, R, AWS, you know, and then

there's SAS, and then there's JMP,

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and then there's Qlik, Qlik, and then

there's Google Data Studio, and there's

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Looker, and there's so many different

tools that you could be learning, guys.

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You know, as a beginner,

you're, like, overwhelmed 'cause

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you're like, "I know nothing.

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I don't even know what

half of those things are."

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In fact, those might just be

Pokémon he just listed, not

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even real data analyst tools.

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Those are all real data analyst

tools, just for the record.

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But my point here is there's so many

different tools, and it's gonna take

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you so long to learn all of them

that you're just gonna feel really

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discouraged if you try to learn them all.

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And so my advice is don't learn them all.

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Learn the lowest hanging fruits,

the ones that are the easiest to

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learn, that are most in demand, and

it ends up being Excel, SQL, and a

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BI tool like Tableau or Power BI.

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I have a whole chart that I've

actually shared with my newsletter

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before about the most in-demand

jobs and how easy they are to learn

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that I've sent out in my newsletter.

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So if you're not subscribed, make sure

you're subscribed to the newsletter

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at datacareerjumpstart.com/newsletter.

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It's absolutely free.

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I send a new episode

every single Wednesday.

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Okay.

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Number three: Your domain knowledge

really matters, and whatever

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career you've had in the past or

whatever you studied in college is

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probably useful in the data world.

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Like, you might be an education teacher

and you're like, "Oh, like, all of

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this, you know, studying and all this

previous work was an absolute waste."

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That's just not the case.

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Like, your domain knowledge is

really useful and really powerful,

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and if you combine your domain plus

data, you're gonna be a superhero.

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You're gonna be, like, deadly analyst.

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Like, you're gonna be able to

analyze things that most data

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analysts wouldn't be able to do.

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It's just because you understand the

domain and you understand the know-

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the business knowledge and the industry

more than just, like, some random data

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analyst would, and that sets you apart,

and it gives you a really big advantage.

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When I was a data scientist at

ExxonMobil, I was not the best data

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scientist at the company at all.

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There was people with PhDs in

computer science, PhDs in mathematics,

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and they could out-theory me.

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They could out-code me.

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They could out-data me in

so many different ways.

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But with my chemical background, I

was pretty good at analyzing chemistry

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data 'cause I, you know, had studied

it for four years in college, and

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I knew it like the back of my hand.

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And so I knew things automatically.

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I could see things in the data that

that would take them, you know-

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days, weeks, months to realize.

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So your domain is your superpower,

it's not your weakness.

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Number four, data analytics

is just not head down coding,

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head down technical analysis.

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It's actually very, uh, collaborative.

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There's actually very, like, you have

to talk to people, you have to get

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business requirements, you have to

think about what you're actually doing.

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It's not like you're just, you

know, at your desk all day,

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"Boo, I'm analyzing data."

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It's, it's a lot more

social than that actually.

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You need to talk to stakeholders on

the front end and on the back end and

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in the middle to make sure that you're

actually solving the question that

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they are trying to get answers to.

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Because we're not analyzing

data for funsies out here, guys.

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It's not just like, "Oh yeah, let's make

a chart," 'cause we wanna make a chart.

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Like, charts are cool, but none of us

wanna be, like, making charts all day.

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We wanna make charts so that we can

understand what's going on in our

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business, so we can understand the

swarm and sea of numbers in a manageable

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human way, uh, via data visualization.

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And so you really need to

be, you know, like this.

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And if you're listening to the

audio version, I'm, like, doing

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something weird with my fingers.

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We're, like, really close to each

other with your stakeholders so

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that you are actually answering

business questions for them and

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helping the business move forward.

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Number five, it's just that

SQL's really important, you guys.

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When I first got into data analytics,

I thought Python was everything.

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Everyone's like, "Oh,

Python, it's so cool.

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Python, it's like the new tool.

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Everyone's using Python."

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And Python's great, and I love Python.

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But just know that SQL

is really important.

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If you've never heard of SQL before, it

stands for structured query language.

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And basically It's the most

used data tool on planet Earth.

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Now, if you read my

newsletter, you know that I…

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50% of data analyst jobs require Excel,

and that's more than that require Sequel.

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So why am I saying it's more important?

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Well, it's because data scientists and

data engineers use Sequel a whole heck

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of a lot more than they use Excel.

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So in the grand scheme of things,

in the big data career world,

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looking at data analysts, data

scientists, and data engineering,

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Sequel is the number one tool.

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In data analytics, it's just Excel,

but then Sequel is number two.

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So I just wanna emphasize

how important Sequel is.

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At the beginning of my career, I didn't

really realize how important it is.

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Um, I kind of just ignored it.

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In fact, I went through my whole

first data job without ever using

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Sequel, and that is, like, a

little bit embarrassing to mention.

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But it's also important to realize

that some jobs don't require Sequel.

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But I wish I would've used Sequel

at that job because it would've just

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managed our data better, faster.

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It's just the best way to

organize and query your data.

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All right, number six, and that

is that your personal brand

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and networking really matter.

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When you're trying to land a job,

either your first data job or your

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second data job or your next data

job, like, having a personal brand

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and networking really matters because

you're just gonna have every advantage.

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Especially now where the applicant

tracking systems have so many different

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applicants, it's really hard to stand out.

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And so if you actually have a human-human

interaction or if someone knows your name,

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if they know your face, you're so much

more likely to get the things in this

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world that you want than if they don't.

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So my recommendation is to start

building your personal brand

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and start building your network,

even if you don't need it today.

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If you're like, "Ah, that seems useless.

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That seems like a lot of work.

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It seems like being awkward and

putting myself in difficult situations.

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I'll wait till I actually need it," if

you wait until you actually need it,

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you've waited too long and it's too late.

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So you need to start building it today.

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So one really easy way to start building

it is to just update your LinkedIn,

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make sure it reflects everything that's

going on in your life right now, and to

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start leaving comments on LinkedIn posts

and, if you're feeling really brave, to

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actually start making posts on LinkedIn.

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That's what we do with all

of my bootcamp students.

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And it's awkward, it's confusing, it

feels weird, but I promise it's worth

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it in the end, and it will give you

so much an advantage in your career.

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At this point, I have the

best job on planet Earth.

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I'm just a data career mentor.

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I help my students land

their first data job.

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But let's just say that all of

that burned to the ground tomorrow.

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I feel pretty confident I could

get a data job pretty quickly, um,

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because of the network I've grown.

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And you're like, "Oh yeah, Avery,

well, you're a YouTuber, uh,

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70,000 subscribers, and you have

LinkedIn followers, like 150,000."

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Well, yeah, but at one point, in

fact, five years ago, I had zero.

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I had none of that.

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And so yes, little things have

built up over the last five years.

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But you don't have to build a YouTube

channel to 70,000 subscribers.

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You don't have to build your

LinkedIn following to 150,000.

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Like, just get double the connections

you have on LinkedIn right now or

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just, you know, make one LinkedIn post.

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You can start small.

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You don't have to start big.

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All right, number seven.

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This is something that I didn't think

I realized, and I don't think most

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people who are getting into data

realize, and that is that you're going

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to be learning on the job Constantly.

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Data is constantly changing.

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There's constant updates, and you

need to be learning on the job.

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It's not like accounting,

where it's like…

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I guess accounting just

took a stray, I guess.

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But I guess they do learn

new things 'cause there's,

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like, new tax codes and stuff.

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But it's like the P&L has been the P&L,

the same P&L for how many years now?

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It's like, it's like a very

regimented way of doing things.

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In data analytics, like, it's

just constantly changing.

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There's constantly new tools.

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There's constantly new

ways to analyze things.

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There's constant breakthroughs,

new technologies, and it's just,

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like, impossible to have known it

all 'cause it literally changes

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probably every other year.

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So just know that you're gonna be

learning on the job, and that's 100% okay.

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That's 100% expected.

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A lot of jobs, in fact, every job I've

ever had, has given me the opportunity

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to learn on the job and given me

time to actually get paid to learn.

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I think that is the best way to learn

data analytics, is to get paid to learn.

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You can learn for free

or you can pay to learn.

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The best is to get paid to learn, and you

might need to learn for free or pay to

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learn to eventually get to that stage.

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But the faster you get to that

stage, the, the easier, the more time

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you're gonna have to learn, and the

better learning it's going to be, and

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you're making money while doing it.

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So that seems like a win-win-win

to me, but just know that

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you will learn on the job.

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No matter who you are, no matter where

you're from, no matter what the job

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is, you will be learning on the job.

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It's just, that's just the

data world that we live in.

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Number eight, being a data analyst

is more collaborative and more of

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a team effort than you realize.

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Uh, when I worked at ExxonMobil, I

almost exclusively worked in pairs.

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Like- I would always do my

analysis with someone else there,

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and we'd kind of do it together.

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Because a lot of it is actually thinking.

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Especially now with AI, the actual

doing isn't ne- necessarily as

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important as it has been historically.

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Um, but, like, actually thinking

through, are we accessing the right data?

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Are we doing the right metric?

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Are we presenting the

data in the right way?

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So I actually did most of my

analysis with, uh, another,

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another data person at Exxon.

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But then also go back to what I said, uh,

earlier, which I think was number four,

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where you're talking to the stakeholders

constantly, at the beginning and at the

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end especially, but also in the middle.

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So, like, you will be analyzing data

on your own, but you'll be presenting

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that constantly to someone else.

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Um, I remember when I worked at a

really small biotech startup, go

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make my graph, show it to my boss.

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"What do you think?

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Change this, change this, change this."

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Um, so it is, like, a very

collaborative team effort.

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It's not as solo as you

probably think it is.

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That being said, there are some roles that

are going to be a little bit more solo.

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But from my experience and a lot of my

students' experimen- uh, experience, it is

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kind of like a team collaborative effort.

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Number nine, landing a remote job is a

lot harder than you think, and I just

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hate to be the bearer of bad news.

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I would love to be the person, you know,

in the podcast world, if you're listening

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on audio, or in the YouTube world, if

you're watching on video, who makes,

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like, a cool clickbait thumbnail, and

I've made clickbait thumbnails before.

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I'm not saying I don't

make clickbait thumbnails.

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But I'd love to make a really

cool, uh, YouTube thumbnail where

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it's like, "Get a remote data job.

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Woo-hoo.

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It's easy.

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It's so much fun."

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But here's the harsh truth that, like,

all the data jobs out there in the United

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States, probably about 14% are remote.

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That means there's, what, 86% that

are either hybrid or in person.

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And let me know if I'm wrong in the

comments on Spotify or on YouTube.

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Tell me if you want a remote job or not.

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In the comments say, "I want a remote,"

or you say, "I want it in person."

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And, uh, there's gonna be a lot…

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Prove me wrong, but there's gonna be a

lot more people who want to work remotely.

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Everyone wants to work remotely,

but there's only 14% of

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opportunities to work remotely.

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It makes it hard to land a remote job.

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Now, let me also tell you,

when you have a remote job,

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there's lots of pros, obviously.

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Like, we all- I love working from home.

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It's great.

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But there's some cons that you're

probably not thinking of, and

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one of them is getting training.

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It's a lot harder to train

people via, like, Zoom.

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And two is career growth.

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I think, once again, if we go

back to, what number was it?

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The networking one where

I men- mentioned earlier.

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Oh, yeah, personal brand and

networking really matter.

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It is easier to have a personal brand

and network In your company, when

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you're in the office in person and

people know your face, they shake your

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hand, they get to hear your jokes,

your career will grow more if you are

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in the office than if you are remote.

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That's just the trade-off.

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And if you're, if you're like, "Okay, I

don't really care about career growth,

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I just don't wanna commute," great.

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That's fine.

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But I just wanna let you know that remote

isn't as cool as you maybe think it is,

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or everyone hypes it up on the internet.

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And it's hard to get.

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I just wanna be realistic with you.

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I, I would love to tell you it's

easy and it's awesome, but it's

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hard, and there's some downsides.

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All right, number 10, it's that

the imposter syndrome that you're

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feeling right now as an aspiring data

analyst never freaking goes away.

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It never does.

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It is so hard to actually feel like

you know anything in the data fields

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because one, it's constantly changing,

two, it's immensely vast, uh, and it's,

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like, impossible to know everything.

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So that feeling you have right now that

you're not good enough, that you don't

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know everything you should, that you

don't know everything in Excel, that

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you've never even touched Python, that

you kinda suck at SQL, guess what?

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That never goes away.

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That's just there the rest of your career.

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And the more…

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The earlier you become comfortable living

in the idea of, "I don't know this, but

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I know I can learn this," the better.

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Because the data world, everything's

changing literally constantly,

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and you will always be learning,

and you'll never know it all.

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And so the fact that you can just own up

to it and be like, "Yeah, I don't know

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this, I don't know that," that's okay.

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If I need to know that,

I will in the future.

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If you can do those things, you

will be a great data analyst

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