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These are the 10 things I wish I knew when I was starting out in data analytics.

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⌚ TIMESTAMPS

00:00 – The title trap

01:00 – Lowest hanging fruit

02:09 – Domain is your superpower

03:15 – Stakeholders come first

04:18 – Why SQL wins

05:27 – Build it before you need it

07:06 – Getting paid to learn

08:24 – Nobody analyzes alone

09:33 – The remote reality check

12:18 – Imposter syndrome is normal

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These are the 10 things I wish I knew when

I was just getting into data analytics,

2

having been a data analyst for 10 years

now and helped thousands of people

3

transition into a data analyst role.

4

Number one is there is lots of data

titles that aren't just data analyst.

5

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

10

responsibilities with a different fancy

title based off of what industry you're

11

in and maybe what company you work for.

12

A lot of these titles do the exact same

thing with just a different industry

13

or maybe with a different tool.

14

And really, like, you would be…

15

If you would like a data

analyst job, you'd be stoked

16

with any of these jobs as well.

17

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.

22

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,

26

Tableau, R, AWS, you know, and then

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

27

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."

32

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.

47

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

54

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

96

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.

111

So why am I saying it's more important?

112

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.

114

So in the grand scheme of things,

in the big data career world,

115

looking at data analysts, data

scientists, and data engineering,

116

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.

120

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.

127

All right, number six, and that

is that your personal brand

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and networking really matter.

129

When you're trying to land a job,

either your first data job or your

130

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.

132

Especially now where the applicant

tracking systems have so many different

133

applicants, it's really hard to stand out.

134

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

148

actually start making posts on LinkedIn.

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That's what we do with all

of my bootcamp students.

150

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.

152

At this point, I have the

best job on planet Earth.

153

I'm just a data career mentor.

154

I help my students land

their first data job.

155

But let's just say that all of

that burned to the ground tomorrow.

156

I feel pretty confident I could

get a data job pretty quickly, um,

157

because of the network I've grown.

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

well, you're a YouTuber, uh,

159

70,000 subscribers, and you have

LinkedIn followers, like 150,000."

160

Well, yeah, but at one point, in

fact, five years ago, I had zero.

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I had none of that.

162

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.

164

You don't have to build your

LinkedIn following to 150,000.

165

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

171

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.

173

Data is constantly changing.

174

There's constant updates, and you

need to be learning on the job.

175

It's not like accounting,

where it's like…

176

I guess accounting just

took a stray, I guess.

177

But I guess they do learn

new things 'cause there's,

178

like, new tax codes and stuff.

179

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

the same P&L for how many years now?

180

It's like, it's like a very

regimented way of doing things.

181

In data analytics, like, it's

just constantly changing.

182

There's constantly new tools.

183

There's constantly new

ways to analyze things.

184

There's constant breakthroughs,

new technologies, and it's just,

185

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.

189

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

ever had, has given me the opportunity

190

to learn on the job and given me

time to actually get paid to learn.

191

I think that is the best way to learn

data analytics, is to get paid to learn.

192

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

194

learn to eventually get to that stage.

195

But the faster you get to that

stage, the, the easier, the more time

196

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.

198

So that seems like a win-win-win

to me, but just know that

199

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.

202

It's just, that's just the

data world that we live in.

203

Number eight, being a data analyst

is more collaborative and more of

204

a team effort than you realize.

205

Uh, when I worked at ExxonMobil, I

almost exclusively worked in pairs.

206

Like- I would always do my

analysis with someone else there,

207

and we'd kind of do it together.

208

Because a lot of it is actually thinking.

209

Especially now with AI, the actual

doing isn't ne- necessarily as

210

important as it has been historically.

211

Um, but, like, actually thinking

through, are we accessing the right data?

212

Are we doing the right metric?

213

Are we presenting the

data in the right way?

214

So I actually did most of my

analysis with, uh, another,

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another data person at Exxon.

216

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

earlier, which I think was number four,

217

where you're talking to the stakeholders

constantly, at the beginning and at the

218

end especially, but also in the middle.

219

So, like, you will be analyzing data

on your own, but you'll be presenting

220

that constantly to someone else.

221

Um, I remember when I worked at a

really small biotech startup, go

222

make my graph, show it to my boss.

223

"What do you think?

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Change this, change this, change this."

225

Um, so it is, like, a very

collaborative team effort.

226

It's not as solo as you

probably think it is.

227

That being said, there are some roles that

are going to be a little bit more solo.

228

But from my experience and a lot of my

students' experimen- uh, experience, it is

229

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

231

hate to be the bearer of bad news.

232

I would love to be the person, you know,

in the podcast world, if you're listening

233

on audio, or in the YouTube world, if

you're watching on video, who makes,

234

like, a cool clickbait thumbnail, and

I've made clickbait thumbnails before.

235

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.

239

It's easy.

240

It's so much fun."

241

But here's the harsh truth that, like,

all the data jobs out there in the United

242

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.

245

Tell me if you want a remote job or not.

246

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

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

247

And, uh, there's gonna be a lot…

248

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.

251

It makes it hard to land a remote job.

252

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.

258

It's a lot harder to train

people via, like, Zoom.

259

And two is career growth.

260

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.

262

Oh, yeah, personal brand and

networking really matter.

263

It is easier to have a personal brand

and network In your company, when

264

you're in the office in person and

people know your face, they shake your

265

hand, they get to hear your jokes,

your career will grow more if you are

266

in the office than if you are remote.

267

That's just the trade-off.

268

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

don't really care about career growth,

269

I just don't wanna commute," great.

270

That's fine.

271

But I just wanna let you know that remote

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

272

or everyone hypes it up on the internet.

273

And it's hard to get.

274

I just wanna be realistic with you.

275

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.

277

All right, number 10, it's that

the imposter syndrome that you're

278

feeling right now as an aspiring data

analyst never freaking goes away.

279

It never does.

280

It is so hard to actually feel like

you know anything in the data fields

281

because one, it's constantly changing,

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

282

like, impossible to know everything.

283

So that feeling you have right now that

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

284

know everything you should, that you

don't know everything in Excel, that

285

you've never even touched Python, that

you kinda suck at SQL, guess what?

286

That never goes away.

287

That's just there the rest of your career.

288

And the more…

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The earlier you become comfortable living

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

290

I know I can learn this," the better.

291

Because the data world, everything's

changing literally constantly,

292

and you will always be learning,

and you'll never know it all.

293

And so the fact that you can just own up

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

294

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

295

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