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⌚ TIMESTAMPS

00:00 - Introduction

00:06 - Become laser-focused on your 'why'

03:34 - Focus on what matters to land the job

05:37 - Quit on being quiet, share your work!

07:51 - Networking

11:44 - Maximizing Your Time

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Here are the five things you need to be doing to get ahead

2

of 99 percent of data analysts

in the next three to six months.

3

Number one, become laser

focused on what you want.

4

If you're watching this video,

chances are you want to become a data

5

analyst, but specifically what type

of data analyst do you want to become?

6

Do you want to become a financial analyst?

7

Do you want to become

a healthcare analyst?

8

What industry do you want to work in?

9

What companies do you want to work for?

10

What type of problems

do you want to solve?

11

What tools do you want to use?

12

When do you want to land that job?

13

How much do you want to make in that role?

14

Do you want to be working remote?

15

Hybrid?

16

What type of impact do you want

to have at that organization?

17

You need to get into the nitty

gritty details of what you

18

actually want in your data career.

19

And once you've figured out

the what, Then you need to ask

20

why, why do you want that role?

21

Why that particular role, that particular

company, why this career in general?

22

And then once you have that, why

you need to ask why one more time,

23

what's the actual reason that

you want to be working from home?

24

What's the actual reason you want to

be making a hundred thousand dollars.

25

Once you've figured out that why,

you should ask yourself why again.

26

This is a method created by

the founder of Toyota, Mr.

27

Toyota himself.

28

When he was trying to figure out an

answer to a problem, he would ask why five

29

times until he found the ultimate root

cause of the desire or of the problem.

30

Once you've figured out what you want

and why you want it, then commit yourself

31

that you're actually going to do it.

32

That no matter the cost,

you're going to figure it out

33

because your why is big enough.

34

There's that old phrase, when

there's a will, there's a way.

35

And if your will is big enough,

you'll figure out the way.

36

No matter your background, even if

you don't have any sort of technical

37

experience, even if you're coming

from a non STEM background or you

38

have no experience working at a desk

job at all, you will figure it out.

39

There's an old fable, uh, that's called

The Crow and the Pitcher, that basically

40

there was a crow that was really

thirsty and it found a pitcher of water.

41

But the neck of the pitcher was too thin

for the crow to actually, you know, stoop

42

its neck down there and get a drink.

43

And I think a lot of us in this

case would give up if we were the

44

crow and be like, Oh, look at this.

45

This pitcher is too small.

46

We're never going to be

able to drink this water.

47

I'm just going to give up.

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I'm going to say it's the economy.

49

I'm going to say it's the market.

50

I'm going to say it's just, you

know, bad luck, but not this crow.

51

This crow came up with a creative

solution and actually found small

52

stones that the crow could throw down

into the pitcher, ultimately raising

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the level of water high enough that

the crow could drink from the pitcher.

54

So I promise no matter your background,

if you have a college degree, if you

55

don't have a college degree, if you've

been making six figures already, or

56

if you've only been making 10, 000

a year, we can figure out a plan.

57

To get you to a data analyst job,

but it's important that we need to

58

create a plan because when you fail

to plan, you should plan to fail.

59

Honestly, if you're just thinking

that you're going to luck into a

60

data job, not in today's economy,

it is so much harder to land a data

61

job today than it was a decade ago.

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And you have to be intentional about it.

63

It's very rare that a data job is

just going to fall in your lap, even

64

if you're trying hard to land one.

65

You need to develop a plan, a personal

roadmap of actual steps that you can

66

take one by one to land your data job.

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This means when you sit down at

your computer to study, you should

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know exactly what you're studying

and why you're studying it.

69

This rarely means that you should

ever sit down and be like, Hmm,

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what am I going to do today?

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No, you should know beforehand exactly

what steps you're going to take.

72

I'm going to be posting on LinkedIn.

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I'm going to leave five comments.

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I'm going to work on my Tableau project,

and then I'm going to call it a night.

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This will help you get to your goal

faster, but it'll also keep your sanity.

76

When I'm tackling big projects, like

completely pivoting my career, I

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need to do it step by step, milestone

by milestone, and follow actionable

78

steps to get to the end goal.

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And this actually leads me to number

two, which is to actually focus on what

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lands you a job, not what feels good.

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If I were to create a scatterplot

of time it took to land the data job

82

against how skilled someone is at

data skills, say SQL, It would not

83

be a linear one to one correlation.

84

You'd like to think the people who are

better at SQL would land data jobs more

85

quickly, but it's just not the case.

86

There's too many factors in play.

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In fact, none of your data skills

really correlate with how fast

88

you're going to land a job.

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So why are you spending so much

time learning new data skills

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when that's actually not what

correlates to landing a job?

91

Later in this episode, I'll talk

about some of the things that I think

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matter more than your data skills

when you're landing a data job.

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But it's really important that you're

focusing on what lands data jobs.

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For example, if I stayed SQL 24 hours a

day for the next 365 days, I'd be really

95

good at SQL, but I wouldn't magically

land a data job because I'm good at SQL.

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It would require me applying to data jobs.

97

There's no magical level that you'll hit

in SQL, Python, or any other data tool.

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That's going to magically

get you a data job.

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And at this point, honestly, if

you haven't landed a technical

100

data interview and failed it, it's

not your skills holding you back.

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It's something like your resume

or your LinkedIn profile.

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Unless you're routinely failing technical

interviews, you don't need to be working

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on your technical skills all that much.

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Once you have a good foundation.

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So why is everyone still

working on their data skills?

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The phrase that best describes it

is one that I don't really enjoy,

107

but I can't think of anything else.

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I'm actually going to look in

chat GPT right now to see if I

109

can come up with a better phrase.

110

Update.

111

I checked chat GPT.

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I can't find a better phrase

and it's mental masturbation.

113

It's the idea that what you're actually

doing is making you feel good, but

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it's really getting you nowhere.

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Learning data skills, it makes

you feel more productive than

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sending out 50 cold messages to

recruiters and getting no responses.

117

That makes you feel rejection.

118

Learning data skills is fun.

119

It's your learning.

120

It makes you feel productive.

121

But you have to remember that

learning data skills and landing a

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data job are two different things.

123

They are related, but they're

not directly correlated.

124

So you have to lower your scope

here and actually be laser

125

focused on what you need to do.

126

What is the actual things, the steps

you need to take to land a data job?

127

One easy thing that you can start doing

to actually help you make some traction

128

on your data journey is number three.

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And that is to quit being silent

and actually share your work.

130

If you're not talking about what

you're doing, you honestly don't exist.

131

Now, this can come in a variety of forms.

132

I challenge you to start posting

on LinkedIn about what you're

133

learning, your daily data journey.

134

The reason I challenge you to do that

is because it literally changed my life.

135

And honestly, if I had never started doing

that, I wouldn't be making this video.

136

You would not be hearing my words.

137

I would just be some data scientist

from the middle of nowhere in Utah.

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But because I started talking

about what I was doing, you

139

guys are hearing my voice today.

140

So start posting on LinkedIn.

141

Tell me what you learned today.

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In fact, I challenge you right now

to pause this video and go post on

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LinkedIn and share this video to talk

about one of the things you're going

144

to try to do is to talk more and

explain and document your process.

145

And I know some of you guys are

thinking, uh, Avery, you are so cringe

146

and everyone's so cringe who posts on

LinkedIn and maybe it is a little cringe.

147

Fine.

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But are you willing to be a little cringe

going back up to number one and your why?

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Your why strong enough to overcome that?

150

Personally, mine is, and

I hope yours is as well.

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If you can't post on LinkedIn for

whatever reason, or it's too scary to

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get started, then just start talking

about what you're doing in your resume.

153

Make sure your resume accurately

is showing your career

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pivot, build a portfolio.

155

Talk about what you're

doing on your portfolio.

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It doesn't even have to be

for, for the public eyes.

157

Other than when you're applying for

jobs to hiring managers and recruiters,

158

instead of getting stuck in tutorial,

how doing the same exercises that

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the rest of the people watching this

YouTube video are doing, build a

160

project, talk about your project,

do a writeup of your project, make a

161

video talking about what you've done.

162

Put it on a portfolio.

163

Think about this.

164

A recruiter or a hiring manager basically

looks at your application for like

165

anywhere between three to seven seconds.

166

How are you going to stand out

in those three to seven seconds?

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How are they supposed to get an accurate

description of who you are in that time?

168

The answer is they're not really going

to, but if you can provide them with

169

like some evidence, some stuff that

you've actually done, like a project.

170

You're going to have a lot higher

chance of earning their next 10

171

seconds, and then the next 60

seconds, and then the next 60 minutes.

172

In today's economy, it's just

not enough to apply for jobs.

173

You have to actually be talking

about what you're doing.

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This leads me to number four,

which is going to be controversial.

175

But you need to be living by the

old fashioned maxim, it's not

176

what you know, it's who you know.

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And that's just the truth.

178

70 percent of accepted job offers

come from being recruited or referred.

179

This basically means you

need to be networking.

180

Remember earlier how I told

you skills aren't directly

181

correlated to getting hired?

182

Well, who you know and your network

is directly correlated at 70%.

183

Honestly, if that's the case and

you actually believe that like two

184

thirds of accepted job offers come

from being recruited or referred,

185

Why aren't you spending two thirds

of your time working on your network?

186

The answer is, it doesn't feel good.

187

Networking sucks.

188

Especially at the beginning when

you're just growing your network.

189

It feels pointless.

190

It feels awkward.

191

You don't know who to talk to.

192

You don't know what to say.

193

Look, I get it.

194

I was the same way.

195

I used to be you watching these videos.

196

And then I did number three and I

started posting on LinkedIn and all

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of a sudden my network was growing.

198

And one day I finally got the

courage to actually reach out to

199

Kate Strachney, who was a really

big LinkedIn influencer at the time.

200

And I did a collaboration with her.

201

That was after, honestly, I sent

dozens, if not hundreds of cold

202

messages that either kind of got

ignored or didn't really lead anywhere.

203

They were all just dead ends.

204

And after I went to a bunch

of in person data events and

205

I Didn't really meet anyone.

206

I honestly can name one person

I met from those events.

207

Networking honestly feels pointless

until all of a sudden it doesn't.

208

And for me, one of the biggest

changes in my life is when I

209

actually reached out to Ken G.

210

Ken is a data scientist, YouTuber, who

has always had way more followers than me.

211

And one day I reached out and I actually

invited him to a platform that was

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invite only at the time called Clubhouse.

213

It was basically like an

audio group call together.

214

It was kind of a weird product, but I

offered him my only invite that I had.

215

And he really appreciated it.

216

And so we ended up doing a video together

and he actually ended up introducing

217

me to people like Alex, the analyst,

Josh Farmer, and a bunch of other data

218

content creators who I've now had the

chance to interview on my podcast.

219

Getting connected to Ken, it.

220

Was lucky, I totally admit,

I had a lot of luck in play.

221

He had to read my message, he had

to be interested, and Clubhouse

222

at the time, he had to be a good

person and kind and want to help me.

223

But you can't just say it's luck, because

I sent hundreds of other messages that

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never got opened or never got replied to.

225

Networking, especially for introverts

like you and me, will always suck.

226

It's just if your why is big

enough, you embrace the suck.

227

This is what I meant earlier when I talked

about, you know, learning SQL, learning

228

more SQL is fun, but it's not really

getting you closer to your day to job.

229

When sending cold messages to recruiters

would get you closer to your job, but it

230

doesn't really feel like it until it does.

231

In fact, I had a stay at home mom

who recently landed a data job.

232

She had been out of the workplace

for 20 years and her previous

233

roles were a teacher, so not

even closely related to data.

234

She landed a data job with

only one application, one

235

interview, and she got the offer.

236

She got lucky.

237

And if you just hear that, you would

just be like, oh, she got lucky.

238

And she did get lucky.

239

But what you aren't seeing behind the

scenes is the hard work and dedication

240

she was putting in towards networking.

241

She found someone for this role

that she could cold message.

242

Cold message them, no response.

243

Found another person,

cold message, no response.

244

Found another person.

245

Cold message, no response.

246

I think most of us

would've given up, right?

247

Cold message someone else.

248

Response.

249

Said, sorry, can't help you.

250

I think we would've all given up there.

251

But not this person.

252

She sent another cold message to

another person that she found.

253

And this person said, oh, you

have an interesting resume.

254

Let me see what I can do.

255

Turns out that role wasn't

even supposed to be posted.

256

It was only an internal hire and

the recruiter had messed up and

257

actually opened it to the whole world.

258

So hundreds of you had applied for that

job and you never stood any chance of

259

actually landing it because they had no

intention of actually hiring externally.

260

But because my student had cold messaged

this person, their resume was in front

261

of the hiring manager's eyes already.

262

And the hiring manager said, well,

this is a pretty interesting resume.

263

Let's take a look and let's

bring her in for an interview.

264

She got the role.

265

Networking will help you land

jobs that aren't even open.

266

It'll open doors that are locked close.

267

And whether you like it or not,

whether you're introverted or

268

not, that's the case for everyone.

269

The last thing that you can do, number

five, to actually get ahead of data

270

analysts this year is mind the gap.

271

And what I mean by that is

we all have limited time.

272

We each have 24 hours in the day,

no matter if you're Elon Musk or

273

the poorest person on planet Earth.

274

That's something that we

all have is just 24 hours.

275

And some of you guys are working two jobs.

276

You're working like 80 hours a week.

277

You have kids.

278

I totally get that.

279

You're like, Oh my gosh, I don't know

when I'm going to do this, Avery.

280

Like how the heck am

I going to learn this?

281

And my short answer is, I don't

know how you do it either,

282

but here's my suggestion.

283

Mind the gap.

284

And what I mean by that is when you

go to the tube in London, they have

285

mind the gap painted on the ground.

286

And they're basically

saying, pay attention.

287

To the space the empty space between

the edge of the platform and as you

288

step on the actual subway Obviously

good advice if you're ever on the subway

289

But what does that have to do with you

and your data career no matter how busy

290

you are and how many things you have?

291

On your schedule, you're always gonna

have little teeny tiny gaps in your

292

day I have them all the time and

honestly, I feel a lot of it with

293

Instagram scrolling looking on Twitter

And watching YouTube videos kind of

294

like this and watching things like Mr.

295

Beast videos on YouTube.

296

My suggestion to you to get ahead

of 99 percent of data analysts is to

297

fill that gap with videos like this.

298

Cut out as much fluff as you can

in the gap and actually try to fill

299

it with things that are valuable,

that are worth listening to.

300

I think that's one of the biggest things

that you can do in your data career is

301

actually be listening to stuff like this.

302

Because obviously if you're watching, it's

involving your eyes, a lot of time you're

303

going to be at a TV, at your phone, or

at your desktop or something like that.

304

With audio, you can be

doing two things at once.

305

So for example, if you have any commute

right now, fill that commute with

306

listening to data YouTube videos.

307

If you found this video on YouTube,

continue listening on YouTube.

308

If you found this via the podcast,

keep listening on podcasts.

309

But obviously you don't

only have to listen to me.

310

There's other great podcasts.

311

I really enjoy the Super Data Science

Show, Plumbers of Data Science,

312

How to Land an Analytics Job, Data

Engineering Podcast, DataViz Today.

313

In terms of YouTube channels,

obviously Alex the Analyst is great.

314

I'm a big fan of Elijah Butler.

315

I just interviewed Tu Vu recently

on my podcast and she's great.

316

I really like Mo Chen's videos and there's

obviously a lot of other good shows.

317

But fill your day and fill

specifically those gaps, specifically

318

with audio, with this good data

content that's honestly free.

319

I promise as you do that, you will

continue to learn and grow without

320

even having to spend more time.

321

So to recap, become laser focused

on what you actually want, a

322

title and the why behind it.

323

Then focus on what actually matters.

324

Cut out all the fluff and focus

on the actual steps that's

325

going to land you a data role.

326

Then, quit being quiet

and actually speak up.

327

We want to hear what you're doing

and you will benefit from it.

328

Remember, it's not what you

know, it's who you know.

329

And five, fill the gap

with content like this.

330

If you want to keep filling your gap

with my content, I highly suggest

331

this episode next and I'll have

it in the show notes down below.