170: Brutally Honest Advice About Landing a Data Job in 23 Minutes

Speaker: 00:00:00

I've spent the last 10 years working

as a data analyst, data scientist,

2

: 00:00:03

and data engineer for some pretty

cool companies like ExxonMobil,

3

: 00:00:08

MIT, the Utah Jazz, and others.

4

: 00:00:11

In the last four years, I've

dedicated my time to teaching others,

5

: 00:00:14

learning how to land their first

data job, and now my students work

6

: 00:00:18

at Apple, Amazon, Rivian, Tesla, and

some other pretty cool companies.

7

: 00:00:23

Let me share 13 things I wish I

knew when I was getting started.

8

: 00:00:28

Number one, your skills aren't

holding you back properly when it

9

: 00:00:33

comes to landing your first day job.

10

: 00:00:34

It's a very frustrating process,

especially in today's market.

11

: 00:00:38

In today's economy.

12

: 00:00:39

There's lots of rejection, there's lots

of frustration, there's a lot to learn.

13

: 00:00:44

But the majority of your time, employers

don't even know how skilled you are.

14

: 00:00:49

Like if they're hiring for a data analyst

position that requires sql, and you

15

: 00:00:53

think SQL is what's holding you back.

16

: 00:00:55

The odds are SQL's not holding you

back because how does the employer

17

: 00:00:58

know how good you are at sql?

18

: 00:01:01

They really don't.

19

: 00:01:02

Unless you've taken some sort

of a technical interview.

20

: 00:01:05

If you're getting rejected and you

think it's your skills, it's actually

21

: 00:01:08

probably something like your resume

or your LinkedIn or your experience

22

: 00:01:12

that you're portraying on either of

those, and you'll want to try to make

23

: 00:01:16

it look like you know more than you

do probably, if I'm being honest.

24

: 00:01:19

That is what's probably holding you back.

25

: 00:01:22

Unless you're failing technical

interviews or you're doing technical

26

: 00:01:25

interviews and you're not getting

hired, your skills aren't going to

27

: 00:01:30

get you more technical interviews.

28

: 00:01:32

The better you are at SQL does not

equal how many SQL interviews you have.

29

: 00:01:36

It's the perception, so you need to

make sure that you have a good LinkedIn

30

: 00:01:40

in a good resume highlighting sql.

31

: 00:01:42

But to be honest, like if someone's really

skilled at SQL and has a bad resume and

32

: 00:01:47

someone's okay at SQL but has a good

resume, this person's going to get.

33

: 00:01:52

More interviews a lot of the time.

34

: 00:01:53

I know it's unfair, but that's

just how it's number two.

35

: 00:01:57

You will get paid to learn on the job.

36

: 00:01:59

I promise that it'll

happen in your career.

37

: 00:02:01

It's happened many times in my career.

38

: 00:02:03

I've learned power, bi,

Tableau, sql, Python, Excel.

39

: 00:02:07

I pretty much learned

everything on the job.

40

: 00:02:10

Now, that does mean you need to

have a base, like you need to know

41

: 00:02:13

something that will get you hired.

42

: 00:02:15

Like you can't know nothing, but the

odds are you're going to be learning

43

: 00:02:19

on the job quite a bit because one.

44

: 00:02:22

It's really hard to

know everything in data.

45

: 00:02:23

Like there's so many different things.

46

: 00:02:25

Two, it's always expanding.

47

: 00:02:26

So even if you did know everything today,

you will not know everything a year from

48

: 00:02:29

now, especially with how ai, uh, and just

rapid technology change and data is going.

49

: 00:02:35

Um, and number three, a lot of

the times there's like more niche

50

: 00:02:38

softwares that you'll use that like

you probably haven't even heard of.

51

: 00:02:42

So for example, when I was at

ExxonMobil, we used a tool to do

52

: 00:02:46

data analytics, you could say.

53

: 00:02:48

Um, and it was called

pims, and I'm sure like.

54

: 00:02:51

No one watching this has

ever heard of pims, PIMS.

55

: 00:02:55

Uh, if you have heard of PIMS for

oil, crude basket selection and

56

: 00:02:58

optimization, let me know in the

comments, but my guess is 99.9%

57

: 00:03:03

of you guys have never heard of it,

and it's something I used every day.

58

: 00:03:06

And there's the equivalent of PIMS for

all different industries and all sorts

59

: 00:03:11

of different niches inside of industries.

60

: 00:03:13

There's so many tools out there that

you've never even heard of that like you

61

: 00:03:16

wouldn't even bother learning, but you

will be using those on the job, maybe as

62

: 00:03:19

like your primary data analytics tool.

63

: 00:03:21

So eventually you are going to get

paid to learn tools that you don't

64

: 00:03:25

know, which brings me number three.

65

: 00:03:27

You don't have to know everything.

66

: 00:03:29

You don't have to know everything

to land your first aid job.

67

: 00:03:31

I definitely don't know everything now.

68

: 00:03:33

I'm on, like whatever, my 10th data

job or whatever, however you wanna

69

: 00:03:37

count all my different experiences.

70

: 00:03:38

Like I definitely don't know everything.

71

: 00:03:40

Um, I even taught a data engineering

course at MIT and I am not

72

: 00:03:45

that great of a data engineer.

73

: 00:03:46

I did not know that much about data

engineering when I took that role.

74

: 00:03:50

Uh, and the truth is like, it's

okay to not know everything.

75

: 00:03:53

You won't know everything.

76

: 00:03:54

And you don't have to know everything.

77

: 00:03:56

Now you do.

78

: 00:03:57

You have to know something.

79

: 00:03:57

Yeah.

80

: 00:03:58

You have to know something.

81

: 00:03:59

But the idea that you have to like know

every single thing before you can even

82

: 00:04:03

start applying is just holding you back.

83

: 00:04:06

So the quicker you realize, Hey,

I don't know everything, and

84

: 00:04:09

that's okay, I'll figure it out.

85

: 00:04:10

What I do need to know, uh, the

sooner you'll be better off.

86

: 00:04:14

Because that is like the biggest

mindset change that will allow you

87

: 00:04:18

to apply for more jobs, more stretch

jobs, things that feel like you're

88

: 00:04:21

not going to land, but you might land.

89

: 00:04:23

You never know.

90

: 00:04:24

Just know.

91

: 00:04:25

You don't have to know it all.

92

: 00:04:25

That's it.

93

: 00:04:27

Four, and this one kind of

sucks, but who, you know, matters

94

: 00:04:30

way more than what you do.

95

: 00:04:32

Uh, it's not, it's not what you

know, it's who you know, right?

96

: 00:04:35

Like that old adage.

97

: 00:04:37

Uh, that is so true.

98

: 00:04:38

Um, I think most of the success

I've had in my career has

99

: 00:04:43

really been to who I know.

100

: 00:04:45

Um, now I didn't know all

those people to start.

101

: 00:04:47

I made a lot of those connections,

uh, from the ground up.

102

: 00:04:50

And if you don't know anyone, you can make

those connections from the ground up too.

103

: 00:04:54

But like.

104

: 00:04:55

We just live in a society and

a world where opportunity is

105

: 00:04:58

given to people and people.

106

: 00:05:01

It's not necessarily merit based.

107

: 00:05:03

It's more risk free based.

108

: 00:05:06

And let me explain because it's like

when you're trying to fill a position

109

: 00:05:09

or when you're looking for a leader

in a project or you're looking to

110

: 00:05:12

promote someone, a lot of the times

it's like, Hey, well who do we know?

111

: 00:05:15

Right?

112

: 00:05:16

Uh, how talented you are or what you've

done is only as valuable as the people

113

: 00:05:22

in power who know about those things.

114

: 00:05:24

I, I work in a shed in my backyard,

and let's say like I cured cancer

115

: 00:05:28

back here, like I solved the

biggest mystery in the world.

116

: 00:05:31

If no one knows about it, it

doesn't really make a difference.

117

: 00:05:34

Now that is such an accomplishment that

if I told someone, it would probably

118

: 00:05:38

go through the grapevine and then I get

interviewed by the local news and I get

119

: 00:05:41

interviewed by the national news, and then

who knows, maybe I'm winning like a Nobel

120

: 00:05:45

Prize and everyone knows my name and I'm

the most famous person on planet Earth.

121

: 00:05:48

That's definitely possible.

122

: 00:05:49

Like your accomplishments can be so good.

123

: 00:05:52

That it makes you known to the whole

world, but for the majority of us,

124

: 00:05:56

that's probably not gonna be the case.

125

: 00:05:58

And so it's important that the good work

we do, do gets recognized by people.

126

: 00:06:04

And you have to know people in

order to get recognized by people.

127

: 00:06:07

So spend time at work, getting to

know your coworkers, getting to know

128

: 00:06:11

your boss, getting know, getting to

know your boss's boss, getting to know

129

: 00:06:15

your, like boss's, like equivalent on

a different organization or a different

130

: 00:06:19

division or something like that.

131

: 00:06:21

Like who you know, really

matters in your career and will

132

: 00:06:23

make a really big difference.

133

: 00:06:24

And if you're not quite in a career

that you wanna be in right now,

134

: 00:06:27

that can be true for networking

before you get into a career.

135

: 00:06:32

So for example, all of my

accelerator students, you know,

136

: 00:06:35

they're new to data analytics.

137

: 00:06:36

They're transferring from being a

teacher or being a delivery driver.

138

: 00:06:40

Uh, or I don't know, being

a scientist or something.

139

: 00:06:45

Getting to know me is valuable,

to be honest, because I

140

: 00:06:47

have a lot of connections.

141

: 00:06:48

I have like 150.

142

: 00:06:50

A thousand connections or

followers on LinkedIn, right?

143

: 00:06:53

I have this YouTube

channel, I have my podcast.

144

: 00:06:55

I know people who are hiring.

145

: 00:06:56

I can, you know, talk about

people in my newsletter.

146

: 00:06:59

I can talk about people, my students

on LinkedIn, so on and so forth.

147

: 00:07:02

So, oftentimes it is important

who you know, and you can start

148

: 00:07:06

from scratch, I promise you.

149

: 00:07:08

Number five, your domain expertise

matters more than you think,

150

: 00:07:13

especially in the future with, uh, ai.

151

: 00:07:15

Um, doing data analytics

is really important.

152

: 00:07:19

We never just do data analytics

for data analytics sake.

153

: 00:07:22

It's not for funsies that

we're analyzing data.

154

: 00:07:24

It's always to make an organization

decision to make a business decision, to

155

: 00:07:28

save money, to save time, to save lives.

156

: 00:07:31

We're doing data analytics for the the

ends, not for just doing it, right.

157

: 00:07:37

There's not like just a

rollout there in the world.

158

: 00:07:40

That's just like doing data

analytics on data analytics.

159

: 00:07:43

That's very meta, right?

160

: 00:07:45

All the data analytics jobs are

on healthcare data, on financial

161

: 00:07:47

data, on manufacturing data.

162

: 00:07:51

And so whatever you've done

in the past is really valuable

163

: 00:07:53

because you understand the domain.

164

: 00:07:56

You have what's called domain expertise,

and if you just brought in like a

165

: 00:08:01

random data analyst, they would not

understand your domain as well as you do.

166

: 00:08:04

When I worked at ExxonMobil, I have

a chemical engineering background,

167

: 00:08:07

so I studied chemistry, I studied

engineering, I studied manufacturing.

168

: 00:08:11

So every once in a while we'd have these.

169

: 00:08:14

Company-wide analytics on competitions

and anyone could, could enter and

170

: 00:08:19

you, they give you a data set and

they'd say, analyze this data set.

171

: 00:08:22

And at the time I was pretty, pretty new

to the data world and wasn't necessarily

172

: 00:08:26

the best data scientists or data analysts.

173

: 00:08:29

I was competing against people

who had PhDs in data science,

174

: 00:08:32

who had PhDs in computer science.

175

: 00:08:33

We had PhDs in mathematics and I was able

to outperform them in these competitions

176

: 00:08:39

a lot of the time, not 'cause I was

smarter than them, or I could make better

177

: 00:08:41

models or I could code better than them.

178

: 00:08:44

Because I could relate what little

of data analytics I knew to the

179

: 00:08:48

business problem, to the actual

domain better than they could.

180

: 00:08:52

I understood the rules

of like the business.

181

: 00:08:55

I understood like the rules of

science, of, of manufacturing, of

182

: 00:08:59

engineering, and that really helped

me craft better analysis and craft

183

: 00:09:04

better explanations of my analysis.

184

: 00:09:07

If you have a background that's not

data analytics, that's not statistics,

185

: 00:09:10

that's actually a good thing.

186

: 00:09:11

Like your domain can really matter.

187

: 00:09:14

Now you can transfer domains.

188

: 00:09:15

That's a thing.

189

: 00:09:15

People do it all the time.

190

: 00:09:17

But I just wanna tell you, your domain is

valuable and you shouldn't give up on it.

191

: 00:09:20

Number six, don't take job rejections.

192

: 00:09:23

So personally, no one likes

getting rejected, right?

193

: 00:09:26

It's never fun whether it's

like getting rejected on.

194

: 00:09:30

A date that you ask or like you ask a girl

for her number, uh, or you apply for a

195

: 00:09:34

job and they reject you, but don't take

it personally, especially job rejections

196

: 00:09:39

in today's economy, because like there's

hundreds of applicants for every job.

197

: 00:09:44

So like every job you apply

for, let's just assume there's

198

: 00:09:46

like 200, 300 applicants.

199

: 00:09:48

That means like if we say 300 applicants,

299 people are gonna get rejections.

200

: 00:09:53

So it's gonna happen more

often than you think.

201

: 00:09:56

A lot of the time nowadays with the

A TS that stands to our applicant

202

: 00:09:59

tracking system, it's the suite of tools

that recruiters and hiring managers

203

: 00:10:03

use to try to make it easier for them

to decide who's the right candidate.

204

: 00:10:09

The A TS sucks, you

guys, it's not very good.

205

: 00:10:11

It's like not a very

good piece of technology.

206

: 00:10:14

I'm looking forward to seeing over the

next five, 10 years how it becomes better.

207

: 00:10:18

But right now it kind of sucks and

a lot of the times you're not even

208

: 00:10:22

getting your resume seen by human.

209

: 00:10:24

It's just a computer, a silly

computer who's looking at your

210

: 00:10:27

resume and is like, eh, I don't

think this resume is very good.

211

: 00:10:30

But it doesn't really know

what a good resume is.

212

: 00:10:33

We're in this world where we're getting

rejected all the time by computers

213

: 00:10:36

and it makes us feel bad, but like

the truth is that like these computers

214

: 00:10:40

aren't very smart, uh, and they're not

making good decisions to be honest.

215

: 00:10:45

They're making decisions that

limit that help hiring managers and

216

: 00:10:49

recruiters spend less time, but not

necessarily make the optimal decision.

217

: 00:10:53

And the truth is that like out

of, um, I dunno, 300 candidates, I

218

: 00:10:57

interviewed a hiring manager one time

who I think had like 250 applicants

219

: 00:11:02

and she was like, yeah, I think

50 of them would've been great.

220

: 00:11:06

So like literally I'll say 20% would've

been great candidates and if they would've

221

: 00:11:10

hired them, it would've worked out.

222

: 00:11:12

So like even, even a lot of

times you're getting rejected.

223

: 00:11:15

You could have done the job and you

could have done really great at it.

224

: 00:11:18

It still sucks, I realize that.

225

: 00:11:19

But I think what sucks more is when

we take the rejection so personally

226

: 00:11:23

that we kinda get depressed and we

stop applying for jobs and then we

227

: 00:11:26

never actually change our career.

228

: 00:11:27

I don't want that to happen to you.

229

: 00:11:28

So like, please stop

taking the rejections.

230

: 00:11:31

So personally and just realize it's just

a silly computer making a silly decision.

231

: 00:11:35

That's why networking, what we

talked about earlier, who you

232

: 00:11:38

know is really important because.

233

: 00:11:41

If you know the right people, you can

skip the whole a TS altogether and just

234

: 00:11:45

get an an interview and then show your

personality there and explain everything.

235

: 00:11:49

Humans can like understand the totality

of a candidate of a human candidate,

236

: 00:11:55

but computers, they just really look at

resume and it's like they're only seeing,

237

: 00:12:00

I don't know, 10% of who you actually

are and what you're actually capable of.

238

: 00:12:04

It's just silly.

239

: 00:12:05

You guys don't take it personally.

240

: 00:12:07

Number seven, data tiles.

241

: 00:12:09

They're super confusing.

242

: 00:12:10

The titles of different data

jobs are all over the place.

243

: 00:12:14

Obviously.

244

: 00:12:15

There's like data scientists,

data analysts, data engineer.

245

: 00:12:17

Those are like pretty cemented

and pretty straightforward.

246

: 00:12:21

But I've seen data science analysts,

I've seen data analytics scientists,

247

: 00:12:25

like I've seen so many different roles.

248

: 00:12:28

My job at ExxonMobil was for a

while was optimization engineer.

249

: 00:12:33

That doesn't sound data E at all,

but I really just built models and.

250

: 00:12:38

Power BI dashboards the whole

time I was in that role.

251

: 00:12:40

So like you just can't judge

a job off of the job title.

252

: 00:12:46

Sometimes job titles are are weird because

the company just doesn't know better

253

: 00:12:51

and they're kind of just making it up.

254

: 00:12:53

Other times, like there's

just no industry standards.

255

: 00:12:56

So it's just kind of all over the place.

256

: 00:12:58

But just know that like you need to

be looking at the like requirements

257

: 00:13:03

and making a judgment yourself on

what type of job this actually is.

258

: 00:13:06

So.

259

: 00:13:07

Be looking for keywords like sql,

Excel visualizations, mathematical

260

: 00:13:12

models, machine learning and stuff

inside of the description, and not

261

: 00:13:16

just taking the title for what it is.

262

: 00:13:19

Like.

263

: 00:13:19

You need to be coming up with your

own titles for every job description

264

: 00:13:21

that you read because they are

going to be quite different.

265

: 00:13:24

So they're really confusing.

266

: 00:13:26

Don't stress it.

267

: 00:13:27

Just know that that exists.

268

: 00:13:29

Number eight, data tools

matter less than you think.

269

: 00:13:32

What I mean by that is I think

now, um, I'm pretty decent at

270

: 00:13:37

a lot of different data tools.

271

: 00:13:38

I think my best data tool

personally is Python.

272

: 00:13:41

I'm pretty good at Python.

273

: 00:13:42

Next might be R for me, and

then after that it might be

274

: 00:13:47

Power bi, Tableau, sql, Excel.

275

: 00:13:50

But there's other ones that I can do.

276

: 00:13:52

I can do matlab, I can do JMP.

277

: 00:13:54

I could do JavaScript if I

had to, I could do D three.

278

: 00:13:57

I could do pencil and

paper, like I could analyze.

279

: 00:14:01

So I could use a, I could analyze data

with like all sorts of different tools.

280

: 00:14:05

And what I mean by this is like if

you're given a business task, like,

281

: 00:14:09

okay, we need to, we wanna know how many

products we're gonna sell next December.

282

: 00:14:14

I think I could do that in Tableau,

sql, Excel, Python, r Matlab, jump.

283

: 00:14:21

Like I, I could do it

basically in a data tool.

284

: 00:14:24

So I would focus maybe a little bit less

on the data tools and more about concepts.

285

: 00:14:28

If you get really good at one data

tool, you could probably just use that

286

: 00:14:31

data tool to pretty much do everything.

287

: 00:14:33

So I don't think data, tools,

learning them all especially is as

288

: 00:14:36

important as you think they are.

289

: 00:14:38

Number nine, the bookends of

analysis are the most important.

290

: 00:14:41

And what I mean by bookends is

think of it, think of data analytics

291

: 00:14:44

as like a sandwich, bread, meat,

bunch of other stuff, vegetables,

292

: 00:14:50

condiments, bread, right?

293

: 00:14:52

The breads, I think are

the most important part.

294

: 00:14:55

I think they're going to become

more important with the explosion

295

: 00:14:58

of AI and data analysis.

296

: 00:15:00

What I mean by the bread is like talking

to stakeholders at the beginning and

297

: 00:15:04

talking to stakeholders at the end.

298

: 00:15:06

Because once again, we're

not doing data analysis for

299

: 00:15:08

funsies, for data analysis sake.

300

: 00:15:11

We're doing it to make impact and to

change lives, save money, save time.

301

: 00:15:17

If we don't do a good job at the

beginning of talking to stakeholders,

302

: 00:15:20

we're gonna do analysis in vain.

303

: 00:15:22

We're gonna probably do the wrong

analysis for the wrong reasons

304

: 00:15:25

and it's not gonna be useful.

305

: 00:15:27

So the more we talk to stakeholders or,

or let's say that it is even useful,

306

: 00:15:31

it might not be adopted very well,

it might not be used like so many.

307

: 00:15:34

You hear so many people about

building dashboards that go

308

: 00:15:37

on to die, never be used.

309

: 00:15:39

And I think a lot of the times it's not.

310

: 00:15:40

It's because they didn't spend

enough time upfront explaining to the

311

: 00:15:42

stakeholders, okay, what do you want?

312

: 00:15:45

Why do you want that?

313

: 00:15:46

Let me create the system or service

that works best to solve your problem.

314

: 00:15:51

Then secondarily the ending where

you've actually done the analysis, you

315

: 00:15:54

need to tie it back to the business,

show them how to use it, make sure

316

: 00:15:57

that they can trust it, because if

you don't do that, once again, you're

317

: 00:16:00

gonna be doing data analysis in vain.

318

: 00:16:03

All the work you've done is just gonna

not go anywhere, and that happens a lot.

319

: 00:16:06

Don't feel bad if it happens, but

if it is happening, spending more

320

: 00:16:09

time on the front end or the back

end is probably the solution.

321

: 00:16:13

Number 10, how you present your

digital self matters more than

322

: 00:16:16

you present your physical self.

323

: 00:16:18

And what I mean by that is.

324

: 00:16:20

Your perception is really important.

325

: 00:16:22

How you're perceived is probably more

important than how you actually are.

326

: 00:16:26

And once again, I kind

of hate saying this.

327

: 00:16:28

I'm not saying to cheat, I'm not

saying to lie, I'm not saying

328

: 00:16:31

looks matter, but they kind of do.

329

: 00:16:34

Um, in, before you get a job, like

on a resume in LinkedIn, as well as

330

: 00:16:38

when you're in a company, the work

that you do is probably less important

331

: 00:16:42

than how your work is perceived.

332

: 00:16:45

That sucks, but it's just the game you're

going to have to play in your career.

333

: 00:16:48

And if you choose not to play

that game, I think you'll suffer.

334

: 00:16:51

So I think it's a game worth playing.

335

: 00:16:53

So that means like you need to

present yourself well on LinkedIn.

336

: 00:16:56

You need to present yourself

well on your resume.

337

: 00:16:58

You need to make sure that your boss

likes you and your boss's cousin likes

338

: 00:17:03

you, and you need to make sure that like

you're talking and you're, you're getting

339

: 00:17:06

seen because that's what's important.

340

: 00:17:10

If your work is important, but if it

doesn't get seen, it doesn't get used.

341

: 00:17:13

It's honestly not important.

342

: 00:17:15

And in today's economy, you have to

take care of you and your family.

343

: 00:17:19

And if that means you need to be

perceived as being a good actor, a

344

: 00:17:24

good professional, as a net positive

to your team and organization, then

345

: 00:17:28

you should spend the time and the

resources necessarily to do that.

346

: 00:17:33

So that would be when you're

doing something, make it known,

347

: 00:17:36

tell people about it, share it.

348

: 00:17:37

Don't just do your work in silence.

349

: 00:17:39

If you do your work in silence, I

think you and your family suffer.

350

: 00:17:42

Number 11, all industries

experience cycles.

351

: 00:17:46

Uh, I think we're in a cycle right now.

352

: 00:17:48

I think we're in a very

revolutionary cycle.

353

: 00:17:50

I think AI is really changing the game,

but all industries go through disruption

354

: 00:17:58

and they go through peaks and valleys.

355

: 00:18:00

Let me kind of explain.

356

: 00:18:02

I worked at ExxonMobil during 2020.

357

: 00:18:04

Now, what happened in 2020?

358

: 00:18:06

Everyone.

359

: 00:18:07

Oh, COVID.

360

: 00:18:07

Good job.

361

: 00:18:08

Class COVID happened in 2020, right?

362

: 00:18:10

And what happens?

363

: 00:18:11

Especially the beginning part of

COVID March, April, may, June.

364

: 00:18:15

We stopped going places.

365

: 00:18:16

We stopped going to work.

366

: 00:18:17

We stopped going to the movies,

we stopped going to sports games.

367

: 00:18:20

We stopped traveling.

368

: 00:18:21

What do you need to travel?

369

: 00:18:22

Oh, gasoline, jet fuel.

370

: 00:18:25

Who makes gasoline and jet fuel?

371

: 00:18:26

ExxonMobil.

372

: 00:18:27

So it was a really bad time to work at

ExxonMobil because our, no one was buying

373

: 00:18:32

oil and no one was buying gasoline.

374

: 00:18:34

So those prices went down quite a bit.

375

: 00:18:36

If you remember, I lived in

Texas when I worked for Exxon.

376

: 00:18:39

I think one time I got gas in Texas

during COVID for less than $2 a

377

: 00:18:44

gallon, which was like very, very low.

378

: 00:18:47

Now in Utah, I'm paying like 3, 3, 5, I

think per gallon, so almost half, right?

379

: 00:18:54

That was not good for ExxonMobil.

380

: 00:18:56

There was layoffs.

381

: 00:18:57

The future felt really grim.

382

: 00:19:00

Life was not good.

383

: 00:19:01

It seemed like things were,

were not going very well now.

384

: 00:19:04

Compare that with a company like Meta.

385

: 00:19:06

Well, if we couldn't go travel,

we couldn't go to sports games.

386

: 00:19:09

What did we do to entertain ourselves?

387

: 00:19:11

We sat on TikTok and Instagram and

scrolled all day, uh, which was

388

: 00:19:14

awesome because that meant that they

could charge a lot more money and

389

: 00:19:19

get a lot more advertisers, a lot

more eyeballs were on their apps.

390

: 00:19:22

And so meta stock went up as Exxons went

down, and, uh, yeah, that meant they

391

: 00:19:29

hired a lot, they hired a lot more people.

392

: 00:19:31

Um, then like two or three years later.

393

: 00:19:33

When things were back to normal,

there was less eyeballs on Instagram

394

: 00:19:36

'cause more people were driving,

more people were flying on vacation.

395

: 00:19:39

So Exxon Stock came back up and meta

stock went down and they did layoffs.

396

: 00:19:45

So here's the truth.

397

: 00:19:45

Data analysts, data scientists, data

engineers, they work in all different

398

: 00:19:49

industries and there's gonna be peaks

and valleys for different industries.

399

: 00:19:53

And you sometimes just have to wait

and be patient and not freak out.

400

: 00:19:57

And so that's what I'm

trying to do right now.

401

: 00:19:59

I don't think it's worth freaking out.

402

: 00:20:01

I think it's just worth being

patient for all these ai.

403

: 00:20:03

Dust to settle and figure out

where we'll be in one to two years.

404

: 00:20:07

I think AI is a big change, but

I kind of just see it as a cycle.

405

: 00:20:11

Alright, number 12, mentorship is the

shortcut to results, and this is kind

406

: 00:20:17

of going back to the who you know is

really important, but in my career,

407

: 00:20:21

having mentors has made a really big

difference because mentors are people

408

: 00:20:26

who have gone through what you've

already gone through and can tell

409

: 00:20:29

you the path that you should take.

410

: 00:20:31

For example, I've been doing YouTube

videos for about four years now.

411

: 00:20:35

Um, but you probably, if you're

watching this or listening to this as

412

: 00:20:38

a podcast, you're probably listening

to me for the first time in: 2025

413

: 00:20:42

If I had to guess.

414

: 00:20:43

Lemme know in the comments if I'm wrong,

and when I say lemme know in the comments,

415

: 00:20:46

I'm really talking to my YouTube people.

416

: 00:20:48

Where are you guys at?

417

: 00:20:49

Go to the comments right now.

418

: 00:20:50

But also if you're listening on

Spotify, Spotify has comments too.

419

: 00:20:53

You guys should try those there.

420

: 00:20:54

If you're listening to another podcast,

there's probably not podcasts or

421

: 00:20:57

probably not comments there, but, um.

422

: 00:21:00

I wanna know, like, are

you new to my worlds?

423

: 00:21:02

Because you probably are.

424

: 00:21:04

And one of the reasons is,

is I got a mentor last year.

425

: 00:21:07

His name's Jay Klaus, uh, and he

makes a lot of YouTube videos.

426

: 00:21:11

And over the last year or so that

I've kind of been in his world, I

427

: 00:21:15

think I've gone from like 15,000

to 45,000 subscribers on YouTube.

428

: 00:21:20

So that's like 30,000.

429

: 00:21:21

So I've basically doubled, no, I, I guess,

tripled my YouTube in the last year.

430

: 00:21:27

It was mostly 'cause I talked to

someone who knew what they were

431

: 00:21:29

doing and they gave me good tips.

432

: 00:21:30

And the same is true for you

in your, your career as well.

433

: 00:21:33

Like if you can find someone who's

already been there, done that, I

434

: 00:21:35

think they'll have a big impact.

435

: 00:21:37

It's so funny because it's not like

something that people talk about very

436

: 00:21:41

much and it's not like, it's kinda

like an abstract thought, but I think

437

: 00:21:45

it will make a really big difference

in your career if you have a mentor.

438

: 00:21:48

So my suggestion is if you're trying to

land your first data job, find a mentor.

439

: 00:21:52

Um, if you want mentorship, I

have the accelerator program.

440

: 00:21:55

That helps people land

their first data jobs.

441

: 00:21:57

If you're already in your role, find

someone at work, find someone at work

442

: 00:22:00

who's one to two steps ahead of you.

443

: 00:22:03

Taking to coffee, taking

to lunch, talk to them.

444

: 00:22:06

Ask them like what they would do

differently, like what they've done well

445

: 00:22:09

and what they maybe had done poorly.

446

: 00:22:11

Tip number 13 is you'll

never stop learning.

447

: 00:22:14

Data analytics is constantly

evolving, and if you stop learning,

448

: 00:22:18

that is when your career will die.

449

: 00:22:20

But as long as you're willing to

learn, I think you're going to

450

: 00:22:22

do really well in this career.

451

: 00:22:24

And I think that's one of the things

that's made a big difference in my

452

: 00:22:27

career is I'm always willing to learn.

453

: 00:22:30

In fact, I read five pages

every single day, so I am

454

: 00:22:32

constantly learning something.

455

: 00:22:34

Uh, and I spend a lot of my time

even at work trying to read, watch

456

: 00:22:39

videos of things that are coming out.

457

: 00:22:41

I also experimenting a lot.

458

: 00:22:43

I'm a big experimenter where it's like,

okay, I've kinda heard about this thing.

459

: 00:22:46

I don't really know it yet.

460

: 00:22:47

I'm just gonna try to open it

up and see if I can use it.

461

: 00:22:49

I did that recently.

462

: 00:22:50

With MCP, I didn't

really know what MCP was.

463

: 00:22:53

Model context, protocol.

464

: 00:22:55

And then I, I tried basically using

Claude to build some, uh, data pipelines

465

: 00:23:00

and I was like, oh, I totally get MCP

and I totally get why it's awesome.

466

: 00:23:04

So when you hear about something,

like, for me, the best way to

467

: 00:23:07

learn about it is to like get hands

on experience actually doing it.

468

: 00:23:11

So there you guys have it.

469

: 00:23:13

If you enjoyed this, please hit

subscribe and uh, we have a new

470

: 00:23:15

video coming out every single week.

471

: 00:23:17

Thank you guys for watching.

472

: 00:23:18

We'll talk to you soon.