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Kam was at Home Depot for five years with a sports management degree and zero data experience. Three months later he landed his first data job.

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⌚ TIMESTAMPS

00:00 – Sports management to data

04:03 – Tutorial hell is real

06:54 – How he found the job

16:54 – Domain knowledge wins

19:18 – Challenge yourself

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I've been at Home Depot

for about five years.

2

I had been stuck in tutorial hell

for, like, like, months on end, so…

3

And I just, yeah, like, applied

to a lot of people, like, probably

4

15 different people a day.

5

Come September, you

actually had a job offer.

6

You're top 1% if you know Python.

7

But, like, your domain knowledge matters

so much more than your technical skills.

8

And for you, has it been worth it, do you

feel like, this, this whole transition?

9

Yeah, 100%.

10

This is Cam, and a year ago, he was

bouncing around a Home Depot, five

11

years deep, a sports management degree,

and absolutely zero data experience.

12

And he was stuck in what he calls tutorial

hell, months of online courses, just going

13

in circles, making no real, true progress.

14

And then he joined my boot camp, the

Data Analytics Accelerator, in June, and

15

by September, he had a data job offer.

16

No fancy CS degree, no years of

experience, and in one of the

17

toughest markets we's ever seen.

18

And he still did it in about three months.

19

In this conversation, he breaks down

exactly how he found the job, the one

20

outreach move that he did that no one else

does that got him in the door, and the

21

thing that surprised him the most about

actually landing a data role, and it has

22

nothing to do with how technical he was.

23

Let's go ahead and get into it.

24

All right, Cam, you are now

an inventory analyst at Incon,

25

but you didn't start that way.

26

You had some other jobs before

you became a data analyst.

27

Tell us a little bit about what you

were doing before you joined my boot

28

camp, before you became a data analyst.

29

What were you kind of doing for work?

30

So, um, yeah, I was at Home…

31

I'd been at Home Depot

for about five years.

32

I was just jumping around the store.

33

I was in freight.

34

I was in customer service.

35

Basically anywhere they

needed me or I wanted to be.

36

And then, 2024, I joined grad school,

uh, for master's IT after graduating from

37

Kennesaw with a sports management degree.

38

And, uh, yeah, I mean, that

was really the gist of it.

39

I just, once I graduated from

sports management, it just didn't

40

feel like a, the right fit for me.

41

I, I don't think I challenged

myself enough in undergrad.

42

The stuff I ended up applying for

anyway was, like, similar to being in

43

an office or being in IT, so I just kind

of pushed myself to the limits and just

44

got a degree in something completely

outside of my realm and just considered

45

it to be a huge learning curve, so.

46

But the whole time, yeah, I

was at Home Depot working.

47

Okay, that's amazing.

48

So you graduate college with a

sports management degree, and, uh,

49

that was kind of your background.

50

You did- you play a little, uh,

collegiate football in college.

51

You were kind of in the sports world.

52

You graduate, and you're like,

"Ah, what type of job do I want?"

53

Maybe not one of these sports jobs, so

you're like, "Ah, I wanna go into IT."

54

You enroll in a master's

program in January of that year.

55

You know, obviously you're

at Home Depot at the time.

56

And for those who are not familiar

with Home Depot, it's kind of like

57

a home goods, like, a hardware,

a get-your-stuff-done store.

58

How was that?

59

Like, did you like working there?

60

Did you like the job you were doing?

61

Did you want to leave?

62

Obviously, you wanted to leave a

little bit 'cause you were, you

63

know, pursuing these, these degrees.

64

I think it was more of

just being- I don't know.

65

I did wanna leave, but I just

wanted to do something…

66

I, you know, I pictured my life a certain

way as far as just consistency and

67

living a certain way and, and working

in a certain consistency as well.

68

Tech and the people around me have

allowed that, being in data especially.

69

So yeah, I would say I did wanna

leave, but I think it was less of

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leaving and just wanting something new.

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'Cause Home Depot has been

good to- I, definitely, I,

72

I can't complain about that.

73

But yeah, I definitely did want to,

uh, be where I am now It makes sense

74

because, you know, yeah, once again, like

nothing wrong with Home Depot at all,

75

but obviously it's a very different…

76

It's not a desk job.

77

You're, you're up- Yeah … you're

working, you're working with, uh,

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customers versus- Right … like as a

data analyst, you're working with graphs

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and- Yep … visualizations and, uh, stuff

like that, so very different lifestyle.

80

Okay, so you- you're

working at Home Depot.

81

You enroll in this master's program

in, in January, and then come June or

82

July you end up enrolling in the Data

Analytics Accelerator, my boot camp.

83

I'm curious like why you made that

decision, 'cause a lot of people

84

will tell me, you know, like, "I'm

already in a master's program, like

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I don- I don't need your boot camp."

86

So I'm curious why you thought maybe the

boot camp was a good decision for you.

87

So, you know, growing up playing

sports all the time, like, you

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know, things can get really

competitive just as far as a mindset.

89

And the coolest thing about Data

Career Jumpstart was, like for me,

90

it definitely seemed like a, like it

is what you make it situation, what

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you were, you know, providing for

all of us and what you're selling us.

92

And I never thought that like a master's

degree was bigger than anything else.

93

I think you can learn in any capacity.

94

And for where I was at the time,

like I had been stuck in tutorial

95

hell for like, like months on end.

96

So I definitely felt like Data

Career Jumpstart was something

97

that was gonna allow me to just…

98

For the way my brain worked, like

it was just gonna allow me to

99

move, like move the right way.

100

I didn't have to know

everything all at once.

101

I didn't have to, you know, know

the whole world and, and memorize

102

every formula, every function,

every concept right then and there.

103

You know, you kind of preached that

to us a lot, and I think that was like

104

the big thing for me that sold it.

105

Like I kind- I remember like

watching the, um, like the prelude

106

before even like enrolling.

107

I was sitting there like contemplating

like if I even wanted to do it, if

108

it was gonna be the right investment,

and it definitely was looking back.

109

But yeah, Data Career Jumpstart to

me was just, it really worked for

110

how I am as a person in my brain.

111

Like, I'm somebody who kind of needs

good structure and, um, I don't…

112

Now I think it's a lot different.

113

Like, I don't mind being off

the rails or trying to figure

114

out something out of nothing.

115

But at the time, like structure

for me was huge, and that's

116

what Data Career Jumpstart was.

117

So yeah, that's a good point.

118

So in the tutorial hell, you

were doing, you were trying to

119

learn a bunch of things- Yeah

120

uh, online.

121

Like how long were you doing that for,

and where were you kind of learning

122

those things, or, or what were you doing?

123

It was mainly Udemy.

124

I was just…

125

I didn't even know what type

of role I wanted to pursue yet.

126

So at first I started with like DBA stuff,

so I just tried to learn SQL in general,

127

and then it applied to like some like

different little projects of like setting

128

up a database and setting up users and

roles and granting access and permissions.

129

And then I kind of slowly went to

analytics, but it seemed harder at

130

first because a lot of people on

social media were pushing, you know,

131

"You're top 1% if you know Python.

132

You're top 1% if you

know Python and Power BI.

133

You're top 1% if you know, if your

stack only grows to the highest it can."

134

But Data Career Jumpstart, you

know, obviously wasn't that.

135

Like it's kind of more of just kind

of like I said in that post, like

136

first get going, then get good.

137

Step by step, baby steps.

138

Yeah, like I was definitely trying

all type of different little stuff

139

though, mainly around SQL at the

beginning, 'cause that's the only

140

thing my brain can understand.

141

That's part of the problem these days,

is there's like, there's so much,

142

so many resources out there- Yeah

143

that it's like when you kind of

choose your own adventure, uh, you

144

could end up basically just going

in circles over and over and over

145

again, not really making any progress-

Yeah … 'cause it's like so many options.

146

And then it's like, "Oh, no,"

like- Someone just gives you like,

147

"Hey, do this and then this and

then this and then this and then

148

this and then this and then this."

149

And that's, that's like, oh, and then

you look back, oh, I made progress.

150

I made more progress, you know, over

these last 12 weeks, which is one of

151

the things I wanna mention because I

actually found a roadmap that we, that I

152

made for you when you joined the program.

153

And on that roadmap, uh, one of the

things, you know, kind of the…

154

I kind of gave you some milestones, which

was basically, you know, you'd study.

155

We talked together and we were like,

156

3:00 every day, so you're gonna try to

157

Mm-hmm.

158

And basically, the program will take

you about 10 weeks if that's the case.

159

And, you know, you started mid-June,

early June, and then you'd be

160

done by, you know, mid-August.

161

And then, you know- Yeah … come

September, you actually had a job offer.

162

So it's like you made some serious

progress in, in those 12 weeks to go from

163

feeling like you were stuck in tutorial

hell to actually landing a job offer.

164

I wanna talk about, you

know, how you found that job

165

because it's a tough market.

166

It's been a tough market for a while now.

167

So how did you find your first

data job amongst the sea of, you

168

know, thousands of data jobs?

169

My initial approach, regardless of the

role, was going to be if it felt right

170

for me, then apply and make sure I

reached out to someone, regardless of

171

the title or the company or whatever.

172

Just because, I don't know, I feel

like it's good to be personable

173

regardless of what the title is.

174

You never know what

you're actually a fit for.

175

But considering I was in the master's

program, like we kind of talked about, I

176

just kind of tried to get an internship.

177

Like, this was still a jump into

a new realm for me, so I, I didn't

178

feel like it was necessary to

just close off certain options.

179

I just kind of used LinkedIn, used

Glassdoor to my advantage, and applied

180

for anything I felt like was gonna be good

for me as far as base level experience.

181

And I just reached out to every

recruiter that was around the

182

department of the, you know, like

what I reached out for, like that…

183

Like if they were the HR for that

team or whatever, I just made sure I

184

reached out to them whenever I applied.

185

Like, if it wasn't that

same day, the next day.

186

And I just, yeah, like applied to

a lot of people, like probably 15

187

different people a day, but that's it.

188

Wow.

189

Or companies a day, yeah.

190

And, you know, I think the

turnover wasn't really that long.

191

It was probably like six weeks-

Yeah … from the time of me

192

finishing the boot camp or something.

193

Not, or where I was like at the

halfway point, like module six.

194

Yeah, basically from when you

joined, which in June, you started in

195

September, so Right … um, pretty,

you know, June, July, September,

196

so we're talking, you know, three,

three, four months, two, three months.

197

So that's absolutely amazing.

198

So you're applying for,

for like 15 jobs a day.

199

You're focused probably a little bit

more on internships than most people

200

because you still are a master's student.

201

Yeah.

202

We should say that you are

a part-time master's student

203

because you're working full-time.

204

You know, you're working 40 hours a week.

205

You're doing your master's

program, you know, o- on the side.

206

You're doing my program on the

side, so you're a busy guy.

207

You're applying to these jobs, and one

thing that you mentioned that I think

208

you're making it sound like it was no

duh, second nature to you, is reaching

209

out to these recruiters for these jobs,

and I think most people don't do that.

210

So tell me about what the process of like

reaching out to these recruiters was.

211

Why were you doing that

and what would you say?

212

Yeah so I mean, just applying, I

mean, everybody does that, you know?

213

Like that's…

214

It doesn't matter what your resume

looks like, that's only like so much.

215

Like there's a lot of people just

applying who may be a better fit than

216

you, and they still might get passed

up, or you may be a better fit than

217

them and they get, you know, a chance.

218

I just feel like it was really important

to be, to reach out and just, you know,

219

get your face and name in someone's eyes.

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Not that necessarily you get

a better chance because, you

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know, that might not even be…

222

It might just be a ghost job or…

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But it's just the fact of building

connection and learning how to talk

224

to people, which was huge for me.

225

That was a big part of the process.

226

Just applying felt like, like going

through the drive-through and like, you

227

know, somebody just hands you your food.

228

Like that's it.

229

There's nothing really after that.

230

Not that something needs to be

said, but in this case, I mean,

231

you never know what pops back up.

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It's more likely that they'll

point you in the right direction.

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For example, with Income, like the

person I reached out to was not the

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person I actually needed to talk to, but

he pointed me in the right direction.

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So I think stuff like

that is very important.

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You- Just applying is like, in my

opinion, it's very like just- Base level.

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Yeah, that's, that's…

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You can't really just do that No,

unfortunately in today's market you can't.

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It's a, it's a low bar to clear, and

especially now with AI, it's like people

240

can just auto apply to so many things,

and these jobs are just getting flooded.

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These ATS, ATS systems, that's

kind of a, an oxymoron 'cause

242

it's applicant tracking system.

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These applicant tracking systems are

getting flooded with candidates, and, uh,

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it's really hard to stand out if you're

just, you know, relying on your resume or

245

your LinkedIn to actually get you a job.

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So doing something proactive like reaching

out to a recruiter makes a lot of sense,

247

so that's really cool that you did that.

248

And so for this, you find

this incon- income job, this

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company that ends up hiring you.

250

You apply for it, and

did you message someone?

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You said you messaged someone.

252

You messaged the wrong person

for this particular job?

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It wasn't really the wrong person,

it was somebody who was on, uh, the

254

talent acquisition team, but they

were like a, they were a higher up.

255

They, like, directed me to the person

who was in charge of the intern program,

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and then it kind of went from there.

257

Uh, I talked to the intern program

person, her name is Jaylana, a couple

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days later actually, like that same…

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Or no, it was a Friday that I reached out.

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SJ is the person who responded to me.

261

He responded a couple minutes later, like

20 minutes later, and then like that next

262

Tuesday I had like a prelude interview,

just kinda get to know me, and then I

263

met with the actual manager of the team

I was interning for like later that week.

264

So the process itself was pretty fast

for what they were trying to do and

265

what I was trying to do, so yeah, it

was probably like a week Very cool.

266

I like that you reached out to someone.

267

And, uh, one of the strategies we

actually talk about in the boot camp

268

when we talk about the cold messaging

and how to send cold messages and

269

who to send cold messages to, it's

almost a good thing sometimes.

270

You have to get lucky, but it's

almost a good thing when you message-

271

Right … the wrong person and

you ask, "Who's the right person?"

272

'Cause then you can message the

right person and be like, "Hey,

273

this person told me to talk to you."

274

And then you're not only just like,

it's not exactly a cold message, it's

275

kind of a little bit warmer where

you, like, have a name to say- Yeah.

276

It's great … that they know, you know?

277

Yeah.

278

Right.

279

So that's cool that it

worked out that way.

280

What was the interview process like?

281

Was it difficult?

282

Was there lots of interviews?

283

Did they ask you really hard

questions, or was it kind of a little

284

bit easier than you maybe expected?

285

I wouldn't say it was easy.

286

I think it wasn't technical, though.

287

It was really, really, like,

a big personality thing, for

288

the internship especially.

289

They definitely knew

where I was at skill-wise.

290

You know, luckily, the cool part

was, like, having my portfolio.

291

I think that at least allowed me

to show something, considering the

292

interview wasn't super technical.

293

But it was very, very personable.

294

Like, my manager at the time, her

name is Lisa, she's on another team

295

now, she was very, very, like, adamant

about getting to know who I was

296

and the way I was answering stuff.

297

That was kind of the, the

whole interview process.

298

It, it was…

299

So yeah, it was pretty, like,

what would that be called?

300

Soft s- I forget.

301

Yeah.

302

Soft skills or- Yeah.

303

So- … behavioral interview.

304

Yeah.

305

Correct.

306

Yeah.

307

So that was the case there.

308

It wasn't really technical

considering it was a internship.

309

And then the, for the job I'm in now,

considering I was, like, already in the

310

company and I was just moving to another

team, it was, it was kind of the same way.

311

Yeah.

312

The manager even I have now,

he's great, and he's like, he

313

was really big on the same thing.

314

I think I just got really fortunate there.

315

But yeah, it was, it was behavioral

interviews for both of them.

316

I think a lot of people listening would

be surprised at how often that's the case

317

when you're landing your first data job.

318

Yeah.

319

A lot of them, I would say over 50%,

aren't really that technical at all.

320

Yeah.

321

And it's more behavioral,

especially if you have a portfolio.

322

Because really- Mm … when,

when someone's doing a technical

323

interview, they're trying to figure

out how skilled you are, you know?

324

Right.

325

Can you actually take a data set

and find meaningful insights,

326

you know, from that data set?

327

Right.

328

And when you give them a

portfolio, you kind of already

329

answered that question for them.

330

Yeah.

331

So it's like, ah, we, we, we know Cam

can actually, you know, use Tableau

332

or do some sort of a SQL query.

333

We're not as worried about that.

334

We're more worried, is he

going to be a good learner?

335

Is he gonna be a good fit for our team?

336

So I think it makes sense that you had

a lot of the, the behavioral interviews.

337

And then that is something

that we should mention.

338

So you, you landed this role.

339

It was a business intelligence analyst

internship with Income Payments.

340

You were there for, like, eight

months or something like that.

341

Is that right?

342

Yeah.

343

Okay So yeah.

344

And then just recently you got the

full-time job, because you're finishing

345

up school, as an inventory analyst.

346

Now, tell me the difference between

these roles 'cause inventory analyst,

347

some people might look at that and

be like, "I mean, it has the word

348

analyst in it, but it doesn't have

the word business intelligence.

349

It doesn't have the word data."

350

So I'm curious, like what did

you do broadly speaking as a BI

351

analyst, and what are you doing

kind of now as an inventory analyst?

352

So the BI analyst role was

very heavy in reporting.

353

We mainly used ServiceNow,

which was so interesting to me.

354

Did not expect to be using ServiceNow

and tickets and management,

355

IT process management, but it

was mainly through reporting.

356

Like, we just made sure reporting daily

was good for other parts of the company.

357

We used SQL to kind of like set up…

358

We have tables for what

is now a new report.

359

Like, basically all the tables were set up

for different reports in SQL, and we just

360

kind of maintained them as far as what was

getting sent out daily to different teams.

361

That was the gist of that whole role.

362

But inventory supply chain now,

like this inventory analyst

363

role, w- is more of supply chain.

364

My bad, I kind of misspoke.

365

But yeah, now it's mainly a

lot of auto replenishment.

366

So we keep up with everything

that's like on hand, on order, in

367

transit for different like stores

and the products at the stores.

368

So we work with account managers

across a bunch of different teams.

369

We have a bunch of different merchants

that I work with that I share with my

370

teammates as well, and it's, yeah, we just

keep up with the inventory of everything.

371

It's mainly just the upkeep

of auto replenishment.

372

So I know where, you know, everything's

being tracked as far as sales and

373

shipments going out for products

that have like out of stock.

374

Yeah, what you're trying to say, you're

doing inventory levels basically.

375

Yeah.

376

Basically, yeah.

377

Okay, sweet.

378

That's actually really cool because

when I worked at Exxon, I basically

379

only worked in supply chain essentially.

380

So i- there's lots of analytics to

be done in supply chain, keeping

381

track of where stuff is, where it's

from, where it's going next, lots

382

of analytics opportunities there.

383

In this new role, like what

type of tools are you using?

384

Are you still using a lot of

SQL or has it kind of changed?

385

It's changed a lot.

386

It's a lot, it's really heavy Excel, which

surprising enough I did not thought…

387

I, I knew way less of Excel than

I thought I did from the time now.

388

And we also use a order management

system, so it's not super technical.

389

It- I think it will be

though in the future.

390

My manager's definitely

pushing for that, which I love.

391

But right now it's, it's very,

uh, conceptual, and the order

392

management system is like the

thing we use like the most.

393

I touch it like daily w- outside of Excel.

394

And then I u- we use SQL for

a few things, mostly like data

395

auditing, um, and data checks.

396

Um, but that's really it.

397

That's like my current stack.

398

I think cr- like as we move

forward, I just kind of talked

399

about it with my team earlier this

week that Power BI and using…

400

Oh, we use AI a lot too

obviously, uh, like Copilot.

401

We use Copilot a lot, um, for automation.

402

But yeah, that's kind of really

where it is right now, like for me.

403

But it's gonna change in the near

future, which I'm excited about.

404

Very cool.

405

Yeah, I think people would be surprised

as well how much of like, I don't want

406

to call them internal tools, we'll

call them like third-party external

407

vendor tools, niche specific tools

that people use for, for analytics.

408

Yeah.

409

A lot of people have gone out there

and, you know, created analytics

410

platforms for specific verticals like

gift card inventory management or oil

411

barrel- management, and you use a lot

of those tools a- as well as, like,

412

some of the basic ones like Excel.

413

So I think that makes a lot of sense that,

that you're using those t- those tools.

414

What do you feel like you've learned in

your first year of being a data analyst?

415

What are some things that maybe

surprised you, didn't necessarily expect?

416

I kind of forgot that, you

know, having the concepts down

417

was gonna be super important.

418

When I first came in, it was

just, like, everything's gonna

419

be like, "Do it this way.

420

Do it this way, do it this way."

421

Like, you never have to…

422

I never really initially realized I

would have to, like, learn on the fly.

423

As far as combining the

business with what I do know

424

technically, that was, like, huge.

425

Like, I kind of focused more on

that in the past year than trying

426

to, like, learn new skills.

427

Because I feel like a data skill,

or not a data skill, but, like,

428

a technical skill is easier to

pick up if you know the business.

429

So that's something I'm, like,

still actively working on,

430

but that part was, like, huge.

431

Like, the people that I've, and l- that

I've encountered, like, so far are very,

432

very, like, in tune with the business.

433

Like, they know it almost better

than whatever ad hoc requests or

434

tasks they're being asked to do.

435

That's the point I'm trying to get to.

436

That was what surprised me the most.

437

That's, like, almost more important than

like, learning how to use something.

438

It's crazy that's the case, but,

like, your domain knowledge matters so

439

much more than your technical skills.

440

Yeah.

441

Um, I've told this story probably

100 times on the podcast now, but

442

when I was at Exxon, I used to enter

these hackathon competitions where

443

you'd compete against everyone in

the company to analyze a data set.

444

Oh, yeah.

445

They would just crowdsource it.

446

I watch them a lot, so I've

heard you talk about this.

447

Well, sorry, sorry for

boring you No, no, no.

448

It's funny.

449

Yeah … but basically, I, I won one

of them- Yes … and not because I

450

was the best data scientist at the

company, 'cause I definitely was not

451

the best data scientist at the company.

452

I was not the smartest.

453

You know, I didn't have a

PhD in computer science.

454

Yeah.

455

I didn't have a PhD in, in mathematics.

456

But I understood the business,

'cause I was a chemical engineer.

457

Uh, so I understood the

business pretty well.

458

Um, I had another friend, uh,

hire- who's a hiring manager

459

now, and he was hiring recently.

460

He narrowed it down to two candidates,

one that had way more, you know, data

461

experience than the other candidate.

462

But the other candidate had

the domain background, and he

463

went with the domain candidate.

464

And so it's just like once

you get to the industry, your

465

skills are obviously important.

466

You have a baseline of competency

to actually do analysis.

467

Yeah.

468

But if you c- like you said, bridging your

technical skills with, like, your business

469

understanding, if you can do that, I

think that's what really sets you apart.

470

So I'm glad to hear that

that's, like, what you've been

471

focusing over the last year.

472

I think that's gonna bring

dividends to your career.

473

I think it's gonna bring

dividends to your company as well.

474

Because it's like we don't do data

analysis for data analysis sake.

475

We're not doing it for funsies.

476

It's, it's- Right

477

to make an impact on, you know,

our products, our customers, save

478

lives, whatever the use case is.

479

So that makes, that makes a lot of

sense that you've been focusing on that.

480

I think it's gonna really

pay off for you well.

481

What advice would you give to maybe

someone that was sitting in your

482

shoes, you know, uh, a year ago you

hadn't joined the accelerator yet.

483

You were thinking about it.

484

You'd maybe watched a few of the YouTube

videos or podcasts or something like that.

485

What would you say to someone that was,

like, the younger version of Cam before

486

they joined the boot camp, you know,

before they landed their first data job?

487

If there's a young Cam out

there listening right now, what

488

advice would you give them?

489

I would just say challenge yourself.

490

If you think about, you know, what

you want your life to look like,

491

the type of people you wanna hang

around, what you wanna be doing day

492

to day, like, that's, that's the

type of stuff I was thinking about.

493

I grew up playing sports, so it was

just really a thing about, like,

494

always trying to just get better at

something Tech was like the, we're not

495

even tech, but data now I would say

was like just the one thing outside

496

of sports that I felt like I could

really try to like just get better at.

497

And you know, that comes with being

around the right people or trying

498

to be around the right people at

least, and having someone push you.

499

I would just say maybe put yourself in a

future bubble of what that would look like

500

and just make action to whatever that is.

501

Even if it's not data, if it's

finance, hos- nursing, whatever,

502

it's definitely gonna take another

level, and it's gonna take you

503

getting outside of your comfort zone.

504

So just picture yourself doing

something you've never done before

505

that's really hard, I guess is

the best way I would say it.

506

That's really it, like that one sentence.

507

Yeah.

508

'Cause that's what it's gonna take.

509

I'm feeling, I'm feeling a bit

hyped 'cause it's like, you know,

510

go out there, picture what you

want your future to look like.

511

For you, like, you know, growing up in the

sports world and, um, you know, studying

512

sports management and, you know, playing

some college sports- Mm … there's

513

not probably a lot of them out there

who are like, "Yeah, I wanna get into

514

like data and tech type of a thing."

515

So- Right … you had to be like,

"Okay, I know my current world

516

and understand what's around

me, but I have to think bigger.

517

I wanna be like, okay, this is

what I want my life to look like."

518

And then I love what you said.

519

I wish I could, I could remember

exactly what you said, but you're

520

like, you have to be around the people

that are gonna help you get there.

521

And- Yeah … you said something

like, "Get a coach, basically,

522

that's gonna get you there."

523

And I hope that your master's degree and

the accelerator was kind of that where

524

it's like you have a, a path to follow,

you have peers to, to follow it with.

525

You have people to push you, people

to hold you a little bit accountable.

526

And ultimately, you reached your goal.

527

You did exactly what you said you

were gonna do, and it was hard work.

528

You had to put in the hours, right?

529

But you- Yeah … ultimately let it.

530

And for you, has it been worth it, do you

feel like, this, this whole transition?

531

Yeah, 100%.

532

There's still a long way to go, obviously.

533

100%.

534

I loved it.

535

I loved even just, I loved

even being stuck in tutorial

536

hell, looking back at it.

537

It was just new, you know?

538

Yeah, it was great.

539

When I first started trying to learn in

general, like I remember like being in

540

the library for like hours on end, like

falling asleep trying to learn something

541

because it was just new and my body wasn't

used to even, even sitting down, you know?

542

Like I gotta move around.

543

I'm fidgety with my hands.

544

I gotta…

545

So I had to get comfortable with that,

and once I got comfortable with that part,

546

just like you move on to something else

new, you know, you've never seen before.

547

But the whole journey in itself so far

has definitely been worth it, 100%.

548

Okay.

549

What's next for you?

550

Like, in terms of, of your

career and growing, what are you

551

kind of focused on right now?

552

I say now that I am a little

better with the business acumen,

553

I do wanna become more technical.

554

I've already kind of started in

the background on outside of work.

555

It's mainly just been getting

better at using prompting in

556

general, but now I'm kind of…

557

I finally can say now I understand,

like, the basics of Python.

558

I think learning Excel, like, in

a actual real-world setting helped

559

with that as far as the logic of it.

560

But overall, now becoming more

technical, kind of building my

561

stack is most important to me.

562

Python, Power BI, kind of fill

in the gaps where need to.

563

And, uh, I've gotten better

about prompting too, though,

564

but basically just getting more

technical to kind of supplement now.

565

Yeah, that's really it.

566

I think that's a, that's a great choice.

567

You know, I love the fact that,

like, first off, Python is infinitely

568

large to, to learn, so I love…

569

We could all get better at Python,

but I also love that, like, you

570

didn't, like, wait to become a

Python expert to start applying

571

for jobs, 'cause you don't need it.

572

Everyone, newsflash, you definitely

don't need to know Python

573

to land your first data job.

574

So I think that's great, you know,

going back and revisiting some of

575

the Python, getting better at that.

576

And I, I agree with you that Python

and coding at the end of the day is

577

just getting logic in the right syntax

and thinking logically and getting

578

it in the right language, basically.

579

And so any, like, working

in Excel for a long time can

580

help you get better at Python.

581

Um, so I think that makes a lot of sense.

582

And then I, I like the last thing

that you mentioned of just, like,

583

how do you tie it all in with AI,

because AI is definitely here.

584

It is here to stay.

585

I don't think, personally, I don't

think it's here to take our jobs,

586

but I think we need to be good

at using AI, um- One system, yes

587

to improve our jobs.

588

So I think that career path

makes sense for, for you, and

589

I think I'm pretty excited.

590

I think you're gonna go great

places 'cause, like you said,

591

you got enough of the technical.

592

I think you're a fantastic communicator.

593

I think you learn a lot

about the business quickly.

594

I got big hopes and, uh, I got big

visions for you, uh, and comment

595

for the rest of your career, man.

596

So I appreciate you coming on the Data

Career Podcast and sharing your story.

597

We'll have a link to your LinkedIn

in the show notes down below if

598

you guys wanna reach out to Cam.

599

Is that okay if they reach out, Cam?

600

Yeah, of course.

601

Yeah.

602

Okay.

603

Perfect.

604

We'll have your LinkedIn down

there, and thanks so much for

605

coming and sharing your story, man.

606

We really appreciate it.