218: This Retail Worker Became a Data Analyst DESPITE No Experience

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I've been at Home Depot

for about five years.

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I had been stuck in tutorial hell

for, like, like, months on end, so…

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And I just, yeah, like, applied

to a lot of people, like, probably

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15 different people a day.

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Come September, you

actually had a job offer.

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You're top 1% if you know Python.

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But, like, your domain knowledge matters

so much more than your technical skills.

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

feel like, this, this whole transition?

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Yeah, 100%.

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This is Cam, and a year ago, he was

bouncing around a Home Depot, five

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years deep, a sports management degree,

and absolutely zero data experience.

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And he was stuck in what he calls tutorial

hell, months of online courses, just going

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in circles, making no real, true progress.

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And then he joined my boot camp, the

Data Analytics Accelerator, in June, and

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by September, he had a data job offer.

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No fancy CS degree, no years of

experience, and in one of the

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toughest markets we's ever seen.

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And he still did it in about three months.

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In this conversation, he breaks down

exactly how he found the job, the one

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outreach move that he did that no one else

does that got him in the door, and the

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thing that surprised him the most about

actually landing a data role, and it has

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nothing to do with how technical he was.

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Let's go ahead and get into it.

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All right, Cam, you are now

an inventory analyst at Incon,

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but you didn't start that way.

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You had some other jobs before

you became a data analyst.

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Tell us a little bit about what you

were doing before you joined my boot

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camp, before you became a data analyst.

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What were you kind of doing for work?

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So, um, yeah, I was at Home…

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I'd been at Home Depot

for about five years.

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I was just jumping around the store.

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I was in freight.

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I was in customer service.

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Basically anywhere they

needed me or I wanted to be.

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And then, 2024, I joined grad school,

uh, for master's IT after graduating from

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Kennesaw with a sports management degree.

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And, uh, yeah, I mean, that

was really the gist of it.

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I just, once I graduated from

sports management, it just didn't

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feel like a, the right fit for me.

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I, I don't think I challenged

myself enough in undergrad.

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The stuff I ended up applying for

anyway was, like, similar to being in

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an office or being in IT, so I just kind

of pushed myself to the limits and just

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got a degree in something completely

outside of my realm and just considered

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it to be a huge learning curve, so.

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But the whole time, yeah, I

was at Home Depot working.

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Okay, that's amazing.

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So you graduate college with a

sports management degree, and, uh,

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that was kind of your background.

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You did- you play a little, uh,

collegiate football in college.

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You were kind of in the sports world.

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You graduate, and you're like,

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

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Maybe not one of these sports jobs, so

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

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You enroll in a master's

program in January of that year.

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You know, obviously you're

at Home Depot at the time.

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And for those who are not familiar

with Home Depot, it's kind of like

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a home goods, like, a hardware,

a get-your-stuff-done store.

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How was that?

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Like, did you like working there?

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Did you like the job you were doing?

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Did you want to leave?

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Obviously, you wanted to leave a

little bit 'cause you were, you

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know, pursuing these, these degrees.

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I think it was more of

just being- I don't know.

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I did wanna leave, but I just

wanted to do something…

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I, you know, I pictured my life a certain

way as far as just consistency and

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living a certain way and, and working

in a certain consistency as well.

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Tech and the people around me have

allowed that, being in data especially.

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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,

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I can't complain about that.

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But yeah, I definitely did want to,

uh, be where I am now It makes sense

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because, you know, yeah, once again, like

nothing wrong with Home Depot at all,

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but obviously it's a very different…

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It's not a desk job.

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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.

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Okay, so you- you're

working at Home Depot.

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You enroll in this master's program

in, in January, and then come June or

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July you end up enrolling in the Data

Analytics Accelerator, my boot camp.

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I'm curious like why you made that

decision, 'cause a lot of people

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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."

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So I'm curious why you thought maybe the

boot camp was a good decision for you.

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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.

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And the coolest thing about Data

Career Jumpstart was, like for me,

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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.

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And I never thought that like a master's

degree was bigger than anything else.

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I think you can learn in any capacity.

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And for where I was at the time,

like I had been stuck in tutorial

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hell for like, like months on end.

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So I definitely felt like Data

Career Jumpstart was something

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that was gonna allow me to just…

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For the way my brain worked, like

it was just gonna allow me to

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move, like move the right way.

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I didn't have to know

everything all at once.

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I didn't have to, you know, know

the whole world and, and memorize

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every formula, every function,

every concept right then and there.

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You know, you kind of preached that

to us a lot, and I think that was like

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the big thing for me that sold it.

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Like I kind- I remember like

watching the, um, like the prelude

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before even like enrolling.

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I was sitting there like contemplating

like if I even wanted to do it, if

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it was gonna be the right investment,

and it definitely was looking back.

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But yeah, Data Career Jumpstart to

me was just, it really worked for

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how I am as a person in my brain.

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Like, I'm somebody who kind of needs

good structure and, um, I don't…

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Now I think it's a lot different.

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Like, I don't mind being off

the rails or trying to figure

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out something out of nothing.

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But at the time, like structure

for me was huge, and that's

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what Data Career Jumpstart was.

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So yeah, that's a good point.

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So in the tutorial hell, you

were doing, you were trying to

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learn a bunch of things- Yeah

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uh, online.

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Like how long were you doing that for,

and where were you kind of learning

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those things, or, or what were you doing?

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It was mainly Udemy.

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I was just…

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I didn't even know what type

of role I wanted to pursue yet.

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So at first I started with like DBA stuff,

so I just tried to learn SQL in general,

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and then it applied to like some like

different little projects of like setting

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up a database and setting up users and

roles and granting access and permissions.

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And then I kind of slowly went to

analytics, but it seemed harder at

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first because a lot of people on

social media were pushing, you know,

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

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You're top 1% if you

know Python and Power BI.

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

stack only grows to the highest it can."

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But Data Career Jumpstart, you

know, obviously wasn't that.

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Like it's kind of more of just kind

of like I said in that post, like

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first get going, then get good.

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Step by step, baby steps.

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Yeah, like I was definitely trying

all type of different little stuff

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though, mainly around SQL at the

beginning, 'cause that's the only

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thing my brain can understand.

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That's part of the problem these days,

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

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so many resources out there- Yeah

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that it's like when you kind of

choose your own adventure, uh, you

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could end up basically just going

in circles over and over and over

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again, not really making any progress-

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

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

like- Someone just gives you like,

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"Hey, do this and then this and

then this and then this and then

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this and then this and then this."

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

you look back, oh, I made progress.

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I made more progress, you know, over

these last 12 weeks, which is one of

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the things I wanna mention because I

actually found a roadmap that we, that I

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made for you when you joined the program.

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And on that roadmap, uh, one of the

things, you know, kind of the…

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I kind of gave you some milestones, which

was basically, you know, you'd study.

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We talked together and we were like,

"Okay, you can study from: 11:00

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

study every day from: 11:00

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Mm-hmm.

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And basically, the program will take

you about 10 weeks if that's the case.

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And, you know, you started mid-June,

early June, and then you'd be

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done by, you know, mid-August.

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And then, you know- Yeah … come

September, you actually had a job offer.

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So it's like you made some serious

progress in, in those 12 weeks to go from

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feeling like you were stuck in tutorial

hell to actually landing a job offer.

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I wanna talk about, you

know, how you found that job

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because it's a tough market.

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

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So how did you find your first

data job amongst the sea of, you

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know, thousands of data jobs?

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My initial approach, regardless of the

role, was going to be if it felt right

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for me, then apply and make sure I

reached out to someone, regardless of

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the title or the company or whatever.

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Just because, I don't know, I feel

like it's good to be personable

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regardless of what the title is.

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You never know what

you're actually a fit for.

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But considering I was in the master's

program, like we kind of talked about, I

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just kind of tried to get an internship.

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Like, this was still a jump into

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

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feel like it was necessary to

just close off certain options.

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I just kind of used LinkedIn, used

Glassdoor to my advantage, and applied

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for anything I felt like was gonna be good

for me as far as base level experience.

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And I just reached out to every

recruiter that was around the

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department of the, you know, like

what I reached out for, like that…

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Like if they were the HR for that

team or whatever, I just made sure I

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reached out to them whenever I applied.

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Like, if it wasn't that

same day, the next day.

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And I just, yeah, like applied to

a lot of people, like probably 15

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different people a day, but that's it.

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Wow.

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Or companies a day, yeah.

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And, you know, I think the

turnover wasn't really that long.

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It was probably like six weeks-

Yeah … from the time of me

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finishing the boot camp or something.

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Not, or where I was like at the

halfway point, like module six.

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Yeah, basically from when you

joined, which in June, you started in

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September, so Right … um, pretty,

you know, June, July, September,

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so we're talking, you know, three,

three, four months, two, three months.

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So that's absolutely amazing.

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So you're applying for,

for like 15 jobs a day.

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You're focused probably a little bit

more on internships than most people

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because you still are a master's student.

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Yeah.

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We should say that you are

a part-time master's student

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because you're working full-time.

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

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You're doing your master's

program, you know, o- on the side.

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You're doing my program on the

side, so you're a busy guy.

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You're applying to these jobs, and one

thing that you mentioned that I think

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you're making it sound like it was no

duh, second nature to you, is reaching

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out to these recruiters for these jobs,

and I think most people don't do that.

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So tell me about what the process of like

reaching out to these recruiters was.

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Why were you doing that

and what would you say?

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Yeah so I mean, just applying, I

mean, everybody does that, you know?

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Like that's…

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It doesn't matter what your resume

looks like, that's only like so much.

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Like there's a lot of people just

applying who may be a better fit than

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you, and they still might get passed

up, or you may be a better fit than

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them and they get, you know, a chance.

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I just feel like it was really important

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

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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…

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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

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to people, which was huge for me.

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That was a big part of the process.

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Just applying felt like, like going

through the drive-through and like, you

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know, somebody just hands you your food.

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Like that's it.

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There's nothing really after that.

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Not that something needs to be

said, but in this case, I mean,

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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

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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

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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

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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,

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so that's really cool that you did that.

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And so for this, you find

this incon- income job, this

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company that ends up hiring you.

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You apply for it, and

did you message someone?

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You said you messaged someone.

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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

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talent acquisition team, but they

were like a, they were a higher up.

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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.

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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.

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He responded a couple minutes later, like

20 minutes later, and then like that next

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Tuesday I had like a prelude interview,

just kinda get to know me, and then I

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met with the actual manager of the team

I was interning for like later that week.

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So the process itself was pretty fast

for what they were trying to do and

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what I was trying to do, so yeah, it

was probably like a week Very cool.

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I like that you reached out to someone.

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And, uh, one of the strategies we

actually talk about in the boot camp

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when we talk about the cold messaging

and how to send cold messages and

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who to send cold messages to, it's

almost a good thing sometimes.

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You have to get lucky, but it's

almost a good thing when you message-

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Right … the wrong person and

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

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'Cause then you can message the

right person and be like, "Hey,

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this person told me to talk to you."

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And then you're not only just like,

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

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kind of a little bit warmer where

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

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

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Yeah.

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Right.

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So that's cool that it

worked out that way.

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What was the interview process like?

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Was it difficult?

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Was there lots of interviews?

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Did they ask you really hard

questions, or was it kind of a little

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bit easier than you maybe expected?

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I wouldn't say it was easy.

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I think it wasn't technical, though.

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It was really, really, like,

a big personality thing, for

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the internship especially.

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They definitely knew

where I was at skill-wise.

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You know, luckily, the cool part

was, like, having my portfolio.

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I think that at least allowed me

to show something, considering the

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interview wasn't super technical.

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But it was very, very personable.

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Like, my manager at the time, her

name is Lisa, she's on another team

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now, she was very, very, like, adamant

about getting to know who I was

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and the way I was answering stuff.

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That was kind of the, the

whole interview process.

298

: 00:12:29

It, it was…

299

: 00:12:29

So yeah, it was pretty, like,

what would that be called?

300

: 00:12:31

Soft s- I forget.

301

: 00:12:32

Yeah.

302

: 00:12:32

Soft skills or- Yeah.

303

: 00:12:34

So- … behavioral interview.

304

: 00:12:34

Yeah.

305

: 00:12:34

Correct.

306

: 00:12:35

Yeah.

307

: 00:12:35

So that was the case there.

308

: 00:12:36

It wasn't really technical

considering it was a internship.

309

: 00:12:38

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

considering I was, like, already in the

310

: 00:12:41

company and I was just moving to another

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

311

: 00:12:45

Yeah.

312

: 00:12:46

The manager even I have now,

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

313

: 00:12:49

was really big on the same thing.

314

: 00:12:50

I think I just got really fortunate there.

315

: 00:12:52

But yeah, it was, it was behavioral

interviews for both of them.

316

: 00:12:55

I think a lot of people listening would

be surprised at how often that's the case

317

: 00:12:59

when you're landing your first data job.

318

: 00:13:00

Yeah.

319

: 00:13:00

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

aren't really that technical at all.

320

: 00:13:04

Yeah.

321

: 00:13:04

And it's more behavioral,

especially if you have a portfolio.

322

: 00:13:07

Because really- Mm … when,

when someone's doing a technical

323

: 00:13:09

interview, they're trying to figure

out how skilled you are, you know?

324

: 00:13:12

Right.

325

: 00:13:12

Can you actually take a data set

and find meaningful insights,

326

: 00:13:15

you know, from that data set?

327

: 00:13:16

Right.

328

: 00:13:17

And when you give them a

portfolio, you kind of already

329

: 00:13:19

answered that question for them.

330

: 00:13:20

Yeah.

331

: 00:13:20

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

can actually, you know, use Tableau

332

: 00:13:23

or do some sort of a SQL query.

333

: 00:13:25

We're not as worried about that.

334

: 00:13:26

We're more worried, is he

going to be a good learner?

335

: 00:13:28

Is he gonna be a good fit for our team?

336

: 00:13:29

So I think it makes sense that you had

a lot of the, the behavioral interviews.

337

: 00:13:32

And then that is something

that we should mention.

338

: 00:13:34

So you, you landed this role.

339

: 00:13:35

It was a business intelligence analyst

internship with Income Payments.

340

: 00:13:39

You were there for, like, eight

months or something like that.

341

: 00:13:42

Is that right?

342

: 00:13:43

Yeah.

343

: 00:13:44

Okay So yeah.

344

: 00:13:44

And then just recently you got the

full-time job, because you're finishing

345

: 00:13:48

up school, as an inventory analyst.

346

: 00:13:50

Now, tell me the difference between

these roles 'cause inventory analyst,

347

: 00:13:53

some people might look at that and

be like, "I mean, it has the word

348

: 00:13:55

analyst in it, but it doesn't have

the word business intelligence.

349

: 00:13:57

It doesn't have the word data."

350

: 00:13:58

So I'm curious, like what did

you do broadly speaking as a BI

351

: 00:14:02

analyst, and what are you doing

kind of now as an inventory analyst?

352

: 00:14:04

So the BI analyst role was

very heavy in reporting.

353

: 00:14:08

We mainly used ServiceNow,

which was so interesting to me.

354

: 00:14:12

Did not expect to be using ServiceNow

and tickets and management,

355

: 00:14:15

IT process management, but it

was mainly through reporting.

356

: 00:14:17

Like, we just made sure reporting daily

was good for other parts of the company.

357

: 00:14:21

We used SQL to kind of like set up…

358

: 00:14:22

We have tables for what

is now a new report.

359

: 00:14:26

Like, basically all the tables were set up

for different reports in SQL, and we just

360

: 00:14:29

kind of maintained them as far as what was

getting sent out daily to different teams.

361

: 00:14:33

That was the gist of that whole role.

362

: 00:14:35

But inventory supply chain now,

like this inventory analyst

363

: 00:14:39

role, w- is more of supply chain.

364

: 00:14:40

My bad, I kind of misspoke.

365

: 00:14:41

But yeah, now it's mainly a

lot of auto replenishment.

366

: 00:14:44

So we keep up with everything

that's like on hand, on order, in

367

: 00:14:48

transit for different like stores

and the products at the stores.

368

: 00:14:52

So we work with account managers

across a bunch of different teams.

369

: 00:14:55

We have a bunch of different merchants

that I work with that I share with my

370

: 00:14:58

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

keep up with the inventory of everything.

371

: 00:15:02

It's mainly just the upkeep

of auto replenishment.

372

: 00:15:05

So I know where, you know, everything's

being tracked as far as sales and

373

: 00:15:08

shipments going out for products

that have like out of stock.

374

: 00:15:11

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

doing inventory levels basically.

375

: 00:15:13

Yeah.

376

: 00:15:13

Basically, yeah.

377

: 00:15:15

Okay, sweet.

378

: 00:15:15

That's actually really cool because

when I worked at Exxon, I basically

379

: 00:15:19

only worked in supply chain essentially.

380

: 00:15:21

So i- there's lots of analytics to

be done in supply chain, keeping

381

: 00:15:23

track of where stuff is, where it's

from, where it's going next, lots

382

: 00:15:27

of analytics opportunities there.

383

: 00:15:29

In this new role, like what

type of tools are you using?

384

: 00:15:31

Are you still using a lot of

SQL or has it kind of changed?

385

: 00:15:34

It's changed a lot.

386

: 00:15:35

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

surprising enough I did not thought…

387

: 00:15:39

I, I knew way less of Excel than

I thought I did from the time now.

388

: 00:15:42

And we also use a order management

system, so it's not super technical.

389

: 00:15:47

It- I think it will be

though in the future.

390

: 00:15:49

My manager's definitely

pushing for that, which I love.

391

: 00:15:51

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

uh, conceptual, and the order

392

: 00:15:55

management system is like the

thing we use like the most.

393

: 00:15:57

I touch it like daily w- outside of Excel.

394

: 00:15:59

And then I u- we use SQL for

a few things, mostly like data

395

: 00:16:03

auditing, um, and data checks.

396

: 00:16:05

Um, but that's really it.

397

: 00:16:06

That's like my current stack.

398

: 00:16:07

I think cr- like as we move

forward, I just kind of talked

399

: 00:16:10

about it with my team earlier this

week that Power BI and using…

400

: 00:16:13

Oh, we use AI a lot too

obviously, uh, like Copilot.

401

: 00:16:15

We use Copilot a lot, um, for automation.

402

: 00:16:17

But yeah, that's kind of really

where it is right now, like for me.

403

: 00:16:20

But it's gonna change in the near

future, which I'm excited about.

404

: 00:16:23

Very cool.

405

: 00:16:24

Yeah, I think people would be surprised

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

406

: 00:16:27

to call them internal tools, we'll

call them like third-party external

407

: 00:16:30

vendor tools, niche specific tools

that people use for, for analytics.

408

: 00:16:34

Yeah.

409

: 00:16:34

A lot of people have gone out there

and, you know, created analytics

410

: 00:16:37

platforms for specific verticals like

gift card inventory management or oil

411

: 00:16:43

barrel- management, and you use a lot

of those tools a- as well as, like,

412

: 00:16:46

some of the basic ones like Excel.

413

: 00:16:48

So I think that makes a lot of sense that,

that you're using those t- those tools.

414

: 00:16:51

What do you feel like you've learned in

your first year of being a data analyst?

415

: 00:16:55

What are some things that maybe

surprised you, didn't necessarily expect?

416

: 00:16:58

I kind of forgot that, you

know, having the concepts down

417

: 00:17:02

was gonna be super important.

418

: 00:17:03

When I first came in, it was

just, like, everything's gonna

419

: 00:17:05

be like, "Do it this way.

420

: 00:17:07

Do it this way, do it this way."

421

: 00:17:08

Like, you never have to…

422

: 00:17:09

I never really initially realized I

would have to, like, learn on the fly.

423

: 00:17:13

As far as combining the

business with what I do know

424

: 00:17:16

technically, that was, like, huge.

425

: 00:17:18

Like, I kind of focused more on

that in the past year than trying

426

: 00:17:21

to, like, learn new skills.

427

: 00:17:23

Because I feel like a data skill,

or not a data skill, but, like,

428

: 00:17:26

a technical skill is easier to

pick up if you know the business.

429

: 00:17:30

So that's something I'm, like,

still actively working on,

430

: 00:17:32

but that part was, like, huge.

431

: 00:17:34

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

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

432

: 00:17:38

very, like, in tune with the business.

433

: 00:17:40

Like, they know it almost better

than whatever ad hoc requests or

434

: 00:17:44

tasks they're being asked to do.

435

: 00:17:45

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

436

: 00:17:47

That was what surprised me the most.

437

: 00:17:49

That's, like, almost more important than

like, learning how to use something.

438

: 00:17:52

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

like, your domain knowledge matters so

439

: 00:17:56

much more than your technical skills.

440

: 00:17:57

Yeah.

441

: 00:17:58

Um, I've told this story probably

100 times on the podcast now, but

442

: 00:18:00

when I was at Exxon, I used to enter

these hackathon competitions where

443

: 00:18:04

you'd compete against everyone in

the company to analyze a data set.

444

: 00:18:07

Oh, yeah.

445

: 00:18:07

They would just crowdsource it.

446

: 00:18:08

I watch them a lot, so I've

heard you talk about this.

447

: 00:18:10

Well, sorry, sorry for

boring you No, no, no.

448

: 00:18:13

It's funny.

449

: 00:18:13

Yeah … but basically, I, I won one

of them- Yes … and not because I

450

: 00:18:16

was the best data scientist at the

company, 'cause I definitely was not

451

: 00:18:19

the best data scientist at the company.

452

: 00:18:20

I was not the smartest.

453

: 00:18:21

You know, I didn't have a

PhD in computer science.

454

: 00:18:23

Yeah.

455

: 00:18:23

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

456

: 00:18:24

But I understood the business,

'cause I was a chemical engineer.

457

: 00:18:26

Uh, so I understood the

business pretty well.

458

: 00:18:28

Um, I had another friend, uh,

hire- who's a hiring manager

459

: 00:18:31

now, and he was hiring recently.

460

: 00:18:32

He narrowed it down to two candidates,

one that had way more, you know, data

461

: 00:18:36

experience than the other candidate.

462

: 00:18:37

But the other candidate had

the domain background, and he

463

: 00:18:39

went with the domain candidate.

464

: 00:18:41

And so it's just like once

you get to the industry, your

465

: 00:18:43

skills are obviously important.

466

: 00:18:44

You have a baseline of competency

to actually do analysis.

467

: 00:18:47

Yeah.

468

: 00:18:47

But if you c- like you said, bridging your

technical skills with, like, your business

469

: 00:18:51

understanding, if you can do that, I

think that's what really sets you apart.

470

: 00:18:54

So I'm glad to hear that

that's, like, what you've been

471

: 00:18:56

focusing over the last year.

472

: 00:18:57

I think that's gonna bring

dividends to your career.

473

: 00:18:59

I think it's gonna bring

dividends to your company as well.

474

: 00:19:01

Because it's like we don't do data

analysis for data analysis sake.

475

: 00:19:04

We're not doing it for funsies.

476

: 00:19:05

It's, it's- Right

477

: 00:19:06

to make an impact on, you know,

our products, our customers, save

478

: 00:19:09

lives, whatever the use case is.

479

: 00:19:10

So that makes, that makes a lot of

sense that you've been focusing on that.

480

: 00:19:13

I think it's gonna really

pay off for you well.

481

: 00:19:15

What advice would you give to maybe

someone that was sitting in your

482

: 00:19:18

shoes, you know, uh, a year ago you

hadn't joined the accelerator yet.

483

: 00:19:22

You were thinking about it.

484

: 00:19:23

You'd maybe watched a few of the YouTube

videos or podcasts or something like that.

485

: 00:19:26

What would you say to someone that was,

like, the younger version of Cam before

486

: 00:19:30

they joined the boot camp, you know,

before they landed their first data job?

487

: 00:19:33

If there's a young Cam out

there listening right now, what

488

: 00:19:36

advice would you give them?

489

: 00:19:37

I would just say challenge yourself.

490

: 00:19:39

If you think about, you know, what

you want your life to look like,

491

: 00:19:41

the type of people you wanna hang

around, what you wanna be doing day

492

: 00:19:43

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

type of stuff I was thinking about.

493

: 00:19:46

I grew up playing sports, so it was

just really a thing about, like,

494

: 00:19:49

always trying to just get better at

something Tech was like the, we're not

495

: 00:19:53

even tech, but data now I would say

was like just the one thing outside

496

: 00:19:56

of sports that I felt like I could

really try to like just get better at.

497

: 00:20:00

And you know, that comes with being

around the right people or trying

498

: 00:20:03

to be around the right people at

least, and having someone push you.

499

: 00:20:05

I would just say maybe put yourself in a

future bubble of what that would look like

500

: 00:20:09

and just make action to whatever that is.

501

: 00:20:11

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

finance, hos- nursing, whatever,

502

: 00:20:14

it's definitely gonna take another

level, and it's gonna take you

503

: 00:20:17

getting outside of your comfort zone.

504

: 00:20:18

So just picture yourself doing

something you've never done before

505

: 00:20:20

that's really hard, I guess is

the best way I would say it.

506

: 00:20:22

That's really it, like that one sentence.

507

: 00:20:24

Yeah.

508

: 00:20:24

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

509

: 00:20:26

I'm feeling, I'm feeling a bit

hyped 'cause it's like, you know,

510

: 00:20:27

go out there, picture what you

want your future to look like.

511

: 00:20:30

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

sports world and, um, you know, studying

512

: 00:20:33

sports management and, you know, playing

some college sports- Mm … there's

513

: 00:20:37

not probably a lot of them out there

who are like, "Yeah, I wanna get into

514

: 00:20:39

like data and tech type of a thing."

515

: 00:20:41

So- Right … you had to be like,

"Okay, I know my current world

516

: 00:20:44

and understand what's around

me, but I have to think bigger.

517

: 00:20:46

I wanna be like, okay, this is

what I want my life to look like."

518

: 00:20:48

And then I love what you said.

519

: 00:20:49

I wish I could, I could remember

exactly what you said, but you're

520

: 00:20:51

like, you have to be around the people

that are gonna help you get there.

521

: 00:20:54

And- Yeah … you said something

like, "Get a coach, basically,

522

: 00:20:57

that's gonna get you there."

523

: 00:20:57

And I hope that your master's degree and

the accelerator was kind of that where

524

: 00:21:01

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

you have peers to, to follow it with.

525

: 00:21:05

You have people to push you, people

to hold you a little bit accountable.

526

: 00:21:07

And ultimately, you reached your goal.

527

: 00:21:09

You did exactly what you said you

were gonna do, and it was hard work.

528

: 00:21:12

You had to put in the hours, right?

529

: 00:21:13

But you- Yeah … ultimately let it.

530

: 00:21:14

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

feel like, this, this whole transition?

531

: 00:21:17

Yeah, 100%.

532

: 00:21:18

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

533

: 00:21:20

100%.

534

: 00:21:21

I loved it.

535

: 00:21:21

I loved even just, I loved

even being stuck in tutorial

536

: 00:21:24

hell, looking back at it.

537

: 00:21:25

It was just new, you know?

538

: 00:21:26

Yeah, it was great.

539

: 00:21:27

When I first started trying to learn in

general, like I remember like being in

540

: 00:21:30

the library for like hours on end, like

falling asleep trying to learn something

541

: 00:21:34

because it was just new and my body wasn't

used to even, even sitting down, you know?

542

: 00:21:38

Like I gotta move around.

543

: 00:21:39

I'm fidgety with my hands.

544

: 00:21:40

I gotta…

545

: 00:21:40

So I had to get comfortable with that,

and once I got comfortable with that part,

546

: 00:21:43

just like you move on to something else

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

547

: 00:21:46

But the whole journey in itself so far

has definitely been worth it, 100%.

548

: 00:21:50

Okay.

549

: 00:21:50

What's next for you?

550

: 00:21:51

Like, in terms of, of your

career and growing, what are you

551

: 00:21:54

kind of focused on right now?

552

: 00:21:55

I say now that I am a little

better with the business acumen,

553

: 00:21:59

I do wanna become more technical.

554

: 00:22:01

I've already kind of started in

the background on outside of work.

555

: 00:22:05

It's mainly just been getting

better at using prompting in

556

: 00:22:07

general, but now I'm kind of…

557

: 00:22:08

I finally can say now I understand,

like, the basics of Python.

558

: 00:22:13

I think learning Excel, like, in

a actual real-world setting helped

559

: 00:22:17

with that as far as the logic of it.

560

: 00:22:19

But overall, now becoming more

technical, kind of building my

561

: 00:22:21

stack is most important to me.

562

: 00:22:23

Python, Power BI, kind of fill

in the gaps where need to.

563

: 00:22:26

And, uh, I've gotten better

about prompting too, though,

564

: 00:22:28

but basically just getting more

technical to kind of supplement now.

565

: 00:22:31

Yeah, that's really it.

566

: 00:22:33

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

567

: 00:22:34

You know, I love the fact that,

like, first off, Python is infinitely

568

: 00:22:38

large to, to learn, so I love…

569

: 00:22:39

We could all get better at Python,

but I also love that, like, you

570

: 00:22:42

didn't, like, wait to become a

Python expert to start applying

571

: 00:22:44

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

572

: 00:22:45

Everyone, newsflash, you definitely

don't need to know Python

573

: 00:22:47

to land your first data job.

574

: 00:22:49

So I think that's great, you know,

going back and revisiting some of

575

: 00:22:51

the Python, getting better at that.

576

: 00:22:52

And I, I agree with you that Python

and coding at the end of the day is

577

: 00:22:55

just getting logic in the right syntax

and thinking logically and getting

578

: 00:23:00

it in the right language, basically.

579

: 00:23:01

And so any, like, working

in Excel for a long time can

580

: 00:23:04

help you get better at Python.

581

: 00:23:05

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

582

: 00:23:06

And then I, I like the last thing

that you mentioned of just, like,

583

: 00:23:08

how do you tie it all in with AI,

because AI is definitely here.

584

: 00:23:12

It is here to stay.

585

: 00:23:13

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

think it's here to take our jobs,

586

: 00:23:15

but I think we need to be good

at using AI, um- One system, yes

587

: 00:23:18

to improve our jobs.

588

: 00:23:19

So I think that career path

makes sense for, for you, and

589

: 00:23:22

I think I'm pretty excited.

590

: 00:23:24

I think you're gonna go great

places 'cause, like you said,

591

: 00:23:26

you got enough of the technical.

592

: 00:23:27

I think you're a fantastic communicator.

593

: 00:23:28

I think you learn a lot

about the business quickly.

594

: 00:23:30

I got big hopes and, uh, I got big

visions for you, uh, and comment

595

: 00:23:35

for the rest of your career, man.

596

: 00:23:36

So I appreciate you coming on the Data

Career Podcast and sharing your story.

597

: 00:23:40

We'll have a link to your LinkedIn

in the show notes down below if

598

: 00:23:43

you guys wanna reach out to Cam.

599

: 00:23:44

Is that okay if they reach out, Cam?

600

: 00:23:45

Yeah, of course.

601

: 00:23:46

Yeah.

602

: 00:23:46

Okay.

603

: 00:23:46

Perfect.

604

: 00:23:47

We'll have your LinkedIn down

there, and thanks so much for

605

: 00:23:49

coming and sharing your story, man.

606

: 00:23:50

We really appreciate it.