138: Steven Tran’s 3-Month Journey to Becoming a Data Analyst

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All right.

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Very excited for today's episode.

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It's actually an interview I did with one

of my students, Stephen Tran, who is a

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member of the data analytics accelerator.

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Um, I, or.

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I've already published this.

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You guys have maybe heard this before,

but I really just wanted to highlight.

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How incredible Steven's journey was.

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And for those of you that are

new to the podcast, you might

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not have listened to this one.

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Um, because it was

quite a bit a while ago.

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So, um, And we just kinda went

through Steven's whole story of

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how he actually landed a data

job kind of step-by-step and.

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I thought this was a great episode.

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I just wanted to reshare it with

y'all if you haven't heard it.

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So, uh, let's get into today's episode.

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Welcome back to the Data Career podcast.

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I'm super excited for two.

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Today's episode, I'm doing an interview.

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With one of our DCJ Data Career

Jumpstart members, Steven Tran.

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And I'm super excited to have him

here and tell us about his story.

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Welcome to the Data Career Podcast,

the podcast that helps aspiring data

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professionals land their next data job.

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Here's your host, Avery Smith.

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So Steven, welcome to the podcast.

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Thank you, Avery.

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I love that you invited me on the podcast.

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Cause I don't know if you know

this, I've listened to every

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single episode of your podcast.

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Have you really?

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Yeah, it's actually helped me

a lot making my LinkedIn posts.

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So we'll talk about that.

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Awesome.

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Well, now you, I guess this is one

of the episodes you won't listen to.

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You don't have to listen to this one.

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I guess you can just, you can just be

in it and you can just talk about it.

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So super excited to have you.

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So let's start with, we're gonna

start with the big picture.

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Okay.

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So for those of you who don't know, which

is probably a lot of you guys, Steven.

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What was your title

before your current title?

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It was technical support analyst.

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Okay.

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And what type of company was that for?

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It was for a mortgage company

called Ellie May and they were

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acquired by ice mortgage technology.

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So that's what they're known as now.

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Okay.

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So you were kind of working in this like

mortgage company doing a little bit of.

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Of it work.

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Is that right?

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Or yeah, I, I just call it a

glorified tech support job.

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Okay, sweet.

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So like, was that like making sure people

like had PowerPoint working correctly

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or like, what was like a daily task?

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Yeah.

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So it was a little bit more than that

because what I was doing, I was giving

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like API support, so we have our program

called encompass where people can, our

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mortgage loan officers can go through and

manage their loans and stuff, but we also

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we allow them to create their own code.

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So I would help debug that

code for them basically.

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So I would have tickets and whatnot that

I'd have to go through and follow up and

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you know, and all that stuff like that,

but yeah, basically a tech support role.

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Okay.

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So from a tech support role

to now, I think you're.

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Title is, I'm going to read this,

Senior Associate in Analytics, right?

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Yep.

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That's correct.

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Okay.

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At Dentsu, which is like a, like a

big media marketing company, right?

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Mm hmm.

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That's right.

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Okay.

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So basically you transformed your career

from this tech support role into this, you

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know, senior associate in analytics role

in like less than six months, correct?

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Absolutely.

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Yep.

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That's right.

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Okay.

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So to give the people a timeline,

you are at this non data job

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and then got this awesome data

job in just a couple of months.

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What were the timelines on that?

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Like, when did you start your data?

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So in data journey overall, I

finished my degree in business

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administration back in December.

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So I was looking into jobs of data or like

how I can gain the skill set in November.

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It's been a very recent pivot.

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Cause I was kind of like, Oh

no, I'm going to graduate soon.

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What am I going to do?

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You know, I'm working this,

this dead end tech support job.

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I don't want to do this forever.

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I want to be a data analyst.

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What is it going to take to become one?

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Okay.

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So I didn't realize that.

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So this is November of 2021.

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You're going to graduate from

your degree, which was in business

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administration in December.

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And, and that's when I guess

you spoke or to a mutual friend

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of ours, I guess your cousin.

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Is that right?

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That's right.

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Okay.

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Dom, shout out Dom.

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And Dom introduced you

to me and in my program.

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So I think you joined Data Career

Jumpstart, the big course, the

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project camp in November, correct?

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That's correct.

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And then when did you land

your job with Densive?

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So my official start date was February

28th, but they extended the offer

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to me about a month beforehand.

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So January, like the end of January.

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Okay.

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So, so end of January, early February.

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So basically we're looking at November,

December, January, January, three months.

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Yep.

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Three months from, from like,

did you, like how much data

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experience did you have?

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So.

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I had Python classes because I also did

computer science before I transitioned.

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And I also had a single SQL course

that I took, which I did not take

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seriously, so I didn't carry a lot.

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So, not a whole bunch, I would say.

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Okay, but some.

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So that's, that's what

you're referring to.

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Back in college, you originally

were studying computer science and

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then switched to business, right?

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Yep.

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So not like a ton, definitely no

real world experience, you know,

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maybe some college classes and you

were in a tech support role and it

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sounded like there were some, at least

looking at code involved in that.

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So not like the furthest away,

but also not the closest, right?

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Yes, exactly.

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Okay.

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So basically just, just to give people

an overview in three months, you went

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from tech support role to this new job

in analytics, and I guess, tell people

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a little bit about your current job.

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Like, are you in the office or no?

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Nope, it's completely remote.

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Completely remote and like, do you like

what you do more than you did previously?

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Oh, absolutely.

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100%.

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It's, it's so much fun.

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I'm learning so much every day.

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I mean, it's stressful with

all the projects that are going

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on, but it's, it's good stress.

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You know, it's something that I

can work on and learn more of.

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Okay.

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And like, so, okay, now

you're working remotely.

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I guess you're still in California, right?

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Yes, that's right.

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Okay.

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So you got to stay where,

live where you want to live.

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The people, the, the company

density is pretty international.

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Where do they have offices?

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I don't even know.

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They have a lot of

offices on the East coast.

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I, one of their main

offices is in New York.

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So a lot of my team is in New York.

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Do you have to like wake

up early for calls then?

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No, actually they've been pretty nice.

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Even though I'm the only West

Coast person, they've been trying

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to schedule all our meetings like

later on in the day just for me.

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So it'll be in the afternoon for

them, but like in the morning for

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me, which I don't mind at all.

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So they've been really nice about that.

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That's awesome.

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Okay, so you get to be where you're at.

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You're a West Coast guy.

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You're working from home.

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What, what about financials?

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Like, like, are you, if I've been

going to as much detail as you want,

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but like, do you feel like you're

better financially at this place

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than you were at the other place?

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Yes.

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100 percent in a better place.

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I wasn't struggling before, but

I definitely not struggling now.

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It was about a 15 K increase,

which is I'm super psyched about

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because this is something that,

you know, I like living on my own.

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I want to keep living on my own.

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So I've am able to do that still.

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Okay.

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So, wow.

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So basically.

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You like essentially in three months, you

gave yourself a 15, 000 raise basically.

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Yeah.

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I would say that.

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And, and the cool part, I think about

analytics and data in general is it's

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like, you're not, it's not dead end.

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Like you can keep progressing

on and on for a long time.

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So it's like, you know, it's

15, 000, you know, at the jump.

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And then, and then, you know, maybe

five years down the line, it's another,

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you know, 20 or something like that.

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Who knows, but it's like,

you can keep progressing.

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Right.

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Isn't, I think that's

one of the coolest parts.

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Yes, that was one of the biggest

things for me because one of the

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things that I asked for when I was

interviewing was that, do you have a

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way for me to become a data scientist?

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Because that was a really big thing

for me because progression is huge

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for me because I need that motivation.

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I need to be able to progress

upwards and you know, it's not

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just from a money standpoint, it's

from, I just want to build myself.

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You know, I just finished college

and it's still fresh for me.

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I want to get into the workforce

and I want to build my reputation.

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So let's now, now, now that people

understand, you know, your journey.

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So from tech support, graduating

college in business, maybe taking

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one, one or two programming

classes to within three months.

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Landing this job at a pretty big

international, you know, marketing

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company, getting that 15, 000 raise,

being able to work, you know, where

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you want, let's talk about kind of

the, the, how, how you got there and

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what you thought was, was important.

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So you started by joining

Data Crew Jumpstart and.

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You know, took some of the lessons there.

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Do you feel like you

were learning quickly?

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Like what, what was the first thing that

you're like, Oh my gosh, I'm getting this.

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I like this.

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Like, what was the first time where you're

like, this totally is something I want to.

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Yeah.

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So definitely the biggest thing that I

love about DCJ, and this is one thing

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that I talk to a lot of people about

is I like the project approach rather

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than the, here's a homework assignment.

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It's due next week kind of approach.

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And also the, the shorter videos, the

bite by bite, 10 to 20 minute videos.

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I don't know about you, but I feel like.

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I just snore an hour,

two hour long lecture.

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Like I don't retain anything,

you know, and a lot of these

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things, they are hands on.

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You can follow along,

but I just get so bored.

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I'm gonna have to pause it here.

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When I come back, I'm

gonna forget where I was.

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So I love the little bite

sized videos that you have.

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And that's just one thing that was able

to keep me to do like, Oh, maybe I'll do

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two videos today or three videos next day.

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So I can just do something every

day, you know, it keeps it fresh.

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Yeah.

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I think that's something I've

really tried to do with most of

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my courses and trainings is like

projects, projects, projects.

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Projects, projects, projects.

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And I remember, I think you

latched onto that pretty quickly.

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I remember, you know, one of the hobbies

that you have outside of data, right?

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But outside of work is,

is weightlifting, right?

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Do you want to tell the people

a little bit about what you do?

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Yeah.

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So I'm a competitive power lifter,

which means I try to lift as

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heavy as I possibly can in the

squat bench and deadlift category.

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So that's a little, fun thing that

I do outside of work, outside of my

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nine to five, outside of my studying.

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So I, you can typically find me at the

gym, maybe two to three hours a day.

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Okay.

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But don't, don't be humble.

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Tell the people how you did

in the last competition.

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Yeah.

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So I ended up getting a gold medal

first place in my last competition.

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So that was really fun.

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And how was it?

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Come on, give us the details.

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So squat, my heaviest lift was 457 pounds.

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Let's see.

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Bench was 270 pounds and

the deadlift was 500.

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2 pounds.

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Sheesh.

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That is crazy.

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Yeah.

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I don't know if I've done that, that

much weight, like in all of my years

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combined of, of going to the gym.

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So anyways, you, you love weightlifting.

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You went through a pretty big fitness

journey in your life too, right?

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Yes.

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Yes.

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Yeah.

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And I remember you made a project

about it, if I'm not mistaken about.

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You know, kind of your weight loss

journey, your, your weight increase

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and like being able to lift.

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And for me, that was when I

was like, all right, I see

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big things coming from Steven.

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I was like this, when you're able to take

something in your life that you enjoy and

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apply it and tie it into data, I'm like,

okay, that person's going to succeed.

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Absolutely.

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That's one thing that I talk to a lot

of people, a lot of people that have

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been reaching out to me through LinkedIn

is just don't just do these projects

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that people are telling you to do.

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Learn those skills and apply them to

things that you're passionate about.

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Because I had so much fun

making that dashboard.

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Like, I don't know about you, but

like, if someone told me I had

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fun doing dashboards, I would be

like, You're just a nerd, dude.

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I don't want to hear this, but I

had so much fun doing that project

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because it's personal to me.

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It's something that I care about.

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And I just wanted, it was my baby.

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You know, I wanted to make

it as best as I could.

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And people loved that dashboard,

especially during interviews.

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Yeah.

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So, okay.

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So you're in DCJ.

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We're doing projects.

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So we start off with the

screen time project, doing

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a lot of data visualization.

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Then, then we have a project

about fitness as well.

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So we dive, dive into Python and those

are kind of the, the two, the two

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things that probably you had done before

applying to jobs, is that correct?

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That's correct.

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Yes.

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So it took you about.

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Two months to do those more or less.

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Is that right?

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More or less.

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Yeah.

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About two months.

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Okay.

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And I guess another aspect of

DataCrew Jumpstart is it's not

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only technical skills, but it's

also, you know, personal skills and

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soft skills and networking skills.

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So whole section on LinkedIn, whole

section on finding jobs when you were

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applying to jobs, what was your strategy?

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And then what ended up working?

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So when I was applying to

jobs, a lot of it was just.

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Going on LinkedIn, looking for

data analysts, whether I was, I

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was filtering by remote because

my last job was remote also.

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And I was like, I don't want

to go back to office anymore.

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I can just leave and go for

a walk whenever I want to.

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So I made sure remote was one of those.

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And then I also did easy apply.

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I know it's not like the best way to

go through jobs, but for me, I needed

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to do job applications as easy as

possible because it's really draining

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to do job applications, especially

if you have to email like three

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different cover letters or whatnot.

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So Easy Apply was really good

for me because I can literally

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just lay in bed, watch Netflix

and just apply, apply, apply.

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Okay.

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I get through like 50 or

so applications a night.

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You know, just chilling,

applying that way.

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And yeah.

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Did you have any luck

with the easy applies?

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I'll be honest.

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Not really.

337

: 00:12:52

I had one company get back to

me, but that's because I had

338

: 00:12:55

experience in the mortgage company.

339

: 00:12:57

So that one company got back to me

and I did interview with them as well.

340

: 00:13:01

Okay.

341

: 00:13:02

So a couple of things, a couple of

things that I think you said, one

342

: 00:13:05

is like you're applying to like

data analyst positions, correct?

343

: 00:13:08

Yeah.

344

: 00:13:08

Two.

345

: 00:13:08

Yes, that's correct.

346

: 00:13:10

Okay, so one thing I like that Stephen

just mentioned, he doesn't have, he's

347

: 00:13:13

never been a data analyst, he doesn't

have analytics experience, he's never

348

: 00:13:16

been a data scientist, but he's applying

for these entry level, you know, data

349

: 00:13:19

analyst jobs, but where he had success,

I think is really important here, Was

350

: 00:13:25

when he applied to a mortgage company.

351

: 00:13:26

And some people are like, Oh, I don't

have any experience being a data analyst.

352

: 00:13:29

And you know what, that might be true.

353

: 00:13:30

That might not be on your resume,

you might not have actually crunched

354

: 00:13:32

that much numbers, but you definitely

have some sort of experience, whether

355

: 00:13:36

it's in teaching or whether it's

in mortgage or something like that.

356

: 00:13:38

I think it's important to really marry

those at the beginning, especially

357

: 00:13:42

when you're trying to get interviews,

because like, There's data in every

358

: 00:13:45

industry around the world, right?

359

: 00:13:47

If you've been, you know, it's, if

you've been an athlete, there's,

360

: 00:13:50

there's sports analytics jobs.

361

: 00:13:51

If you've been in business,

there's business analyst jobs.

362

: 00:13:53

Like I think Steven did a really

good point there of like leaning

363

: 00:13:56

in on his, you know, background.

364

: 00:13:59

I think that made him more

attractive to, to employers and

365

: 00:14:03

recruiters and stuff like that.

366

: 00:14:04

And also a lot of perseverance right

there, you know, cause, cause I'm sure you

367

: 00:14:07

got a lot of projections and, and didn't

hear back from a lot of those, right?

368

: 00:14:12

I'm still getting those rejection

emails and I'm like, I'm good, man.

369

: 00:14:15

I'm almost three months into this job.

370

: 00:14:16

I don't, I'm good.

371

: 00:14:18

Yeah.

372

: 00:14:18

Yeah.

373

: 00:14:18

You're like sucks to suck.

374

: 00:14:20

I already have a job.

375

: 00:14:20

Thank you very much.

376

: 00:14:21

So let's talk about that.

377

: 00:14:23

So how did you find this job

or how did they find you?

378

: 00:14:26

And what was that process like?

379

: 00:14:28

Sure.

380

: 00:14:28

Yeah.

381

: 00:14:29

So I actually saw through the DCJ discord,

you know, Ellie, I absolutely love Ellie.

382

: 00:14:33

She's one of.

383

: 00:14:34

My mentors and Avery, you're

also one of my mentors.

384

: 00:14:36

I want to make sure that that's clear

like You have an amazing community that

385

: 00:14:40

you've built here and the people that are

giving back even though they're not We

386

: 00:14:44

talked about this which is really funny.

387

: 00:14:45

Yeah, and I wanted to message her on

linkedin, but she did not allow People so

388

: 00:14:50

I had to get in mail and to get in mail I

had to get the was it called the LinkedIn

389

: 00:14:54

premium Yeah, I literally paid 40 just

to get LinkedIn premium so I could send

390

: 00:15:00

her a message and say hey, I'm interested

about this job Can you look at my resume?

391

: 00:15:05

Can you talk to me?

392

: 00:15:06

Let me know like would I be a good fit?

393

: 00:15:07

What do I need to look for?

394

: 00:15:09

To learn to be a good fit.

395

: 00:15:11

And we scheduled a phone call

and we talked about all of that.

396

: 00:15:13

And she actually helped

me rebuild my resume.

397

: 00:15:16

She helped me highlight some

words and stuff like that.

398

: 00:15:18

I would, I would go as far to say that

she did, redid my whole resume for me.

399

: 00:15:23

She was giving me tips at first

and she was like, you know what?

400

: 00:15:25

Just send me the, send me the word file.

401

: 00:15:26

And she, she redid my whole resume for me.

402

: 00:15:30

So she's absolutely amazing.

403

: 00:15:32

She's a star.

404

: 00:15:32

Senior manager of analytics

in Dentsu as well.

405

: 00:15:35

We don't work on the same

team, unfortunately, but we

406

: 00:15:37

still talk from time to time.

407

: 00:15:38

And she's an amazing asset

to have in this industry.

408

: 00:15:41

So I think, I think there's a lot

of really interesting things there

409

: 00:15:43

because part, part of the reason I

made DataCrew Jumpstart, and I haven't

410

: 00:15:47

really, I haven't really talked about

this since I launched the course.

411

: 00:15:50

I've kind of, I've kind of forgotten

about this, but one of the reasons

412

: 00:15:53

I launched it was because, you know,

I, I broke into data science, like.

413

: 00:15:57

Like seven years ago.

414

: 00:15:59

Right.

415

: 00:15:59

And when I was doing it, I mean, it was,

it was still pretty popular, I think, but

416

: 00:16:03

definitely not as popular as it is now.

417

: 00:16:05

And there definitely was not

nearly as many resources.

418

: 00:16:08

And I was super lonely.

419

: 00:16:09

I was like, I don't know if anyone knows

what I'm doing or like, I don't know if

420

: 00:16:13

anyone else is on the same journey as me.

421

: 00:16:14

A shout out to Ellie.

422

: 00:16:15

Ellie is totally awesome.

423

: 00:16:16

Very helpful to the community

and aspiring data professionals.

424

: 00:16:20

And you connected with

her, but, but hold on.

425

: 00:16:23

I, what I love here is that like.

426

: 00:16:24

There was a, there was a

something to yet overcome.

427

: 00:16:26

You like couldn't figure

out how to message her.

428

: 00:16:29

That's okay.

429

: 00:16:29

Cause cause you paid the 40 bucks.

430

: 00:16:31

You got the LinkedIn

premium, sent her an email.

431

: 00:16:34

What was that cold message?

432

: 00:16:35

Like, like, just like, Hey, I

saw you posted in DCJ discord.

433

: 00:16:40

About a job opening, you know, I've

been, I've been an Avery's program

434

: 00:16:44

and learning something like that.

435

: 00:16:46

It was 100 percent just like that.

436

: 00:16:48

It was just, yeah, I've

been working in DCJ.

437

: 00:16:50

I've been, I've done with most of it.

438

: 00:16:52

Can you look at my resume?

439

: 00:16:53

What can I change or what should I learn?

440

: 00:16:55

What should I focus on basically?

441

: 00:16:57

Yeah.

442

: 00:16:57

And I love, I love that also

because you had a portfolio.

443

: 00:17:00

That's, that's something.

444

: 00:17:02

That that.

445

: 00:17:03

Okay.

446

: 00:17:04

So I have a lot of DMS.

447

: 00:17:06

I get a lot of DMS every day.

448

: 00:17:08

People, people asking me for

advice, people asking me for jobs.

449

: 00:17:11

I get a lot of jobs.

450

: 00:17:13

It's, I think one out of a hundred

DMS that I've ever gotten have

451

: 00:17:18

had a portfolio attached to it.

452

: 00:17:20

And guess what?

453

: 00:17:21

Guess who I hired as the one

person I've ever really hired is

454

: 00:17:24

the person who had a portfolio.

455

: 00:17:26

Having a portfolio just proves that

you like are for real and like you can

456

: 00:17:30

do the things that you say you can.

457

: 00:17:32

Here's the evidence, right?

458

: 00:17:33

And I know Ellie really liked that

about you that you had the portfolio.

459

: 00:17:37

That like you had evidence, she's

a big fan of data visualization.

460

: 00:17:40

You had awesome data visualizations,

for instance, from, from data career

461

: 00:17:43

jumpstart and also just like your fitness

journey and stuff like that as well.

462

: 00:17:46

Had some, had some pretty

cool data visualizations.

463

: 00:17:49

So I think that played a big role in

you, like catching her attention and

464

: 00:17:53

her being willing to help you out

was just like, you were for real, you

465

: 00:17:56

know, that portfolio made you for real.

466

: 00:17:58

Yeah, I was gonna say portfolios are very

undervalued right now because I, I also

467

: 00:18:03

get a lot of DMs, especially now with all

my posts going viral or whatnot, but a

468

: 00:18:07

lot of them, they don't have portfolios.

469

: 00:18:09

Like they don't send me, I ask them,

I always say, Hey, I can help you.

470

: 00:18:12

I know you're looking, send me your

resume, send me your portfolio.

471

: 00:18:14

A lot of them don't have any portfolios.

472

: 00:18:16

And I just keep telling people

like, how do these companies

473

: 00:18:19

know what you've been working on?

474

: 00:18:20

Sure.

475

: 00:18:21

You got the SQL skills.

476

: 00:18:22

You got the Python skills.

477

: 00:18:23

Data visualization, but they need to

see something needs to be tangible.

478

: 00:18:27

They need to be able to picture you

in their role before they hire you.

479

: 00:18:31

Yeah.

480

: 00:18:31

That's one thing that I try to promote.

481

: 00:18:33

Yeah, for sure.

482

: 00:18:34

And let's, let's go ahead

and talk about your LinkedIn.

483

: 00:18:36

So I'm actually, I'm actually

going to go ahead and I'm going

484

: 00:18:39

to go to your LinkedIn right now.

485

: 00:18:40

Cause I want to, I want to get

some live, some live things.

486

: 00:18:43

All right.

487

: 00:18:43

So I'm going to linkedin.

488

: 00:18:44

com.

489

: 00:18:45

We'll have Steven's LinkedIn

in the show notes down below.

490

: 00:18:47

I'm going to go to your page.

491

: 00:18:49

Let's see.

492

: 00:18:49

I just lost it.

493

: 00:18:50

There we go.

494

: 00:18:51

And I want to check something.

495

: 00:18:53

So currently right now, you have

3, 831 followers on LinkedIn.

496

: 00:19:00

Okay.

497

: 00:19:01

Yeah, I want you to go back to

November, six months ago, okay,

498

: 00:19:04

not even half a year really.

499

: 00:19:05

How many, how many connections or

followers did you have on LinkedIn?

500

: 00:19:09

So I had zero followers cause I didn't

allow followers and connections.

501

: 00:19:13

It was probably like 20, like 20 people.

502

: 00:19:16

So basically you've grown your LinkedIn,

like who knows how many times since,

503

: 00:19:23

since you joined DCJ basically.

504

: 00:19:26

And, and, and more specifically.

505

: 00:19:28

So you went from, let's say, let's

say from: 2020

506

: 00:19:32

has like connections and followers.

507

: 00:19:33

It's kind of confusing.

508

: 00:19:34

I'm just going to call them followers.

509

: 00:19:35

So you had like 20 connections.

510

: 00:19:37

And now you have 3, 831.

511

: 00:19:40

Now let's, let's talk about

specifically how you gained those.

512

: 00:19:44

So you've been posting, I know

a big part about DCJ is posting,

513

: 00:19:47

posting, posting, posting, posting.

514

: 00:19:49

And recently, let's see a couple of

days ago, you had a post go super viral.

515

: 00:19:54

Five days ago, it has 3, 194

516

: 00:19:59

reactions.

517

: 00:20:00

95 comments, 57 shares,

and it's three sentences.

518

: 00:20:05

That's right.

519

: 00:20:05

So did most of the followers

come from that or before that?

520

: 00:20:09

I would say most of them came from

that, but I wanted to make sure

521

: 00:20:13

that I was still posting after that.

522

: 00:20:15

Because I feel like when you get

that exposure, it only lasts so long.

523

: 00:20:19

So the biggest thing I wouldn't

say stressor for me was like, Oh,

524

: 00:20:24

what's the next post going to be?

525

: 00:20:25

It's definitely not going to be as

good, but I need to show these new

526

: 00:20:28

followers, you know, the type of

content that I want to put out, the

527

: 00:20:30

kind of things that I want to set.

528

: 00:20:32

So it was a little bit

of a time crunch for me.

529

: 00:20:35

Yeah.

530

: 00:20:35

Okay.

531

: 00:20:35

So then the next one was three days later.

532

: 00:20:38

And, uh, it ended up getting 872

likes, 82 comments and 25 shares.

533

: 00:20:45

Yeah.

534

: 00:20:46

Yep.

535

: 00:20:46

That was the things you can do

to break into data analytics.

536

: 00:20:49

Okay.

537

: 00:20:50

And then the next one

had 1, 437 reactions.

538

: 00:20:56

85 comments and 163.

539

: 00:20:57

That's right.

540

: 00:20:58

Okay.

541

: 00:20:58

So gone pretty viral

recently on, on LinkedIn.

542

: 00:21:02

People are asking you for advice.

543

: 00:21:03

What, what advice do you give people who,

who said they want to go into analytics?

544

: 00:21:07

A lot of the time I will ask what their

background is because a lot of people,

545

: 00:21:11

I, you don't necessarily think that you

need a background in data analytics.

546

: 00:21:15

You can literally get started today.

547

: 00:21:16

Like look up SQL, look up some

Python, learn some data viz.

548

: 00:21:21

But a lot of the times.

549

: 00:21:23

They're, they're asking like, what

can I do or what can I learn, but some

550

: 00:21:27

people are just straight up asking

me for a job and I'm like, I mean,

551

: 00:21:29

I'm just a, I'm just an associate.

552

: 00:21:30

Like, I can't, I can't give you

a job, but some, actually some

553

: 00:21:33

people ask me like, Oh, do you

have any projects I can help on?

554

: 00:21:35

Those people, I value their

comments a little bit more because

555

: 00:21:38

it's not asking for a handout.

556

: 00:21:41

I don't want to sound vain, but it just

seems like it's not mutually beneficial.

557

: 00:21:46

beneficial to either of

us, you know what I mean?

558

: 00:21:49

So I like those messages that

people are asking like, well,

559

: 00:21:52

what are you working on?

560

: 00:21:53

Or what, what can I help you with?

561

: 00:21:55

Things like that.

562

: 00:21:56

It's, it's, yeah, I totally agree that

whenever, whenever you're, you know,

563

: 00:22:01

cold messaging someone or, or even like

talking to someone, it's a, The first

564

: 00:22:05

message should always be, how can I

provide this person value in their life?

565

: 00:22:10

How can I help this person?

566

: 00:22:12

Because, you know, obviously, you know,

I have a substantial LinkedIn following

567

: 00:22:16

and when I have posts that, that go

viral, it's like a mad zoo in there.

568

: 00:22:21

It's like very, it's very crazy.

569

: 00:22:23

And to be honest, I don't read like

half of them probably at the end of

570

: 00:22:26

the day, it's just, it's just too

much, but I try to find the ones

571

: 00:22:29

that like, Oh, this is interesting.

572

: 00:22:30

Or this person's different.

573

: 00:22:31

Or this person is saying, thank you.

574

: 00:22:33

Like this person doesn't

want anything from me.

575

: 00:22:35

They're just saying thank you.

576

: 00:22:35

And yeah, maybe it does seem vain,

but that's like human nature.

577

: 00:22:38

Like we, we don't trust people until

they prove their worthiness, you know?

578

: 00:22:43

And most of the time people are just

asking for stuff and it's, it's kind

579

: 00:22:46

of annoying because unfortunately.

580

: 00:22:49

I can't spend, you know, we

can't spend our whole lives and

581

: 00:22:51

our whole time helping people.

582

: 00:22:52

We can help a few people, but when

it gets to such a big, big number, it

583

: 00:22:55

gets a little bit difficult because

we got to put food on the table.

584

: 00:22:59

Got to pay the bills.

585

: 00:23:00

Yeah.

586

: 00:23:00

So let, let me actually, let me, let me

read this, this viral post that you had.

587

: 00:23:06

So let me pull this up here.

588

: 00:23:07

The one I really liked was things you

can do to break in a data analyst.

589

: 00:23:11

Learn your hard skills

in order of importance.

590

: 00:23:13

I am a SQL Excel.

591

: 00:23:15

Python, statistics, data

visualization, Tableau, and Power BI.

592

: 00:23:19

Learn your soft skills.

593

: 00:23:20

Tailored resume, online portfolio,

answers to basic data analytic questions.

594

: 00:23:24

And then don't forget to apply.

595

: 00:23:26

Okay, so talk about one of those

points that you find that's like really

596

: 00:23:29

valuable that other people maybe, maybe

don't see the same way that you do.

597

: 00:23:32

So, um, Yeah, this, this post was

definitely built on my experience trying

598

: 00:23:39

to get a job in data analytics, which

I feel like my individual experience

599

: 00:23:44

would also apply to a lot of other

people, which a lot of people have been

600

: 00:23:47

sending me messages like, hey, I've

been in the exact place where you are,

601

: 00:23:50

except I haven't gotten that job yet.

602

: 00:23:53

But the biggest thing that, the main

reason I wanted to make this post This

603

: 00:23:57

post was the just because you don't

satisfy the job requirements part.

604

: 00:24:01

There was actually a podcast that I

listened to you and someone said this.

605

: 00:24:05

I'm sorry.

606

: 00:24:05

I'm forgetting the name of

the person that you talked to.

607

: 00:24:07

I think they were a data

freelancer, a data freelancer.

608

: 00:24:10

They were talking about that.

609

: 00:24:11

Just make sure that you apply.

610

: 00:24:12

Like a lot of these job requirements are

just like, they're not even minimums.

611

: 00:24:16

I don't think they're

like the ideal candidate.

612

: 00:24:18

And that really resonated with me

because when I was applying to jobs,

613

: 00:24:21

um, A lot of the time, I wasn't even

looking at the job requirements.

614

: 00:24:24

I was just applying, because

like, because in my head, I'm

615

: 00:24:26

just like, if they considered me,

then they fit me as that profile.

616

: 00:24:30

So if, if I might as well

shoot my shot, right?

617

: 00:24:33

For sure.

618

: 00:24:33

So that was, that was the main

thing I wanted to nail home.

619

: 00:24:35

It's just like, these are like, if you fit

50%, 60 percent of that profile, do it.

620

: 00:24:40

Why not?

621

: 00:24:40

What do you have to lose?

622

: 00:24:42

Yeah, especially if it's if it's only

time I mean, and time obviously is

623

: 00:24:46

valuable but at least it's not money you

know what I'm saying like you can apply

624

: 00:24:50

and definitely like I think, I think

the requirements have to be honest so

625

: 00:24:55

I obviously I try to help people find

jobs and so one of my one of my main

626

: 00:25:00

jobs is to try to help my students,

especially inside data career jumpstart.

627

: 00:25:05

Find jobs that fit them well.

628

: 00:25:07

And so I spent a lot of time

talking to CEOs, a lot of times

629

: 00:25:10

speaking to recruiters and try to

match make the process basically.

630

: 00:25:14

And so now people kind of send me jobs

and say, Hey, I'm looking for this.

631

: 00:25:17

Do you have anyone like that?

632

: 00:25:19

And recently I had a guy reach

out to me, a CEO of a company.

633

: 00:25:23

I will not say which, but it's

anyways, it's, it's a big business

634

: 00:25:26

and they, but they've never actually

had a data analyst or data scientist.

635

: 00:25:29

So I guess not that big.

636

: 00:25:30

I guess it's a midsize company,

actually probably small compared

637

: 00:25:33

to everything in the world.

638

: 00:25:34

It probably has like.

639

: 00:25:35

100 employees.

640

: 00:25:36

And he wanted to hire a data,

data analyst or a data scientist.

641

: 00:25:40

And he's like, I'm going to

write the job description and

642

: 00:25:42

let me know what you think.

643

: 00:25:43

And he came back to me and it was

this, it was a data analyst role,

644

: 00:25:47

but like all the requirements were

data scientists, like requirements.

645

: 00:25:51

And I was like, bro, this

is a data scientist job.

646

: 00:25:53

And he's like, well,

what's the difference?

647

: 00:25:55

And so sometimes the people, you

know, writing the job, hopefully

648

: 00:25:57

this isn't always the case.

649

: 00:25:58

Like, I don't know.

650

: 00:25:59

I hope, I hope this is an exception,

but like, he didn't even really

651

: 00:26:02

know what he was talking about.

652

: 00:26:03

And that's, that's why

he was talking to me.

653

: 00:26:05

But sometimes, sometimes, especially

smaller companies, they don't

654

: 00:26:07

know what they want, or they're

listing like 100 things and they

655

: 00:26:10

don't really need those things.

656

: 00:26:11

They need, they need two

out of the hundred things.

657

: 00:26:13

So you never know, it can never hurt.

658

: 00:26:15

But, but one of the things I think is,

is most valuable that you did was you're,

659

: 00:26:19

you're leaning on your networking,

you're leaning on the people, you know,

660

: 00:26:22

you know, you're, you're in the data

career jumpstart discord, you're talking

661

: 00:26:25

to DMing people, you know, who, who

know me, like you're, you're leaning

662

: 00:26:29

on the community around you and using

the network that ended up landing you

663

: 00:26:33

the, you know, the, the awesome job.

664

: 00:26:35

And a lot of the times I think, you

know, applying online does work,

665

: 00:26:38

but if you can figure out how to

like, Talk to a human instead of

666

: 00:26:41

having to go through the system.

667

: 00:26:43

I would always choose

talking to human 10 times.

668

: 00:26:47

Absolutely.

669

: 00:26:48

Absolutely.

670

: 00:26:48

I 100 percent agree every job I've

ever had, and I've had six or seven

671

: 00:26:52

jobs or because I knew someone that was

working there already every single job.

672

: 00:26:55

So networking is another

undervalued skill.

673

: 00:26:57

I mean, I don't, I don't want to say

it's undervalued, but people don't

674

: 00:27:00

practice it the way that you should.

675

: 00:27:01

It was making those connections and

building your skills based on those

676

: 00:27:04

connections is just, I don't know.

677

: 00:27:06

I would, yeah, undervalued . I think

especially on LinkedIn because like, I

678

: 00:27:10

don't know about you, but like I do not

necessarily enjoy networking events.

679

: 00:27:15

Like where, well, okay I take it back,

but like for instance, like socials,

680

: 00:27:19

like where you like just have to like

go up to someone and introduce yourself.

681

: 00:27:22

I'm not very good at that as an introvert,

and I know maybe you guys don't believe

682

: 00:27:25

me, but I'm super introverted and like

I'd much rather have like a topic, so like

683

: 00:27:29

for instance, if they posted on LinkedIn.

684

: 00:27:32

I would love to comment on their post,

or, or maybe they'll come on my post

685

: 00:27:36

if I post like I like having like a

vehicle, where our conversation flows

686

: 00:27:39

versus just like meeting in person

and, and also like on the internet.

687

: 00:27:43

I can tell, I know exactly who you

are, off of your LinkedIn profile.

688

: 00:27:47

If we go to a real life like mixer.

689

: 00:27:50

I'm just like judging your appearance

to like, hopefully know what you do.

690

: 00:27:53

And like, I know that I went to the

Silicon slopes conference, which is like

691

: 00:27:57

a pretty big tech tech conference in Utah.

692

: 00:28:00

And like, I was like, how

do I maximize my time?

693

: 00:28:02

I'm going to like meet some random

people and like, you just walk into

694

: 00:28:05

people and be like, Hey, what do you do?

695

: 00:28:07

And it's like, Oh, like I make potato.

696

: 00:28:09

Like machines, and it's

like, okay, I'm sorry.

697

: 00:28:12

I'm not really interested in that.

698

: 00:28:13

And I can't relate versus on LinkedIn.

699

: 00:28:15

I can be like, oh, this person, you know,

works for a marketing analytics company.

700

: 00:28:18

That's super interesting.

701

: 00:28:20

Let's start a conversation there.

702

: 00:28:21

So I just feel like I feel like

LinkedIn is still underrated

703

: 00:28:24

for the networking aspect of it.

704

: 00:28:26

I don't know.

705

: 00:28:27

Yeah, I think events like that, they kind

of force this genuine connection when you

706

: 00:28:31

can't really force something like that.

707

: 00:28:33

Especially at those events, you're, you're

expected to ask people what they do.

708

: 00:28:36

You're expected to be asked what you do.

709

: 00:28:39

Whereas in LinkedIn, you can choose that.

710

: 00:28:41

You can choose to let anyone know as much

as you want to, but also, you know, you

711

: 00:28:44

have your profile and all that stuff.

712

: 00:28:46

But yeah, it's just, it

just lacks that genuineness.

713

: 00:28:50

Yeah.

714

: 00:28:50

And who knows, maybe, maybe,

maybe I do like in person events.

715

: 00:28:53

So maybe Maybe I was just that too

broad of an event and maybe like,

716

: 00:28:57

like, for instance, I have enjoyed the

data conferences that I've gone to.

717

: 00:29:01

So maybe it was just too broad but anyways

I like LinkedIn because it can be really

718

: 00:29:04

like I'm much better on one on one versus

in group so big big fan of LinkedIn.

719

: 00:29:10

Okay, so, With that, I'm just

going to rehash your story.

720

: 00:29:13

You're working for this mortgage company

as a tech support, graduating college,

721

: 00:29:17

join, join DCJ, start posting on LinkedIn.

722

: 00:29:21

You know, you're by the way, your

LinkedIn profile looks really good.

723

: 00:29:23

I love, love your cover photo.

724

: 00:29:25

That's one of the things that we go over

in DCJ and no one uses the cover photo.

725

: 00:29:30

In a good way.

726

: 00:29:31

A lot of people don't anyways.

727

: 00:29:33

So love it.

728

: 00:29:33

Love your profile picture.

729

: 00:29:35

Great, great headline on your LinkedIn.

730

: 00:29:37

Posting good things.

731

: 00:29:38

You're using the featured

section, which is another thing.

732

: 00:29:41

Your first thing is your portfolio.

733

: 00:29:42

Next few things are cool

graphs and viral posts.

734

: 00:29:45

So you're nailing, you're

nailing the LinkedIn thing.

735

: 00:29:48

Land a job through networking,

you know, 15k increase.

736

: 00:29:51

In, in salary, you know, you're

working remotely, which is awesome,

737

: 00:29:55

enjoying life, have room to grow.

738

: 00:29:57

So that's, that's kind of the Steven

story that we want to shout from the

739

: 00:30:01

rooftop rooftops and let everyone know

that you can, you know, you can go

740

: 00:30:04

from, from, I don't want to say nothing

because you are definitely something,

741

: 00:30:08

but, but non non data jobs, non data

jobs to a data analyst role or associate

742

: 00:30:14

data analytics role in three months.

743

: 00:30:16

Yeah, absolutely.

744

: 00:30:19

Crazy journey I've been

on and still going on.

745

: 00:30:23

So I just want to give you all the

biggest props because there's people

746

: 00:30:27

inside of data career jumpstart.

747

: 00:30:28

Who have been in there, you know,

like how long has it, I guess we

748

: 00:30:32

started in September, September,

October, November, I guess like eight

749

: 00:30:35

months who are still struggling.

750

: 00:30:37

And one thing I think you did

really well is one, you took

751

: 00:30:40

the content really quickly.

752

: 00:30:42

You built and like fell in love with

your portfolio like you're like my

753

: 00:30:45

portfolios is where I post stuff

you documented stuff really well.

754

: 00:30:48

And then you networked.

755

: 00:30:50

I mean, those are really like, honestly,

that's what data career jumpstart is.

756

: 00:30:54

is all about.

757

: 00:30:55

It's like those three things.

758

: 00:30:56

It's like, can you work fast?

759

: 00:30:58

Can you make projects?

760

: 00:30:59

And can you network?

761

: 00:31:00

And you did those three things well.

762

: 00:31:01

I think that's why it led to, you

know, your success so quickly.

763

: 00:31:05

You know, I think, I think at the end of

the day, that's, that's pretty much, you

764

: 00:31:09

know, how you got to where you're at.

765

: 00:31:10

And now, now you're helping other people.

766

: 00:31:12

Now you're learning more.

767

: 00:31:13

I know you've, you've been mentioning,

you've been SQL on the job and,

768

: 00:31:17

and that's the whole point, right?

769

: 00:31:18

Yeah.

770

: 00:31:18

Like, I think I told you this straight

up, because we had a call before

771

: 00:31:21

you joined Data Career Jumpstart.

772

: 00:31:23

I said, I don't really want to take

you from being nothing to the best

773

: 00:31:27

data scientists on planet earth.

774

: 00:31:29

I want to help you get your first

job, get your foot in the door,

775

: 00:31:32

so you can get paid to learn.

776

: 00:31:35

Absolutely.

777

: 00:31:36

And that's what you're

going to do now, right?

778

: 00:31:37

And hopefully, I mean, tell, tell the

people what you're, what you're learning

779

: 00:31:40

and then what your, what your goals are.

780

: 00:31:41

So yeah, definitely.

781

: 00:31:42

I, During the interview process itself,

it was actually very conversational.

782

: 00:31:47

We never talked about anything that I

wasn't, I never had to say too many times.

783

: 00:31:51

It was very good.

784

: 00:31:52

I loved talking to, I had basically,

so I had three interviews back

785

: 00:31:56

to back to back from I think

nine o'clock to twelve o'clock.

786

: 00:31:59

In the morning, but it

was I wasn't sweating.

787

: 00:32:02

It was like the most genuine So I got to

interview with my director who I currently

788

: 00:32:07

direct our report to right now a senior

manager and then another a senior Director

789

: 00:32:11

too, which who all work on my current team

and they were they're absolutely amazing

790

: 00:32:14

people and I love them And one thing I

want to shout out about my director and

791

: 00:32:18

why I love So I've only been working

there for a little over two months.

792

: 00:32:23

And we have flexible time off, so FTO.

793

: 00:32:26

And she's like, Stephen, you've

been working here for two months,

794

: 00:32:27

you haven't taken time off.

795

: 00:32:28

You should take some time off.

796

: 00:32:29

I've never worked for a company

that told me, Hey, you need to

797

: 00:32:33

just, you know, you might burn out.

798

: 00:32:35

Just Take a break, take it off.

799

: 00:32:37

And I was like, okay, that's cool.

800

: 00:32:39

But yeah, anyway, that's awesome.

801

: 00:32:41

Yeah, it's, it's been an absolute journey.

802

: 00:32:43

So they, they have been

teaching me a lot of things.

803

: 00:32:46

So that was one thing that they made

sure of in the interview process.

804

: 00:32:49

Have you been exposed to SQL?

805

: 00:32:51

Have you been exposed to Tableau?

806

: 00:32:52

Have you been exposed to Python?

807

: 00:32:54

So I only had one technical question

during that whole interview, which was,

808

: 00:32:57

so here's a table and then she described

the columns and here's another table.

809

: 00:33:01

She asked her, how would you join

these or what join would you use?

810

: 00:33:04

And I was.

811

: 00:33:05

Able to, I actually had a, a definition

of all the joins, cause I, I kinda

812

: 00:33:08

knew that they might ask some join

questions on one of my screens.

813

: 00:33:11

I have three screens, basically.

814

: 00:33:13

So I had, on one of my screens, I

had, I had, oh, what a full join was.

815

: 00:33:15

I was like, oh, full join sounds

like something I would want to use.

816

: 00:33:17

She was like, yeah, that's,

that's what you would use.

817

: 00:33:19

And I was like, cool,

that's about all I know.

818

: 00:33:22

About SQL besides, you know, the main

definitions select from where group by

819

: 00:33:26

all that kind of stuff, but yeah, they're

basically giving me SQL lessons right now.

820

: 00:33:30

And I've just been

learning, I know Python.

821

: 00:33:32

It's just learning pandas,

sqlearn, sklearn a little

822

: 00:33:36

bit more and stuff like that.

823

: 00:33:37

So it's definitely the ideal situation

of getting paid to learn and knowing

824

: 00:33:41

from the get go, what the expectation

was really made it easy for me to

825

: 00:33:45

transition into it, and I'm really

excited to learn more because one

826

: 00:33:48

thing I want to talk about is like, So

many people are asking me for advice.

827

: 00:33:51

They're saying like, oh, you know,

so much about data analytics.

828

: 00:33:53

I'm, I'm literally the first rung on the

ladder of data, but like the way I think

829

: 00:33:58

about it is there's a, there's a big

gap from the floor to the first rung to

830

: 00:34:01

like, you know, the, the ladder of data.

831

: 00:34:04

So people are asking me so many things

and I, I'm trying my absolute best and

832

: 00:34:07

I'm, I'm a people pleaser by, by nature.

833

: 00:34:09

So I try to answer every DM, try to

answer every comment and it's, it's

834

: 00:34:13

starting to burn me out a little bit.

835

: 00:34:14

So I'm not forcing myself too much,

but it's just crazy to me that

836

: 00:34:17

people are, Relying on me or like

trusting me with helping them in

837

: 00:34:22

their career when I'm literally

just the first rung on that ladder.

838

: 00:34:26

So it's been super humbling and

it's been super great to help out

839

: 00:34:29

the community any way that I can.

840

: 00:34:31

Yeah, totally.

841

: 00:34:32

Well, I think, I think in order

to be like a teacher or like a

842

: 00:34:36

mentor, you really only have to

be one step ahead of the people.

843

: 00:34:40

You know, that you're teaching.

844

: 00:34:41

So I think, I think that's,

that's totally fine.

845

: 00:34:43

And, and at the end of the day,

we're, we're all still learning.

846

: 00:34:45

You never will know everything in data.

847

: 00:34:47

So it all, it all works out in the end.

848

: 00:34:49

And I think, I think people talking

to you is a good thing for them, but I

849

: 00:34:53

totally understand the burnout aspect.

850

: 00:34:55

That's one of the reasons why,

you know, before, before I did

851

: 00:34:58

data career jumpstart, when I was

still working my nine to five at

852

: 00:35:00

Exxon, I did a lot of mentorship.

853

: 00:35:02

I did a lot of live calls.

854

: 00:35:03

I did a lot of DMing and it got

to a point where I was like, okay,

855

: 00:35:06

I'm at my absolute cap for what I

can do, you know, at this point.

856

: 00:35:10

And that's why, one of the reasons

why I started Data Career Jumpstart.

857

: 00:35:12

But awesome stuff.

858

: 00:35:13

I'm stupid.

859

: 00:35:14

I'm, I'm not stupid.

860

: 00:35:16

I'm super stoked for you and your, your

journey, the way I see you, like your

861

: 00:35:20

next, your next, you know, couple of

years, like, you know, you're going

862

: 00:35:22

to nail a bunch of SQL right now.

863

: 00:35:24

You're going to learn a

bunch of SQL on the job.

864

: 00:35:26

Get really good at SQL.

865

: 00:35:27

Um, you know, do really

learn the business.

866

: 00:35:29

Cause I think marketing something that's

new to you, it'd be new to me as well.

867

: 00:35:32

Like really learn your domain.

868

: 00:35:34

And then, you know, maybe, you know.

869

: 00:35:36

I guess you started in February

or, or March, you know, maybe six

870

: 00:35:39

years down the road, or not six

years, a year or two down the road.

871

: 00:35:42

You know, maybe you, you switch to a

different team, or, or maybe you go

872

: 00:35:46

into like a, a data scientist role

and you do that for two to three

873

: 00:35:49

years and then, you know, all of a

sudden you're, you're like an expert,

874

: 00:35:52

you know, data guy in marketing.

875

: 00:35:54

You combine those two things.

876

: 00:35:55

That's a huge niche.

877

: 00:35:57

I, I, I see such a bright

future for you, man.

878

: 00:35:59

I'm, I'm super excited for you

and couldn't, couldn't be more.

879

: 00:36:02

More happy for you also couldn't

happen to a better person.

880

: 00:36:05

So congratulations on all your success.

881

: 00:36:07

You know, just, just to recap 15 K new

job has way more, you know, place to

882

: 00:36:14

expand 3000 followers on LinkedIn in

like, in like four months, basically.

883

: 00:36:21

That's crazy.

884

: 00:36:22

And I think it goes a huge testament

to who you are as a person.

885

: 00:36:26

Right.

886

: 00:36:26

Thank you so much, Avery.

887

: 00:36:27

Yeah.

888

: 00:36:27

I just, like I said, you're, you're one of

my mentors in this data journey and you've

889

: 00:36:31

been an absolute huge help in everything.

890

: 00:36:33

So I love everything that DCJ has

been able to let me do and grow.

891

: 00:36:37

It's still, it's still helping

me even after I finished, you

892

: 00:36:39

know, most of those projects.

893

: 00:36:40

So looking forward to that

sequel part when that comes out.

894

: 00:36:44

Oh, it's, it's coming out.

895

: 00:36:45

It's coming out soon.

896

: 00:36:46

So yeah, looking, looking

forward to that as well.

897

: 00:36:49

And yeah, great stuff.

898

: 00:36:50

Any, any parting words you'd

like to leave the people with?

899

: 00:36:53

Yeah.

900

: 00:36:53

So it's, it's going to be

a tough road, but like, I.

901

: 00:36:56

I don't want to say that because

of my skills I got to where I am,

902

: 00:37:00

but I, one thing that my brother,

I've heard my brother say is that

903

: 00:37:04

luck favors those who are prepared.

904

: 00:37:07

So I, I want to say I'm very blessed and

I'm very lucky to have meet the people

905

: 00:37:11

that I have met and also to get to be

in the position that I have, I am in,

906

: 00:37:15

but the thing is you got to be prepared.

907

: 00:37:16

You know, you got to put the work

in, you got to study, you got to

908

: 00:37:19

learn and like when luck, when luck

happens to you, you're prepared.

909

: 00:37:23

So that's just one thing I would say.

910

: 00:37:25

For sure, for sure.

911

: 00:37:26

I like that.

912

: 00:37:27

Well, Steven, it's been

an absolute pleasure.

913

: 00:37:29

We'll have your link to

your LinkedIn down below.

914

: 00:37:33

And yeah, we'll see you more on LinkedIn.

915

: 00:37:34

We look forward to more posts.

916

: 00:37:36

I hope you enjoyed that episode.

917

: 00:37:38

And if you did, I'm going to

have an awesome free masterclass

918

: 00:37:42

that I know you're going to love.

919

: 00:37:43

We're going to talk about a lot of

things this episode talked about.

920

: 00:37:46

You can get it absolutely for

free at data career jumpstart.

921

: 00:37:49

com slash training, or using the

link in the show notes down below.

922

: 00:37:53

Hope to see you there.

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