213: This Bartender Became a Data Analyst With One Tableau Project

Brandon Traditi: 00:00:00

I love Tableau so much, I probably spent close

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to a hundred plus hours on easily,

late nights just going crazy.

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But I was hooked.

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Avery: That's Brandon Traditti, and he was

bartending in New Jersey when he stumbled

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onto this very channel and podcast.

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Two years later, he's now a data

consultant at The Information

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Lab, one of the most respected

Tableau shops in the world.

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And here's the wild part.

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He got the job with just

one Tableau project.

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No resume, no cover letter,

just a simple dashboard.

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But before any of this even happened,

there was a networking event in New

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York City he wasn't supposed to be at.

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/ Brandon Traditi: it was sold out.

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And I was like, no.

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So I showed up anyways.

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They were like, I don't see you here,

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Today, Brandon's gonna break down

exactly how he did it, the project he

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built, the networking event he crashed,

and the mindset that got him hired.

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Let's get into it.

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Avery: Brandon, you were able

to land your first data job

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being a

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data consultant

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a little bit about your transition.

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What

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you doing before bartending

and up doing bartending?

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Brandon Traditi: Yeah, absolutely.

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Um, well first off, all thanks to

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but before bartending, studying

my master's in cybersecurity.

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Right after that, I got a job at the New

York State Department of Education with

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them I was a cybersecurity analyst and it,

day to day involved just kind of waking

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up, reading some reports, making some

calls, helping people update different

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types of systems, to a point where I was

really looking at it and just trying to

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think, is this what I wanna do for the

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With that being said, I made the

bold move to kind of just leave

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that job, leave that industry.

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It wasn't where my passion was.

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always had background in the hospitality

industry, I decided to just take a jump,

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go in, go back to bartending, um, and try

to figure out what that next move was.

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Funny enough, bartending and setting

up the bar, and if anybody's in the

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hospitality industry, they'll know

this is, you know, it takes about an

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hour to kind of set up the setup shop.

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I would always listen to podcasts.

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one day, lo and Beholds kind of

came through and saw Avery Smith

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Data Career Jumpstart, and I was

like, oh, I wonder what this is.

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I, I put it on and I was hooked.

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I, at that time.

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starting, I wanna say in the, the

first couple months of bartending,

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you had about 105 episodes out.

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And I wanna say I watched

through almost 85,

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it was just every morning I would

plug 'em in, I would set up everything

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on autopilot, and I would just

be listening to all these success

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stories and all these different,

how to crack into the data world.

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And having that background in tech,

I was like, you know, I never really

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knew this was kind of a possibility.

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And then it opened up the floodgates

and I said, you know what?

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Let's, let's give this.

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Give this a go.

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So I joined DAA, I started

exploring with different tools

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that I had never touched before.

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I had heard of sql.

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I, you know, knew of R but I never

really got in depth with them.

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And one day sitting down.

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It was, I think it's one of

the first modules in DAA of

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the Massachusetts school.

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dashboard and I thought

it was the coolest thing

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And I thought Tableau was awesome, and

it, from that moment on just got me

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So going with that, finishing, this

was in about the summer of: 2024

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So it took me a little bit longer

than I think it's scheduled for.

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It took me about six months to

kind of get through the course.

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Um, and through that time I'm sure

we'll touch on was my favorite part of

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it was the capstone, which was my NFL

vetting dashboard, ultimately used as an

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application for my current role with that.

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The place that I work now is I'm a data

consultant for the information Lab.

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And the information lab is a little

different than a traditional job,

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in the application process that is.

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lab is built off of purely in

aptitude based application process.

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There is no resume attached

to it, which SP lights there,

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you know, they don't care.

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cover letters.

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It was you submit a Tableau dashboard

the team will take a look at it and

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then they like it, if they think

you put a lot of effort into it,

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you know, you can see things with a

different eye view in the data world,

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they'll bring you in for an interview.

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that second interview is a little more

of the behavioral interview process.

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So that was where I got to

present that NFL dashboard.

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I got to really show the true

colors of what I thought about data

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and how my mind worked with the

things that I was interested in.

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From there, once you get past that round,

it turns into one last round, which is

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they give you a data set and it's, you

have about a week and a half, two weeks to

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build out another dashboard, Then present

to the board at the final interview.

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And looking back, I remember

just being so nervous.

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I love Tableau so much, and I think

that NFL dashboard between your

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program and that time, I probably

spent close to a hundred plus hours on

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easily, late nights just going crazy.

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But I was hooked.

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Uh, and I think that's what

they saw in me, and I think

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they, they saw that intro of.

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really wants to be here

and he, he really loves it.

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So I ended up getting the job.

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I have now been there.

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We just hit our one year anniversary

with my cohort, so just over a year now.

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Avery: Congratulations.

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That's absolutely amazing.

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Uh,

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incredible that you're able to

go through this journey and.

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so many things right, that I wanna

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our listeners and watchers

can learn from your journey.

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off, I think to be said

about your job, the systems,

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cybersecurity role where you were like.

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kind of hate this.

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I want to be done with this

because I've heard that

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time demanding.

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burnt out a lot.

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People get really bored with it

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wanna do this the rest of my life.

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I'm curious, like, why didn't you go

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job to just like

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this intermediate job

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and

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I, I don't know the answer,

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like one thing I've,

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people are pitting their careers.

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trying to get out

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current job,

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bootcamp or

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of videos

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current job,

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very demanding, very taxing,

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do the data stuff on top of it.

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a lot of people get burnt out doing

that 'cause they're already burnt out.

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That's why they want a new career.

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curious, kinda like

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where you were at and you're

like, what, I'm just gonna,

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uh, I mean you weren't taking a

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gonna go to a job that maybe you

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requires less demand

and stress on your life

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ultimately have

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careers.

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Is that, is that true or

kind of just saying that

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Brandon Traditi: Yeah, no,

that, that's totally true.

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I think at the time, uh, it was more

of, I didn't know what I wanted to do.

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Uh, after being a cybersecurity

analyst, I, I just knew that at

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least bartending I had flexible time.

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You know, I, I at least still had

my day and I would work at night.

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that daytime I knew I could at

least take that and explore.

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And there were so many other options.

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Honestly, I was looking into,

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Social media marketing, like starting an

agency or, I was looking into, at that

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time blockchain was even blowing up too.

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I was like, oh, should I

be a blockchain developer?

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And I just, and I think that's how

the text side of things started and

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then that's how I found your podcast.

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And then once I started learning

and the possibilities of a data

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career, I was a little more

inclined of like, this sounds fun.

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'cause it brings in the, the logical

side, the computer side, the tech side.

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But it also brings in the creative side

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Was what I was searching for

was that, that creative side

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of letting that out in data.

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Avery: Okay,

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wanna become a data

analyst and you're like,

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listening to

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Smith guy.

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Uh, a lot.

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took you 105 episodes

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finally like, pull the trigger

and joined the accelerator,

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I say on the hundred was so important?

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Brandon Traditi: I, it's no test

to you and, and honestly, anybody

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who knows me know I do just so

much research before I do anything.

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Like even the Mac that I am talking on

right now, it took me over two and a half

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months of research before I knew this

was the exact model, makeup, everything.

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So I, I wanna say in it, it was just

hearing other people's success stories and

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how, and if anybody's listening to this

and they're in that type of stuck feeling.

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It's not until you hear other

people who were in your spot that

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made it out that you actually

get like, oh, I can do that too.

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Because after a while you're, you're

kind of sitting there and you're

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like, well, maybe this isn't for

me, or, you know, nobody that I

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heard of came from hospitality.

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But it was hearing teachers, it was

hearing, I believe you did have another

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hospitality worker or construction worker.

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And I was like, if they

can do it, why can't I?

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And, and I think that was finally

the moment where I was like,

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let's, let's do this thing.

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And if we're gonna do

it, we're gonna commit

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Avery: to

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now we're, we're

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And you're on the podcast,

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in, your shoes.

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let's dive into a little

bit more how you did it.

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So

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to the podcast.

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the accelerator.

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follow the SPN

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but you learned the

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building the projects as we go.

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and

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I guess,

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the

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really

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Tell me about like what in Tableau.

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Brandon Traditi: Yeah, so I think

what it was was it was, it's low code,

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but it's still enough that you can do

some really creative things with it.

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but I think the barrier to entry

is, it, it's a free tool online.

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Anybody can go download

it and play with it today.

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Um, I think.

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Normalized data for me, and it was one

of those things that just looked more

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familiar to a drag and drop type type

feel to it, and being able to just go in

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and not even know what I'm doing, but be

able to create something and just, like

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I just made a dashboard and I don't even

know how half it works, but I did it and

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then it kind of scratched that itch of.

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Now I really wanna know how it works,

and now I really want to get in depth

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with every intricacy of Tableau.

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And that's when things below

surface level get really,

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Avery: excellent points there

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is

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realize

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can literally download

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version of Tableau public,

and it is really easy.

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Like the

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entry is so low,

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Excel, because most people

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they're familiar

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the next thing we touch

is Tableau because.

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It's

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or R.

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just drag and

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I'm glad to hear that.

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Like you got in there and you're

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really know what I'm doing, but I'm

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charts and I'm, oh, I kind

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play

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as you go.

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Visualization is

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is so important

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numbers

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You were hooked there.

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you do the Tableau

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two Tableau

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You

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do the other projects.

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me about like your, your job hunting

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of interviews?

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Were

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the projects?

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How was that going?

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Brandon Traditi: Yeah, so

taking us back to that process.

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We'll start

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I was scrolling through LinkedIn doing.

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My normal posts and kind of

trying to outreach and talk.

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And I saw an ad from the Information

Lab and it was big title, said

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Meet and Greet, New York City.

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And I was like, oh, well what's this?

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And it was, you know, do you

wanna become a data analyst?

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And it, it's one of those things

that you look at and you're like,

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is this too good to be true?

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And it's say, come meet our team

and see what we're all about.

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On X date.

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And I was like, and I think it was like a

following Thursday and there was a signup.

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So I, I immediately went to the signup.

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I'm close enough to New York

City and right across the

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river, uh, and it was sold out.

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And I was like, no.

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Like, uh, I want to go, I wanna

know what this is all about.

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So I showed up anyways.

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Um, thankfully, I, I sent my name.

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They were like, I don't see

you here, but just go on up.

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It's fine.

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And I had shown up and there was

probably about a hundred people.

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they, now do this regularly where they

basically bring in everybody, you get to

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see the office, you're in the office, and

they put on a presentation of just who the

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information lab is, how they came to be,

they do, kind of the program behind it.

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Um, so once I saw that, I got

out, my girlfriend, I said, Hey,

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I said, this is where I wanna be.

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This is what I

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And I think from that moment, I didn't

really look anywhere else, which is.

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different than most probably DAA students.

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But I, I was just eyes focused

on the information lab.

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This is where I wanna be,

this is what I wanna do, these

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are the people I wanna work

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So from that time, I wanna say that

was probably around, um, August-ish,

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because I wanna say I was finishing

up your program and then the next

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application process was that December.

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So I saw the applications open.

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I had finished our capstone, and then

even after finishing the capstone, I

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think I put another extra 200, 2 50 hours

into it to make sure I can do the best

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that I can for this application process.

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But yeah, so I had, which is very not

normal, I would say, is just one company.

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I sat on it, this is where I

wanna be, this is what I wanna

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Avery: perfect.

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I think that's way to

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approach it is like, I'm not gonna spray

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focus on,

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you

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Brandon Traditi: one company.

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Avery: And

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think that was a good option for

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one,

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accelerator has

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partnership with.

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information lab, maybe a

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wrong term, but like

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I

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known the

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Information Lab,

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Andy

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of the founders

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friends.

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podcast before,

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recruiters and some of the

people who've worked there, I've

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LinkedIn.

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So it was good because like you not only.

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that networking

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me and knowing,

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people there,

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you.

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I think I have some messages from people.

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Yeah.

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Some of the interview

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to go in there and look.

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on Tableau,

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They're one of

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of the things they do is they

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hooked on

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also made sense.

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one downside to the information lab

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basically only.

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Mm-hmm.

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you don't live in New York

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for you.

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So it makes sense.

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You, you're niched down

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method there.

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And

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you applied the P part, right?

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Because I love the way the

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interviews where it's like,

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all we want is a

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us your best

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Pat

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Tableau project.

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And that's all we wanna

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very merit

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some emails with you,

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from

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Capstone

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also pop up your capstone project on

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take a look at it.

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But basically.

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Is your chance to actually do

your first project on your own.

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So

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why don't you tell

everyone what your project.

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: 00:15:33

Was and why you chose it?

352

: 00:15:36

Yeah,

353

: 00:15:36

so I did an NFL betting dashboard,

and essentially where this all kind

354

: 00:15:43

of came to be was I, I grew up playing

football, loved football my whole life.

355

: 00:15:48

And, uh, when I moved to New Jersey.

356

: 00:15:52

They started out the legalization of being

able to gamble and, and bet on sports,

357

: 00:15:56

and I thought it was so interesting

and I already loved watching football,

358

: 00:15:59

and I would put some money here and

there on a game, but the one thing

359

: 00:16:02

I found was I, I would always lose.

360

: 00:16:04

And I was like, all right,

so I mean, I love football.

361

: 00:16:06

I think I know football,

but I'm just losing.

362

: 00:16:08

So I wanted to see if there were any

intricacies or anything that I could find

363

: 00:16:13

that would give me a slight advantage.

364

: 00:16:16

And then on top of it, to combine

that with Tableau, I was like,

365

: 00:16:19

this is, this is a home run.

366

: 00:16:20

This is all I want to do.

367

: 00:16:21

And it was really one of those

projects that you kind of get

368

: 00:16:24

into and you get lost into.

369

: 00:16:26

'cause you just get so in depth of like.

370

: 00:16:29

Both loves of the NFL and this

getting put together, and it

371

: 00:16:34

truly, this day, still my favorite project

372

: 00:16:37

I

373

: 00:16:38

really wanna say

374

: 00:16:40

it's a good project.

375

: 00:16:41

And you ended up using this

376

: 00:16:44

for DAA and then we ended up kind of

workshopping it and you did a lot of

377

: 00:16:49

I gave you a few notes about, and I think

the Information Lab even gave you a few

378

: 00:16:53

to make it the dashboard better.

379

: 00:16:55

and this ultimately was

your, your submission

380

: 00:17:01

information lab and

381

: 00:17:04

and then the jobs.

382

: 00:17:06

one Tableau project.

383

: 00:17:07

catching your job.

384

: 00:17:09

I

385

: 00:17:09

I would say single handedly, but also

like you were really brave and networked

386

: 00:17:15

you couldn't get a ticket

to the event and you still

387

: 00:17:17

a lot of it was credit to you.

388

: 00:17:19

and, and this

389

: 00:17:21

you did it,

390

: 00:17:23

but

391

: 00:17:23

Um,

392

: 00:17:25

said you spent like hundreds of hours

393

: 00:17:27

a ton of time on

394

: 00:17:29

would bet if this was like.

395

: 00:17:32

just go back

396

: 00:17:39

Mm-hmm.

397

: 00:17:40

spend hours on, on that

398

: 00:17:43

spend hours

399

: 00:17:45

no one wants to spend hours of

400

: 00:17:48

getting paid

401

: 00:17:49

so what kept you going?

402

: 00:17:51

Brandon Traditi: I think the first note

I have never opened that workbook again.

403

: 00:17:56

I'm scared to see what's behind the

404

: 00:17:59

Um, I was still totally new to Tableau.

405

: 00:18:01

I was still like trying to learn all

the extra things, and I'm sure that a

406

: 00:18:07

lot of things I did were a little more

time consuming than they had to be.

407

: 00:18:12

with that being said, want to say it was.

408

: 00:18:17

Trying to be the best that I could

and make things work no matter what.

409

: 00:18:22

I think at one point, like a good example

410

: 00:18:25

have DZV now, dynamic zone

visibility, but back then I didn't

411

: 00:18:28

know I, it's relatively new.

412

: 00:18:30

I don't know if they had it when

I was developing this project, but

413

: 00:18:32

there was something called sheet

414

: 00:18:34

And it was basically the concept of

I had a filter and if you clicked a

415

: 00:18:38

certain thing, I wanted a sheet to move.

416

: 00:18:40

So it showed something else.

417

: 00:18:41

I think it was like a time, like if you

clicked like all time, it was like a

418

: 00:18:44

little, another filter that popped up.

419

: 00:18:47

And I wanna say even that took me like

four or five hours just to figure out.

420

: 00:18:51

But I knew I wanted it on the

dashboard and I knew I was

421

: 00:18:54

gonna do whatever I could.

422

: 00:18:56

make it happen.

423

: 00:18:57

And I, I think that's kind of that

start line of, I have this in my

424

: 00:19:00

head, I have this idea, I don't

care how much time it takes me.

425

: 00:19:04

Like I want to get to that end product.

426

: 00:19:07

And I know that at the end of the day, and

even if this didn't work out and I didn't

427

: 00:19:11

get into the information lab, that it

was gonna make me a better analyst and a

428

: 00:19:15

better Tableau user at the end of the day.

429

: 00:19:17

So me it was just.

430

: 00:19:19

Nonstop resiliency of just how do

I get what's in my head on this

431

: 00:19:26

Avery: a

432

: 00:19:26

lot of,

433

: 00:19:29

resiliency, but also like.

434

: 00:19:34

because

435

: 00:19:35

um,

436

: 00:19:35

really liked Tableau,

437

: 00:19:37

maybe if it

438

: 00:19:40

I'm gonna give up,

439

: 00:19:43

you really, like

440

: 00:19:45

It was fun.

441

: 00:19:46

Mm-hmm.

442

: 00:19:46

So many people will choose

like such boring projects

443

: 00:19:49

with

444

: 00:19:53

think really choosing

445

: 00:19:55

you're passionate about

446

: 00:19:57

Pairs

447

: 00:19:57

well because when you hit those roadblocks

448

: 00:20:00

hours to figure out, you

figure it out and you

449

: 00:20:03

I think you're really smart on choosing

450

: 00:20:08

the

451

: 00:20:09

settings basically to, to.

452

: 00:20:11

bring

453

: 00:20:12

a a, really good project.

454

: 00:20:14

Brandon Traditi: And I, I think

that's a tribute to you too.

455

: 00:20:15

I think that's one of the things that

you stress in the capstone is like, pick

456

: 00:20:19

something you love at the end of the day.

457

: 00:20:20

'cause you can pick anything you want

and it's just pick something that

458

: 00:20:22

you, you know, you wanna work with.

459

: 00:20:25

And like you said, it ends up

being fun at the end of the

460

: 00:20:28

Avery: Amazing.

461

: 00:20:29

present this,

462

: 00:20:31

dashboard

463

: 00:20:32

an interview, leads to a second interview,

464

: 00:20:34

uh,

465

: 00:20:35

an offer ultimately,

466

: 00:20:37

um,

467

: 00:20:39

amazing of you to do

468

: 00:20:42

now, working,

469

: 00:20:43

you're

470

: 00:20:44

data consultant inside of the

471

: 00:20:48

institutions.

472

: 00:20:49

Uh,

473

: 00:20:50

in the world.

474

: 00:20:51

I had you

475

: 00:20:53

billboard in

476

: 00:20:57

YouTube as well.

477

: 00:20:58

It was an absolute

478

: 00:20:59

I wanted to talk about your

479

: 00:21:02

what you're doing.

480

: 00:21:03

So I

481

: 00:21:03

wanted to know what tools

you're mainly using,

482

: 00:21:06

at

483

: 00:21:06

your job,

484

: 00:21:08

and maybe like, lesson that you've

kind of learned, since being on the

485

: 00:21:12

job that maybe you didn't expect.

486

: 00:21:13

Brandon Traditi: Um, so I think to

start off it, it might be good for.

487

: 00:21:17

Me to explain how the information Lab

488

: 00:21:21

Um, so you don't just come in day

one and just become a consultant.

489

: 00:21:26

the Information lab is a, it's a

28 month contract where the first

490

: 00:21:30

four months a classroom setting.

491

: 00:21:34

in a cohort with anywhere from six to

eight other people, and you are just

492

: 00:21:39

learning all different types of tools

and learning how to not only become a

493

: 00:21:43

consultant, but become a subject matter

expert in the tools that we specialize

494

: 00:21:48

So those first four months

are relatively intensive.

495

: 00:21:54

through sql, you go through

Tableau, you go through Alteryx,

496

: 00:21:57

you go through Tableau Prep, and.

497

: 00:22:01

With that, you go through a little bit

of the baseline of like Snowflake, DBT.

498

: 00:22:05

And then one cool thing with that is

that it's not, I guess just studying

499

: 00:22:10

every day, but eight weeks of it you'll

be on what's called a client project.

500

: 00:22:15

we actually do kind of like a seeing

is believing where, you know, we have

501

: 00:22:19

companies where we're like, Hey, you know,

we have this set of students who are, are.

502

: 00:22:24

Learning and doing their best, you

know how to about, we bring them in

503

: 00:22:27

and see what we can do with some data

problems that you have currently and

504

: 00:22:30

then, you know, go from there so you

actually get hands-on experience.

505

: 00:22:33

We worked with some pretty cool

companies when I was, when I was going

506

: 00:22:37

through training and it gives you that.

507

: 00:22:39

That hands-on experience of,

okay, like I, I can do this.

508

: 00:22:43

Like I, I see real world data problems

and we come together as a cohort and

509

: 00:22:47

we, we accomplish all these different

things in a week span from there.

510

: 00:22:52

Avery: Sorry, I'm gonna interrupt.

511

: 00:22:54

that is a really good

point to bring up that

512

: 00:22:56

the

513

: 00:22:57

lab kind of starts as like

514

: 00:22:59

apprenticeship.

515

: 00:23:01

you're getting paid to learn.

516

: 00:23:02

And that's one of the things I try to

stress in the podcast and in the bootcamp

517

: 00:23:07

learn for free.

518

: 00:23:08

pay to learn.

519

: 00:23:09

That's fine too, but the best

520

: 00:23:12

get paid to learn.

521

: 00:23:13

and

522

: 00:23:14

so you are earning a data analyst salary,

523

: 00:23:16

during

524

: 00:23:18

was, eight

525

: 00:23:20

literally get paid to learn.

526

: 00:23:21

And I love the Information Lab for

doing it that way and doing it kind

527

: 00:23:24

of this like apprenticeship model.

528

: 00:23:26

Obviously a

529

: 00:23:27

a lot of companies don't

do it that way, but.

530

: 00:23:30

Everyone will have the opportunity

at work to get paid to learn

531

: 00:23:34

to know

532

: 00:23:36

sorry for the interruption.

533

: 00:23:37

No,

534

: 00:23:38

keep, going.

535

: 00:23:39

So you're, you're, you went through this

536

: 00:23:41

the Information lab is

training you keep going.

537

: 00:23:43

Brandon Traditi: Yep.

538

: 00:23:44

Yeah, so I, um, just to touch on that

point, it, it's funny you say the

539

: 00:23:47

word apprentice because as of recent.

540

: 00:23:50

year or so since I've been there, are

actually registered with New York State.

541

: 00:23:55

It's the second in New York

State, first in New York City.

542

: 00:23:57

it's, it is an actual

apprenticeship program.

543

: 00:23:59

So once I hit my required hours,

I will be a journeyman in, uh,

544

: 00:24:04

data, which is really cool.

545

: 00:24:08

so it's funny that you say that.

546

: 00:24:09

So it is technically the end

of day an apprenticeship.

547

: 00:24:12

Um, but yeah, so, so that

training is, it's intensive,

548

: 00:24:16

but it's fun at the same time.

549

: 00:24:18

You're learning from smartest

people that I've ever encountered.

550

: 00:24:22

some of the people who are in the Hall

of Fame for Tableau, you really get a

551

: 00:24:28

very, very in-depth knowledge of a lot

of different tools that we utilize.

552

: 00:24:33

Once those four months are up, you

move into four, six month contracts.

553

: 00:24:38

being there just over a year now

I'm in my second contract, large

554

: 00:24:43

financial institution and you

basically get, get kind of put in.

555

: 00:24:47

With a, being a subject matter expert

in any of the tools that we use.

556

: 00:24:51

So we have some people who

are in all Alteryx placements.

557

: 00:24:54

like myself, I am mostly Tableau.

558

: 00:24:58

and you know, we even branched

off into, now we lean into the DVT

559

: 00:25:02

space, snowflake, whatever it might

be for me, current tech stack.

560

: 00:25:07

And that was something, you

had asked was it is almost 90%

561

: 00:25:11

Tableau and 10%, uh, Tableau Prep.

562

: 00:25:15

So their, their ETL tool, a very solid,

I'll actually put down a couple notches

563

: 00:25:20

for a little bit of SQL now, as of

recent, so into the SQL thing, but

564

: 00:25:26

one of the things that you asked was

a tip, and I think this is the best

565

: 00:25:32

tip that I can give and what I wish I

could tell myself two years ago is that

566

: 00:25:37

you're never gonna know everything.

567

: 00:25:41

go for it.

568

: 00:25:43

even with months of training, which

equates to whatever it is, 500

569

: 00:25:48

plus hours of training, there's

still things in Tableau that I am

570

: 00:25:52

learning on a day-to-day basis and

571

: 00:25:54

still things that I, you know, can't

figure out for some reason and, and

572

: 00:25:58

have to go and troubleshoot and I think

a lot of people who are, were in the

573

: 00:26:01

po uh, position that I was in have

that little bit of a sense of imposter

574

: 00:26:05

syndrome and it's, you know, I, I, I

don't know everything about the tool

575

: 00:26:09

and it's like you, you never will.

576

: 00:26:11

And, and I wish I can go back

and tell myself that and, just,

577

: 00:26:16

to keep pushing and, and you

are good at what you're doing.

578

: 00:26:20

Just keep going.

579

: 00:26:22

Avery: love,

580

: 00:26:23

Love, that.

581

: 00:26:23

Um,

582

: 00:26:25

will never

583

: 00:26:26

don't know it all.

584

: 00:26:27

Um.

585

: 00:26:28

this is episode What?

586

: 00:26:30

216

587

: 00:26:33

data career podcast

588

: 00:26:34

uh,

589

: 00:26:35

two 13, I think.

590

: 00:26:37

and, uh,

591

: 00:26:38

I

592

: 00:26:38

definitely don't know

593

: 00:26:39

to, you

594

: 00:26:41

Hall of Famers and

595

: 00:26:42

people who

596

: 00:26:44

in everything.

597

: 00:26:45

to learn from

598

: 00:26:46

people and I,

599

: 00:26:49

it's really cool

600

: 00:26:50

even after you going through.

601

: 00:26:53

my

602

: 00:26:53

bootcamp, you

603

: 00:26:56

information

604

: 00:27:00

like a year now?

605

: 00:27:01

uh, you're

606

: 00:27:02

you're still

607

: 00:27:04

I think

608

: 00:27:07

uh,

609

: 00:27:07

really takes that to heart.

610

: 00:27:09

Do

611

: 00:27:09

enjoy being

612

: 00:27:12

job?

613

: 00:27:13

Brandon Traditi: I love it

614

: 00:27:14

I, I, I'll never forget it.

615

: 00:27:16

It's like, and I, I commute

into the city anyhow, it's.

616

: 00:27:20

Every morning when I go in, it's,

you know, you see a lot of people

617

: 00:27:22

who are, who are in, you know, maybe

positions that they don't want to,

618

: 00:27:26

and they, they look a little down.

619

: 00:27:27

It's every day I walk into that

building with a huge smile on my

620

: 00:27:30

face of just like, this is everything

that I always wanted it to be.

621

: 00:27:33

This is exactly what I envisioned

when I said, you know, I

622

: 00:27:36

wanted to get my dream job.

623

: 00:27:39

I get to be creative.

624

: 00:27:41

I get to, to be on the

data side of things.

625

: 00:27:43

I get to be logical and it's.

626

: 00:27:46

Combined couldn't, I couldn't ask for

627

: 00:27:51

Avery: That's

628

: 00:27:52

And, uh,

629

: 00:27:53

I

630

: 00:27:54

that's a testament

631

: 00:27:55

to a

632

: 00:27:57

are maybe in a job that

they hate right now, that

633

: 00:28:00

isn't greener on the other side.

634

: 00:28:01

The grass is greener on the other side.

635

: 00:28:03

And in

636

: 00:28:03

in my opinion,

637

: 00:28:06

everyone's in different

638

: 00:28:08

if possible,

639

: 00:28:10

like you're

640

: 00:28:10

you're gonna live a long

641

: 00:28:12

you're gonna

642

: 00:28:13

third of your life.

643

: 00:28:14

working,

644

: 00:28:16

if not more,

645

: 00:28:18

you

646

: 00:28:18

might as well do something you enjoy.

647

: 00:28:20

so take the steps necessary

today to figure out how to get

648

: 00:28:25

a

649

: 00:28:25

situation where you can

650

: 00:28:27

a smile on your face for that third.

651

: 00:28:29

That's at least, at least my thought.

652

: 00:28:31

Um,

653

: 00:28:32

I'd be curious to hear like

any other advice you'd have

654

: 00:28:35

for aspiring data analyst

655

: 00:28:37

listening right now.

656

: 00:28:38

and you,

657

: 00:28:39

you know, is like thinking

about becoming a data analyst or

658

: 00:28:42

trying to become a data analyst.

659

: 00:28:43

What would you, what

advice would you give them?

660

: 00:28:45

Brandon Traditi: Yeah, I think

on the non-technical side of

661

: 00:28:49

things, is a fun place to be in.

662

: 00:28:54

But you, you have to be curious.

663

: 00:28:57

Um, I think it all starts there.

664

: 00:28:59

It's asking.

665

: 00:29:01

of questions.

666

: 00:29:03

that's something that we always

emphasize is, you know, there,

667

: 00:29:06

there is no stupid question.

668

: 00:29:07

You should ask as many questions

as possible and understand.

669

: 00:29:13

you're trying to do with it and

where, what route you want to go

670

: 00:29:16

with it and just be curious the whole

time in a technical side of things.

671

: 00:29:22

I would lean in more if you wanna be

a data analyst on to learning a tool.

672

: 00:29:29

I'm a little biased when it comes

to Tableau, uh, but there is

673

: 00:29:32

Power bi, there is, uh, Sigma.

674

: 00:29:35

all different types of tools and

I think are becoming one of the.

675

: 00:29:41

Leading front runners in, how to break in.

676

: 00:29:47

the day, even being the job for

a year, you know, I barely touch

677

: 00:29:51

Excel other than to look at a file.

678

: 00:29:53

I never do any formulas, anything.

679

: 00:29:55

Um, very lightly touch sql, but it's

nothing that we don't cover in DAA,

680

: 00:30:01

uh, but 90% of my time is at least, you

know, learning the visualization tool,

681

: 00:30:06

learning some type of ETL tool kinda.

682

: 00:30:09

The background of data, you know,

joins unions, pivoting, things

683

: 00:30:14

like that, to get started and

to, to break into the industry.

684

: 00:30:17

I think that's the key

685

: 00:30:22

Avery: and what

686

: 00:30:22

advice would you give someone

who's considering the accelerator

687

: 00:30:26

Brandon Traditi: Do it?

688

: 00:30:28

Do it.

689

: 00:30:29

Um, it's a great community.

690

: 00:30:31

Um, I, I look back and I, I wish

I was a little more in it and,

691

: 00:30:36

and a little more on the boards.

692

: 00:30:38

I would, I feel a little more,

I was behind the scenes and

693

: 00:30:41

kind of, you know, did it.

694

: 00:30:42

But it is, at the end of

the day, a great community.

695

: 00:30:45

Um, you are awesome.

696

: 00:30:47

I wouldn't really be where I am

today if I didn't start with DAA.

697

: 00:30:52

honestly.

698

: 00:30:53

You know, if I, if I never found

your podcast, if I never found

699

: 00:30:56

your program, I don't know.

700

: 00:30:58

If this ever happened

or where I, I am today.

701

: 00:31:03

So I would just say, do it.

702

: 00:31:05

Do it.

703

: 00:31:05

Have fun with it, do

it, and enjoy yourself.

704

: 00:31:10

Avery: Well,

705

: 00:31:10

I'm glad that, uh, that you did it and,

706

: 00:31:13

uh,

707

: 00:31:14

I'm excited to have you

708

: 00:31:16

come

709

: 00:31:17

and be more vocal

710

: 00:31:18

as an

711

: 00:31:21

session,

712

: 00:31:23

Tableau.

713

: 00:31:24

now that

714

: 00:31:24

that

715

: 00:31:27

than me for sure.

716

: 00:31:28

That's, that's for

717

: 00:31:29

Uh,

718

: 00:31:30

Brandon,

719

: 00:31:33

sharing your story.

720

: 00:31:34

Uh, we'll have a link

721

: 00:31:37

down below

722

: 00:31:38

and you,

723

: 00:31:39

it

724

: 00:31:40

from him.

725

: 00:31:41

that okay with you, Brandon?

726

: 00:31:42

Brandon Traditi: Yeah, absolutely.

727

: 00:31:44

Avery: Well, thank you

728

: 00:31:45

we'll see you in the next episode.

729

: 00:31:46

Brandon Traditi: Thank you so much, Avery.

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