Music therapist to Fortune 50 financial analyst in under 60 days. Here's exactly how Erin did it without a traditional background.

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⌚ TIMESTAMPS

01:20 – From music to Fortune 50

07:18 – What a good boss actually does

12:51 – 60 days from sign up to offer

20:18 – Applying while building skills

20:36 – One resume tweak, three interviews

23:03 – Stop only applying to remote jobs

28:39 – What the interview was like

34:40 – Do this if you're starting over

🔗 CONNECT WITH ERIN SHINA

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💻 Website: https://datacareerjumpstart.com/

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You were working as a music therapist and

now you're able to work as a financial

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analyst for a Fortune 50 company.

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I am a financial analyst

at a healthcare company.

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But you were able to land this,

this financial analyst role

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pretty quickly signed up, clicked

submit on the accelerator package

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like right before Christmas.

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Started actually like doing the

program right after Christmas.

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I.

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Job was on March 1st, which

was a Wednesday, and then that

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Friday I accepted my offer.

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That's not even 60 days.

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So one of the projects I think that

really helped me was the SQL Project

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and the Accelerator, the, that was

something that I talked about in

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all of the interviews that I had.

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Today I'm really excited about my guest.

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We have one of the, uh, members of

the data Analytics Accelerator who has

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gone through a portion of the program

and landed a pretty sweet job that

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we're gonna be talking about today.

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Uh, my guest today is Aaron Sheena.

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Aaron, welcome to the Data Career Podcast.

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Thank you.

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So happy to be here.

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Yeah.

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So excited that you, uh, agreed to come

on the show and talk a little bit, uh,

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about your, your journey, which I think

is something that's really unique and

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something that needs to be told because

you have a pretty interesting background.

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Uh, you studied music in school,

but you no longer work in music.

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So let's start off with what

you're currently doing now.

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What do you do for work now?

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Sure.

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So, um, I am a financial analyst, um, at

a healthcare company, um, called Humana.

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Uh.

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Nationwide.

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Um, and essentially I

work in risk adjustment.

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Um, so basically looking at claims

data, the data that comes through

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anytime you go to the doctor, um,

and make sure that we're analyzing

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and filtering it correctly compared

to the government agency that runs.

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Medicare, um, and making sure that

we are kind of aligning with them so

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that we can predict how much we'll be

reimbursed for caring for those members.

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Um, and so basically we then take that

analysis, um, and, uh, use it to help

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us predict revenue and make projections

for, um, both what we'll get paid for.

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During this year and then

in future years as well.

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Um, so yeah, that's kind of

the, the really paired down, uh,

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version of, of what I'm doing

in a kind of complicated space.

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But, um, yeah.

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Okay, sweet.

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That's awesome.

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So basically, you know, you have

two music degrees, I think you have

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a bachelor's and a master's degree.

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And now I think your official

title, is it a financial analyst?

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Is that what it is?

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Yes.

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Yeah.

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Um, so I, it's two

bachelor's degrees actually.

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I, um, my first one is in just kind

of general music, and then my music

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therapy degree is another bachelor's.

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Um, but, uh, yeah.

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So even less impressive, right?

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Well, I mean, that's perfect.

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So, so two different music degrees, a

music degree and a music therapy degree.

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Mm-hmm.

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You were working as a music therapist and

now you're able to work as a financial

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analyst for a Fortune 50 company.

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You know, solving problems when it comes

to healthcare billing, it sounds like.

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Mm-hmm.

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Yes.

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Correct.

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That's awesome.

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That, and that's the journey that's

a little foreshadowing of what

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we're gonna be talking about today.

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So we're gonna get into what your

background is and, and how you

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got to where you're at today.

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Um, but also it does look like,

I mean, I'm no expert, you know,

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I don't work for Humana, but I'm

guessing that your background back

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there is not the Humana offices.

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So can you tell us a

little bit, are you remote?

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Are you hybrid?

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Are you in the office right now?

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Uh, I'm not in the office.

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I'm in my office.

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Um, at home I am, I'm a hybrid employee,

so I do have one office day per week.

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Um, Humana is like headquartered

in Louisville where I'm from.

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Um, and so my team meets in the

office on Wednesdays and, um, which

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works for me really well since

I am a very extroverted person.

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Um, but the rest of the

time I am at home remote.

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Um, it's actually rare that I'm.

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In my office area instead of on the couch.

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So that, that's awesome.

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Um, and I'm guessing that was not the

case as a music therapist, am I right?

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Uh, it was not, no.

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I spent, um, every day, even through the

pandemic, um, every day at the hospital,

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spending most of my time in patient's

rooms, um, sitting with them and, and

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providing music and, um, you know,

going through that therapeutic process.

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Um, so a remote job was, uh.

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A very big change for me.

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Okay.

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And, and how has it been?

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I, I know you mentioned that you're

extrovert is, are you lonely at home?

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Is it is, do you get enough interface?

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Do you get enough support from your team?

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Yeah, I'm really, really lucky.

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My team is super, super supportive.

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Um, we use Microsoft Teams,

so I, I am my boss constantly.

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Um, you know, whether she likes it

or not, but, um, I, I do feel like

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I get enough kind of interaction

and, um, I really love my team.

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And getting to see them on Wednesdays,

but it's also really nice to just kind

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of be relaxed at home while I'm, you

know, working on, um, on my analysis and

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on, you know, all of my, my daily tasks.

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Um, and it, it feels.

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It feels very like, right.

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The pace is still good.

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I'm still, you know, kind of challenged

every day, but it's much different

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than, um, you know, having to like,

go into the hospital and, and kind

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of be part of that crazy environment.

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That's awesome.

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I I love hearing that because I love

that you're like, yeah, I'm, I'm in

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my o my own office today, but to be

honest, my real home office is my couch.

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I think that's awesome.

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Um, that you have the opportunity, you

know, the commute in the morning from the

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bedroom to the couch must be very mm-hmm.

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You know.

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Full of traffic and stuff like that.

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Uh, but, but you know, all jokes aside,

you had to deal with like a commute.

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You had to deal with traffic

in your last, last job.

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Right?

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That was a, a decent amount of driving.

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Yeah, I, I'm very lucky that I live close

to the hospital, um, where I was working.

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But yeah, I mean, it's, I still had to

get on the interstate, um, and, you know.

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Make my way.

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Um, sometimes traffic was worse

than others, but it's, yeah.

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I, uh, I much prefer when my dog is

the only one that's in my way trying

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to, trying to get to the office now.

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Yeah.

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That, that is awesome.

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Um, I also have a dog and I can

testify of the power of having

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your, your dog as your coworker.

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It is like so much fun.

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Mm-hmm.

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Um, and, and now, I mean, one of the

things that you probably couldn't do as

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easily when you were doing the hospital

visits is like, for instance, oh.

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Let's take, let's take the dog out

for a walk or you know, I got to

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feed the dog or I gotta take the

dog outside, or something like that.

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So I imagine that's gotten a lot easier

since you've been able to work remotely.

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Yes.

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Yeah, basil, my dog is, uh,

she is, her quality of life has

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increased even more than mine.

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So that's awesome.

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And that's what matters most, right?

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We don't, we don't really care about

our own lives, it's just about our Ps.

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Exactly.

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Yep.

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Exactly.

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Okay.

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Awesome.

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Um, and you mentioned that you're

able to, I am your boss, and

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that communication's going well.

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'cause one of the questions I get

is, you know, I want a remote job,

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but I'm also new to this field.

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And I'm kind of nervous that like, I'm

not gonna be able to get enough training

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or get enough support from my team.

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You felt like that's been

pretty good at Humana then?

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Yes.

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Yeah.

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Um, my boss is a really,

really wonderful mentor.

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Um, and.

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The, the kind of professional and

personal development that, um, my

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company invests in, um, has been

a really, really good support.

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Um, there are lots of like modules

and things that are provided just

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like by default from the company.

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Um, but then also my boss has been

really wonderful and, you know, we'll

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hop on a Zoom and I'll share my screen

and, you know, I'll say like this,

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I think this is what's giving me a

problem, but, um, I can't, you know,

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figure out what I need to change or.

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What does, what does this actually mean?

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Um, and she can tell me and she'll

kind of help me puzzle through it and,

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and figure out, um, you know, where I

went wrong or how I should approach it

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in the future or what, what to tweak.

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Um, and so that's really, really helpful.

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That's awesome.

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So you not only have, like your boss,

you're able to, you know, message

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anytime you get stuck, but you also

have some sort of provided learning

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so that you're not like, stuck with

the skills that you're at right now.

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You can kind of upskill as you

go, it sounds like as well, right?

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Mm-hmm.

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Exactly.

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Yes.

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Yeah, my, uh, my next thing to tackle

is, um, getting into some python for like

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moving data from one place to another.

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So, um, I'm excited to get started

on that in the next couple of weeks.

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Sweet.

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That's awesome.

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Very cool.

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Um, okay, awesome.

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So actually, wh while, while

you've mentioned Python mm-hmm.

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Let's talk about, as you know, an entry

level financial analyst new to the field.

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What type of, what type of tools

are you using on a day-to-day basis?

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Yeah, so biggest one is sql.

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Um, we use SQL Server.

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Um.

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And kind of the whole, like

Microsoft Suite, all of that.

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Um, lots of excel for the kind

of like financial part of it.

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Um, but most of my analysis

and most of the testing that

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we're doing is within sql.

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Um, and yeah, that's been, it's

been really fun to kind of take,

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uh, the skills that I know like.

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Uh, just in my own little like, simple

projects into, you know, actual like

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millions and millions of rows of data.

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Um, and, you know, see,

see how it translates.

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Yeah.

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I'm sure some of it is very similar, like,

like you kind of have the base for it,

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but it's probably like you're doing things

you might not have necessarily expected.

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Um, and using things kind of in a new

way with his, with his new application.

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Mm-hmm.

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Yes.

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Yeah.

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There and there's a lot

of, um, kind of logical.

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Like analytical thinking.

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Um, and you know, that's part

of the learning curve of, of.

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Going into, you know, this specific

industry, um, like healthcare.

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I thought, you know, being in the

hospital every day, I thought I knew

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00:10:01,920 --> 00:10:07,050

all of the acronyms, um, that came

with like the medical, you know, field.

222

00:10:07,410 --> 00:10:09,270

Um, but apparently I didn't.

223

00:10:09,480 --> 00:10:11,430

Health insurance is

like totally different.

224

00:10:11,430 --> 00:10:14,100

So, um, yeah, lots of acronyms.

225

00:10:14,160 --> 00:10:19,260

Um, lots of kind of the, the

logical analytical thinking to get

226

00:10:19,680 --> 00:10:22,805

from point A to point B and then

figure out how to get there in sql.

227

00:10:24,810 --> 00:10:25,230

Okay.

228

00:10:25,290 --> 00:10:25,890

I love that.

229

00:10:25,949 --> 00:10:28,500

And that's something that we

didn't necessarily talk about.

230

00:10:28,500 --> 00:10:29,880

We, we, I mean, we mentioned

your background, we

231

00:10:29,880 --> 00:10:31,110

mentioned your music degrees.

232

00:10:31,410 --> 00:10:34,469

We, we said the term music

therapy a couple times.

233

00:10:34,709 --> 00:10:37,050

I wasn't familiar with

music therapy beforehand.

234

00:10:37,230 --> 00:10:40,199

So, um, maybe, if you don't mind,

will you just give like a, a quick

235

00:10:40,199 --> 00:10:41,640

introduction to what you were doing?

236

00:10:41,670 --> 00:10:44,100

You kind of mentioned it

earlier, but if I was, if I was a

237

00:10:44,100 --> 00:10:45,750

5-year-old, what is music therapy?

238

00:10:46,665 --> 00:10:47,205

Sure.

239

00:10:47,235 --> 00:10:52,245

So, um, you know, the go-to line, I

guess, is that music therapy is using

240

00:10:52,245 --> 00:10:54,675

music to accomplish non-musical goals.

241

00:10:54,885 --> 00:10:59,145

So, um, in the hospital, what I was

doing most of the time was using music

242

00:10:59,145 --> 00:11:03,915

for decreasing pain, decreasing anxiety,

um, kind of providing that additional

243

00:11:03,915 --> 00:11:08,115

emotional support, um, that people often

don't get, um, especially when they're

244

00:11:08,115 --> 00:11:10,125

going through something that is, you know.

245

00:11:10,484 --> 00:11:13,814

Potentially like traumatic and scary

for them, like being in the hospital.

246

00:11:14,175 --> 00:11:18,405

Um, and you know, we worked

really closely with our like

247

00:11:18,614 --> 00:11:20,204

hospice and palliative care team.

248

00:11:20,505 --> 00:11:23,084

Um, so a lot of, uh, folks

who were going through kind of

249

00:11:23,084 --> 00:11:25,574

the end of life process, um.

250

00:11:26,700 --> 00:11:30,030

And Yeah, that's kind of what

my, my daily life was like.

251

00:11:30,840 --> 00:11:31,170

Yeah.

252

00:11:31,170 --> 00:11:34,800

And, and I think that's important to

realize because one of the tips that

253

00:11:34,800 --> 00:11:38,370

we, that we talk about throughout

the data analytics accelerator is,

254

00:11:38,370 --> 00:11:40,140

can we find you a stepping stone job?

255

00:11:40,140 --> 00:11:40,290

Right?

256

00:11:40,290 --> 00:11:43,860

Because you're new to this

world of, of data, right?

257

00:11:43,860 --> 00:11:45,270

You're, you're new to this world of data.

258

00:11:45,270 --> 00:11:50,280

You had no prior, you know, math

jobs or science jobs, right?

259

00:11:50,280 --> 00:11:51,810

It was, it was the music stuff, right?

260

00:11:52,050 --> 00:11:54,480

Um, but you were able to land this.

261

00:11:54,975 --> 00:11:57,375

This financial analyst

role pretty quickly, like

262

00:11:57,375 --> 00:11:58,905

within, within about 90 days.

263

00:11:58,905 --> 00:12:01,875

I was looking, I was trying to look, find

our exact, when you told me you had the

264

00:12:01,875 --> 00:12:05,145

interview and you had the offer, um, and

when you joined, you joined like right

265

00:12:05,145 --> 00:12:08,985

before Christmas, and then I think you

started working like mid-March, right?

266

00:12:09,704 --> 00:12:09,824

Mm-hmm.

267

00:12:10,755 --> 00:12:11,295

Yes.

268

00:12:11,475 --> 00:12:11,834

Yeah.

269

00:12:12,255 --> 00:12:20,084

Um, so I, I like, you know, signed up,

um, clicked submit or whatever on the.

270

00:12:20,625 --> 00:12:23,145

Accelerator package, like

right before Christmas.

271

00:12:23,535 --> 00:12:27,944

Um, started actually like doing the

program right after Christmas, like the

272

00:12:27,944 --> 00:12:29,444

week before Christmas and New Year's.

273

00:12:29,954 --> 00:12:33,855

Um, and I, I think my job.

274

00:12:35,070 --> 00:12:40,530

The interview was on March 1st, um,

which was a Wednesday or whatever

275

00:12:40,530 --> 00:12:41,520

the Wednesday was that week.

276

00:12:41,610 --> 00:12:43,890

And then that Friday I accepted my offer.

277

00:12:44,670 --> 00:12:49,140

Um, so yeah, I was,

uh, not expecting that.

278

00:12:49,590 --> 00:12:50,100

Um.

279

00:12:50,625 --> 00:12:52,755

That's not even, that's not even 60 days.

280

00:12:52,785 --> 00:12:54,824

'cause February is not even 30 days.

281

00:12:54,824 --> 00:12:55,155

Right.

282

00:12:55,155 --> 00:12:56,115

So that's basically Right.

283

00:12:56,415 --> 00:12:56,895

Yeah.

284

00:12:56,955 --> 00:12:58,755

So that's awesome.

285

00:12:59,025 --> 00:13:01,725

Um, and absolutely incredible.

286

00:13:01,785 --> 00:13:02,895

Congratulations.

287

00:13:02,900 --> 00:13:03,040

Thank you.

288

00:13:03,040 --> 00:13:03,600

I cut you off.

289

00:13:03,600 --> 00:13:03,800

Thank you.

290

00:13:03,830 --> 00:13:06,135

Keep, keep talk, talking us

through that, through that journey.

291

00:13:07,395 --> 00:13:07,785

Yeah.

292

00:13:07,785 --> 00:13:14,564

So, um, I should back up and say that part

of what I was doing, um, before kind of

293

00:13:14,564 --> 00:13:19,725

getting into the, into the accelerator

bootcamp, um, you know, when I.

294

00:13:21,195 --> 00:13:25,635

Was looking into data analytics as

a potential, you know, career move.

295

00:13:26,085 --> 00:13:30,645

Um, I did kind of what anybody does and

just Googled it, um, and landed on the

296

00:13:30,645 --> 00:13:32,775

Google, the Coursera, like Google mm-hmm.

297

00:13:33,015 --> 00:13:34,425

Data analytics program.

298

00:13:34,485 --> 00:13:34,575

Mm-hmm.

299

00:13:34,905 --> 00:13:36,405

Um, and I was doing that for a while.

300

00:13:36,405 --> 00:13:38,265

I think I started that

sometime in the summer.

301

00:13:38,685 --> 00:13:40,065

Um, last year.

302

00:13:41,205 --> 00:13:44,685

Felt, um, like I was understanding things.

303

00:13:44,985 --> 00:13:45,630

Um, I didn't.

304

00:13:46,620 --> 00:13:49,260

I didn't have any, you know,

foundational knowledge besides

305

00:13:49,260 --> 00:13:51,270

like, using Excel for budgeting.

306

00:13:51,780 --> 00:13:56,610

Um, and you know, I think it was a really

good introduction into like, what is data

307

00:13:56,610 --> 00:13:58,350

analytics and all of that sort of thing.

308

00:13:58,350 --> 00:14:02,850

But I didn't, um, I made it like

three quarters of the way through,

309

00:14:02,850 --> 00:14:06,240

but I didn't feel like I could like

actually apply my knowledge in a

310

00:14:06,240 --> 00:14:08,939

way that was, um, helpful for me.

311

00:14:09,300 --> 00:14:13,230

I was like understanding it as I was going

through, but there wasn't a lot of like.

312

00:14:14,114 --> 00:14:15,314

There weren't any steps after that.

313

00:14:15,464 --> 00:14:20,354

So, um, I was looking for something

that was just more hands-on

314

00:14:20,354 --> 00:14:22,395

and more like active for me.

315

00:14:22,724 --> 00:14:24,495

Um, that's how I tend to learn best.

316

00:14:24,495 --> 00:14:27,495

And so I was, you know, just kind of

looking to see what was out there.

317

00:14:27,495 --> 00:14:32,564

And, um, I think, I think you had

a sale going on, I saw on LinkedIn

318

00:14:32,564 --> 00:14:34,515

and I was like, that sounds good.

319

00:14:35,685 --> 00:14:40,334

Um, you know, most boot camps are

like five grand plus, and you know,

320

00:14:40,334 --> 00:14:42,074

that's not something that in my.

321

00:14:42,675 --> 00:14:48,195

Previous job that I could even consider

budgeting for, um, in any kind of,

322

00:14:48,255 --> 00:14:51,675

you know, uh, reasonable timeline

for wanting to make a career move.

323

00:14:51,675 --> 00:14:54,945

So, um, I was like, sounds great.

324

00:14:55,305 --> 00:14:56,205

This guy's cool.

325

00:14:56,205 --> 00:14:57,975

I'm gonna just do this

and see where it goes.

326

00:15:00,165 --> 00:15:05,745

And so from there, that's kind of

when I, uh, started like doing.

327

00:15:06,150 --> 00:15:10,440

Doing the Analytics Accelerator

Bootcamp, um, and the curriculum,

328

00:15:10,710 --> 00:15:13,350

and I think it, that is what really.

329

00:15:13,845 --> 00:15:15,045

Made a big difference for me.

330

00:15:15,795 --> 00:15:16,305

Yeah.

331

00:15:16,365 --> 00:15:19,785

I, I think, I think your story

is, you know, very similar.

332

00:15:19,785 --> 00:15:23,355

In fact, someone emailed me today and

said, you know, how is your program

333

00:15:23,355 --> 00:15:24,704

different than the Google data cert?

334

00:15:24,944 --> 00:15:27,220

Um, which is, which is a

common question, um mm-hmm.

335

00:15:27,465 --> 00:15:30,585

And I think, I think you kind of

nailed it, like actually applying

336

00:15:30,675 --> 00:15:32,055

what we're, what you've learned.

337

00:15:32,055 --> 00:15:32,475

Right.

338

00:15:32,775 --> 00:15:37,005

Um, and then really focusing on creating

the projects and the networking.

339

00:15:37,005 --> 00:15:37,305

Right.

340

00:15:37,305 --> 00:15:38,085

Because mm-hmm.

341

00:15:38,324 --> 00:15:39,944

At the end of the day, if you don't

have the projects, you don't have the

342

00:15:39,944 --> 00:15:42,824

network, it's a lot harder to land

that job and then also just doing it.

343

00:15:42,824 --> 00:15:42,834

Mm-hmm.

344

00:15:43,439 --> 00:15:46,560

With someone, like someone that's

able to, you know, talk to you.

345

00:15:46,560 --> 00:15:49,170

I know you were pretty active on our

community, so having all the peers

346

00:15:49,170 --> 00:15:52,530

around you, uh, I think, I think that's

pretty helpful for, for most people.

347

00:15:52,829 --> 00:15:57,030

Um, and the other thing you

did really well is, and I mean,

348

00:15:57,334 --> 00:15:58,530

I, I think this was helpful.

349

00:15:58,530 --> 00:16:03,295

You can tell me if I, if I'm wrong, um,

but you were trying to land a data job.

350

00:16:04,245 --> 00:16:07,125

You don't have necessarily the,

what people would consider the

351

00:16:07,125 --> 00:16:09,435

traditional or the ideal background.

352

00:16:09,435 --> 00:16:12,675

I don't think there is a traditional

or ideal background for data analytics,

353

00:16:12,675 --> 00:16:13,905

but that's, that's besides the point.

354

00:16:14,265 --> 00:16:19,395

Um, but you found this job inside of

healthcare and you have been working

355

00:16:19,454 --> 00:16:20,984

in healthcare as this music therapist.

356

00:16:20,984 --> 00:16:24,584

You've been visiting hospitals, like you

said, you know, hospitals speak a little

357

00:16:24,584 --> 00:16:26,084

bit like the acronyms and stuff like that.

358

00:16:26,295 --> 00:16:28,755

Did that play a role in

helping you land this job?

359

00:16:28,755 --> 00:16:30,464

Like, was that helpful to

know the hospital stuff?

360

00:16:31,305 --> 00:16:32,505

Yeah, I think so.

361

00:16:32,625 --> 00:16:36,675

Um, you know, especially during

my interview process, that was

362

00:16:36,675 --> 00:16:37,965

something that I spoke to a lot.

363

00:16:38,415 --> 00:16:43,785

Um, you know, and having kind of that

background knowledge of just how the

364

00:16:43,785 --> 00:16:48,735

industry works, um, and understanding

like, yeah, I might not know like the

365

00:16:48,765 --> 00:16:52,965

backend of health insurance, but I

know like what these things mean and

366

00:16:52,965 --> 00:16:56,745

I know, you know, kind of why things

are set up the way that they are.

367

00:16:57,195 --> 00:16:59,480

Um, even if, I dunno the details of like.

368

00:17:00,585 --> 00:17:03,435

How, how it works on the backend.

369

00:17:03,555 --> 00:17:08,595

Um, and one of the projects I think that

really helped me kind of be able to speak

370

00:17:08,595 --> 00:17:14,865

to that was the SQL project, um, and the

accelerator, the, um, healthcare analysis.

371

00:17:15,165 --> 00:17:18,105

That was something that I talked about

in all of the interviews that I had.

372

00:17:18,493 --> 00:17:21,795

Um, and they were really, really

interested to know, you know, not

373

00:17:21,795 --> 00:17:25,395

only to see that I had some SQL

skills, but also just to see like.

374

00:17:26,339 --> 00:17:31,590

I had used my prior knowledge and

like, um, how I had applied that

375

00:17:31,590 --> 00:17:36,210

understanding of the industry

to the analysis, like with sql.

376

00:17:36,720 --> 00:17:40,170

Um, so that I think was

really, really helpful for me.

377

00:17:40,500 --> 00:17:40,860

Um.

378

00:17:41,760 --> 00:17:45,780

Yeah, in that whole interview process,

gosh, I actually, I mean, I should have

379

00:17:45,780 --> 00:17:49,590

realized that, but I didn't even, I

didn't even realize that, and that's so

380

00:17:49,590 --> 00:17:53,580

cool because they were like, Hey, we're

hiring for a financial data analyst role.

381

00:17:53,850 --> 00:17:56,700

The the hope is that someone

will understand data analytics,

382

00:17:56,970 --> 00:17:57,775

they'll understand sql.

383

00:17:58,415 --> 00:18:01,595

They'll, they'll understand

hospital, or I guess healthcare data.

384

00:18:01,865 --> 00:18:04,985

And you were like, oh, well here's a

project I've done that you can read

385

00:18:04,985 --> 00:18:08,315

where I analyzed, I can't remember

how much data is in that one, like 1.6

386

00:18:08,315 --> 00:18:10,505

million rows of hospital data.

387

00:18:10,510 --> 00:18:10,580

Mm-hmm.

388

00:18:10,660 --> 00:18:13,955

And like looked at outcomes and

like looked at like what procedures

389

00:18:13,955 --> 00:18:16,805

led to these different things

and how race played in role in

390

00:18:16,805 --> 00:18:17,765

the hospital and stuff like that.

391

00:18:17,945 --> 00:18:19,025

And you're like, just, here you go.

392

00:18:19,025 --> 00:18:20,555

This is, this is my evidence.

393

00:18:20,555 --> 00:18:20,825

Right.

394

00:18:20,825 --> 00:18:22,415

Are you interested, Uhhuh?

395

00:18:22,985 --> 00:18:24,755

That must have been really

powerful for the recruiter.

396

00:18:24,755 --> 00:18:25,385

They're like, oh.

397

00:18:25,950 --> 00:18:26,370

Wow.

398

00:18:26,490 --> 00:18:28,350

Uh, SQL project with healthcare data.

399

00:18:28,800 --> 00:18:32,040

I'm, I'm sure they didn't have

very many other projects like that.

400

00:18:32,040 --> 00:18:33,120

If I were to guess, I don't know.

401

00:18:33,180 --> 00:18:36,360

But if I were to guess, I don't

know who else applied, but

402

00:18:36,420 --> 00:18:37,500

apparently I did something right.

403

00:18:37,500 --> 00:18:38,460

So, yeah.

404

00:18:38,615 --> 00:18:41,190

'cause, 'cause you interviewed and then

like three days later had an offer.

405

00:18:41,790 --> 00:18:41,940

Mm-hmm.

406

00:18:43,050 --> 00:18:43,260

Yeah.

407

00:18:44,205 --> 00:18:46,784

That's amazing con that's so amazing.

408

00:18:46,784 --> 00:18:47,355

Congrats.

409

00:18:47,415 --> 00:18:47,804

Thank you.

410

00:18:48,824 --> 00:18:52,875

Um, one, one of the things I just wanna,

I wanna highlight, um, that makes me

411

00:18:52,875 --> 00:18:57,105

so happy to hear, because when I was

designing the, the analytics accelerator,

412

00:18:57,495 --> 00:18:59,835

I was like, okay, we have to do projects.

413

00:18:59,835 --> 00:19:04,004

And to be perfectly honest, uh,

when I first got into helping people

414

00:19:04,034 --> 00:19:07,845

land data jobs, I, I had the same

philosophy that projects were the way.

415

00:19:08,610 --> 00:19:10,920

But I had a little bit of a

different twist where I was

416

00:19:10,920 --> 00:19:12,395

like, projects should be fun.

417

00:19:12,945 --> 00:19:13,635

It's always fun.

418

00:19:14,310 --> 00:19:16,020

To do your own personal data.

419

00:19:16,020 --> 00:19:19,260

So when I originally launched my

bootcamp, all of the projects were

420

00:19:19,260 --> 00:19:23,460

actually from your own life, like

your own screen time on your phone,

421

00:19:23,700 --> 00:19:27,659

the data, the, the music you listen

to, you know, and stuff like that.

422

00:19:27,870 --> 00:19:30,870

And that those projects were really fun

and I think they were very impressive

423

00:19:30,870 --> 00:19:32,190

to recruiters and hiring managers.

424

00:19:32,399 --> 00:19:36,120

They were a little bit harder because it's

just hard, keep getting your own data.

425

00:19:36,360 --> 00:19:38,250

But now that we've transitioned to.

426

00:19:39,255 --> 00:19:40,965

Using data from all the

different industries.

427

00:19:40,965 --> 00:19:44,715

I'm so happy to hear that we, when

I was choosing the nine industries

428

00:19:44,715 --> 00:19:47,265

for the nine projects, I was like,

man, there's so many industries.

429

00:19:47,265 --> 00:19:48,015

Which ones did we choose?

430

00:19:48,195 --> 00:19:51,765

And I'm so glad to hear that the

healthcare and the SQL combo was

431

00:19:51,765 --> 00:19:53,145

at least useful for one person.

432

00:19:53,175 --> 00:19:53,805

That's so good to hear.

433

00:19:53,810 --> 00:19:56,175

Yes, yes, for sure.

434

00:19:56,715 --> 00:20:00,855

Um, and actually I think just

having the projects in general

435

00:20:00,855 --> 00:20:03,375

and having the specific, um.

436

00:20:05,160 --> 00:20:09,090

Like specific tools for a

specific project, um, I think

437

00:20:09,090 --> 00:20:10,290

was really, really helpful.

438

00:20:10,830 --> 00:20:15,570

Um, and, you know, maybe

just also to highlight, uh,

439

00:20:15,570 --> 00:20:17,970

another aspect of the bootcamp.

440

00:20:18,270 --> 00:20:22,280

Um, I think the difference for me, I

was applying to things, um, kind of,

441

00:20:22,620 --> 00:20:24,630

you know, throughout the whole, uh.

442

00:20:25,034 --> 00:20:27,284

The time that I was, you

know, doing all the modules.

443

00:20:27,675 --> 00:20:31,425

Um, and I wasn't really getting

a lot of bites and, you know,

444

00:20:31,514 --> 00:20:35,655

kind of trying to network and,

and get referrals for, for jobs.

445

00:20:35,655 --> 00:20:40,665

But, um, I think the thing that made

the difference was having, I went

446

00:20:40,665 --> 00:20:44,504

to one of your, um, I don't remember

if it was a live session or just a

447

00:20:44,504 --> 00:20:49,274

module, but, um, all about resumes

and like optimizing your resume.

448

00:20:49,695 --> 00:20:52,125

Um, and so I added like.

449

00:20:52,665 --> 00:20:54,735

Literally, as I was watching, I

was like, okay, I'm gonna add these

450

00:20:54,735 --> 00:20:57,044

links to my portfolio projects.

451

00:20:57,044 --> 00:21:00,044

I'm gonna add, you know, a blurb about

what I learned, what I analyzed, what

452

00:21:00,044 --> 00:21:02,565

I, and why, um, and what I found.

453

00:21:02,985 --> 00:21:09,195

Um, and I like sent off a round of,

of applications kind of with that new

454

00:21:09,195 --> 00:21:13,125

resume, with my projects added like

an actual section, not just a link

455

00:21:13,125 --> 00:21:17,925

to my, um, to my portfolio site and.

456

00:21:18,524 --> 00:21:23,325

I like literally had three interviews

lined up for like that same week.

457

00:21:23,475 --> 00:21:23,534

Wow.

458

00:21:24,195 --> 00:21:25,935

From just that difference.

459

00:21:26,085 --> 00:21:32,145

Um, and that was, you know, one of the,

one of those interviews that I had is

460

00:21:32,145 --> 00:21:33,405

the role that I ended up accepting.

461

00:21:33,405 --> 00:21:34,995

So, and yeah.

462

00:21:35,024 --> 00:21:37,754

Did you, did you apply to that

job or did, did they find you?

463

00:21:38,804 --> 00:21:40,034

So I applied for it.

464

00:21:40,125 --> 00:21:44,625

Um, I have, uh, I know several people that

also work for Humana and I had someone

465

00:21:44,625 --> 00:21:47,294

who was willing to let me be a, um.

466

00:21:47,729 --> 00:21:49,709

Or be, you know, my referral.

467

00:21:50,070 --> 00:21:55,020

Um, so definitely worked kind of with my

network and, and connecting with people,

468

00:21:55,350 --> 00:22:03,810

um, to get my in, um, Humana as a company

that I have, you know, uh, considered

469

00:22:03,810 --> 00:22:05,580

working for, for, for a long time.

470

00:22:05,939 --> 00:22:09,810

Um, and you know, I know just

from having those personal, uh,

471

00:22:09,840 --> 00:22:12,030

relationships with people here, um.

472

00:22:12,450 --> 00:22:14,370

They're, they treat their

employees really well.

473

00:22:14,430 --> 00:22:17,010

And so I was like, oh yeah, that's

like a company I really wanna work for.

474

00:22:17,010 --> 00:22:21,600

So, um, that's kind of how I was

focusing my, my networking attention.

475

00:22:22,020 --> 00:22:27,570

Um, but yeah, I had very, very quick

turnaround from submitting those

476

00:22:27,570 --> 00:22:31,470

applications to hearing from recruiters

for the, the specific positions.

477

00:22:31,740 --> 00:22:32,160

Okay.

478

00:22:32,160 --> 00:22:33,600

There's a lot there I wanna unpack.

479

00:22:33,690 --> 00:22:38,820

Um, number one, did you apply on like

LinkedIn jobs Indeed, or on their website?

480

00:22:39,495 --> 00:22:40,335

On their website.

481

00:22:40,785 --> 00:22:44,715

Um, so I found them on LinkedIn,

but I went to the website to apply.

482

00:22:45,045 --> 00:22:45,255

Okay.

483

00:22:45,285 --> 00:22:46,725

Um, yeah.

484

00:22:47,115 --> 00:22:47,445

Okay.

485

00:22:47,745 --> 00:22:48,315

Two.

486

00:22:48,615 --> 00:22:53,295

Um, one of the things I wanna, I wanna

highlight here is, um, I don't know if

487

00:22:53,295 --> 00:22:58,065

you remember this, but the job, the job

description, it probably said hybrid

488

00:22:58,065 --> 00:22:59,625

on the job description, do you think?

489

00:23:00,045 --> 00:23:00,135

Mm-hmm.

490

00:23:00,135 --> 00:23:00,420

Or do you say in mm-hmm.

491

00:23:00,425 --> 00:23:00,585

Okay.

492

00:23:00,825 --> 00:23:05,205

And I wanna highlight that because I've,

no, this is like a super underrated play

493

00:23:05,205 --> 00:23:07,155

that everyone is sleeping on right now.

494

00:23:07,665 --> 00:23:09,825

And that's the idea of hybrid jobs.

495

00:23:09,975 --> 00:23:10,065

Mm-hmm.

496

00:23:10,305 --> 00:23:13,575

Everyone is like, oh, I

don't wanna be in person.

497

00:23:13,635 --> 00:23:14,025

Right.

498

00:23:14,145 --> 00:23:16,545

So they, they go and they go

to LinkedIn jobs, they use

499

00:23:16,545 --> 00:23:18,075

the Boolean search for remote.

500

00:23:18,105 --> 00:23:18,195

Mm-hmm.

501

00:23:18,495 --> 00:23:21,525

And they're competing with literally

thousands of other people for these

502

00:23:21,525 --> 00:23:27,015

remote jobs, because literally you

can be all over the US or the world.

503

00:23:27,015 --> 00:23:27,255

Right.

504

00:23:27,255 --> 00:23:28,905

And be an applicant for this job.

505

00:23:29,505 --> 00:23:29,595

Mm-hmm.

506

00:23:29,835 --> 00:23:32,955

But like your role, you're in the office.

507

00:23:32,955 --> 00:23:33,255

Like what?

508

00:23:34,050 --> 00:23:35,850

Eight hours a week on Wednesday.

509

00:23:35,855 --> 00:23:36,044

Mm-hmm.

510

00:23:36,129 --> 00:23:40,800

Like, if you want to like, you know, work

from home, from, you know, the rest of the

511

00:23:40,800 --> 00:23:45,270

days, you can, if you wanna, I don't know

Humana's exact policy, but let's just say

512

00:23:45,419 --> 00:23:49,770

you wanna go visit your, your parents,

or I don't know, your brother, right.

513

00:23:49,770 --> 00:23:50,564

You could, you could go, mm-hmm.

514

00:23:50,760 --> 00:23:54,179

You could leave Wednesday night and

come back home, you know, Tuesday night.

515

00:23:54,179 --> 00:23:57,645

Like that's like a week that you could

be working somewhere else, like you're.

516

00:23:58,215 --> 00:24:01,425

Not quite remote, but

you're 90% there, right?

517

00:24:01,425 --> 00:24:01,784

I guess.

518

00:24:01,784 --> 00:24:04,004

I guess literally 75%, right?

519

00:24:04,004 --> 00:24:04,695

Or what, what, what?

520

00:24:04,875 --> 00:24:05,715

80% there.

521

00:24:05,804 --> 00:24:07,125

Um, yeah.

522

00:24:07,125 --> 00:24:07,455

80.

523

00:24:07,455 --> 00:24:07,695

Yes.

524

00:24:08,264 --> 00:24:09,735

But, but it's, but it's

pretty good, right?

525

00:24:10,245 --> 00:24:10,455

Mm-hmm.

526

00:24:11,115 --> 00:24:11,534

Yeah.

527

00:24:11,534 --> 00:24:15,554

And, and like I said, I am a really

extroverted person, so I really

528

00:24:15,554 --> 00:24:19,965

like, you know, I, I think we get

less done on Wednesdays than we

529

00:24:19,965 --> 00:24:21,554

do, like when we're all remote.

530

00:24:21,975 --> 00:24:22,969

Um, you know, 'cause we're.

531

00:24:23,685 --> 00:24:27,375

Catching up with each other and, uh,

you know, socializing a little bit.

532

00:24:27,795 --> 00:24:32,265

Um, but yeah, we have, my team

has a lot of freedom outside of

533

00:24:32,265 --> 00:24:34,815

those, those, uh, in-person days.

534

00:24:35,175 --> 00:24:40,215

And, you know, if you do need to take

a, a remote day, that's also fine.

535

00:24:40,635 --> 00:24:45,110

Um, you know, I had a coworker

yesterday who was like, yeah, I just.

536

00:24:45,929 --> 00:24:48,510

I'm not feeling super well,

not well enough to not work,

537

00:24:48,510 --> 00:24:49,889

but can I just stay home today?

538

00:24:49,889 --> 00:24:52,290

And my team was like, yeah, of course.

539

00:24:52,290 --> 00:24:55,110

Just call into the meeting

that we have and we'll be fine.

540

00:24:55,830 --> 00:24:58,260

Um, so there's a lot of flexibility there.

541

00:24:58,785 --> 00:24:59,745

That's, and that's awesome.

542

00:24:59,745 --> 00:25:02,565

And I think people are like,

no, I only want a remote job.

543

00:25:02,565 --> 00:25:05,265

But the hard thing is like when

you're doing a remote job, you're

544

00:25:05,265 --> 00:25:08,535

competing with people you know,

not only in Louisville, right?

545

00:25:08,535 --> 00:25:10,815

You're competing with people all over

the country, but if you're hybrid mm-hmm.

546

00:25:11,055 --> 00:25:14,925

That job pool that they're selecting from

the candidate pool is so much smaller.

547

00:25:15,135 --> 00:25:17,295

And so you can stand out so

much more as a candidate.

548

00:25:17,565 --> 00:25:21,405

Um, the third thing I wanna mention is,

you know, you mentioned the referral.

549

00:25:21,405 --> 00:25:21,495

Mm-hmm.

550

00:25:21,765 --> 00:25:24,825

And people are gonna be like,

well, okay, I don't know anyone.

551

00:25:24,895 --> 00:25:29,995

You know, I, no one's gonna refer me, but

the people that referred you, what, what

552

00:25:29,995 --> 00:25:31,315

part of the company do they work for?

553

00:25:32,485 --> 00:25:33,295

Uh, not mine.

554

00:25:34,735 --> 00:25:42,085

Um, yeah, it's, uh, there are several

organizations kind of within Humana.

555

00:25:42,415 --> 00:25:46,825

Um, and they are in one that

is parallel with mine, but

556

00:25:46,825 --> 00:25:48,504

they are not in finance at all.

557

00:25:48,805 --> 00:25:50,435

Um, and so, you know.

558

00:25:51,720 --> 00:25:55,320

They, I even asked her, she, she like,

looked into some of the jobs when I was

559

00:25:55,320 --> 00:25:57,810

talking with her and she was like, yeah,

I don't know these hiring managers.

560

00:25:58,140 --> 00:26:03,000

Um, but I know this person who, um, you

know, worked with this other person and,

561

00:26:03,150 --> 00:26:09,150

you know, she kind of connected some

dots, but she, I didn't know, she didn't

562

00:26:09,150 --> 00:26:13,470

know anyone personally who was like, in

charge of hiring or, you know, the next

563

00:26:13,470 --> 00:26:17,190

like three steps up, um, from my manager.

564

00:26:17,190 --> 00:26:18,390

So, um.

565

00:26:19,395 --> 00:26:23,834

Yeah, I think it's a powerful thing

to, even if you aren't, if you can get

566

00:26:23,834 --> 00:26:27,375

a referral from someone, um, even if

they aren't directly involved with.

567

00:26:28,455 --> 00:26:30,645

The position that you're applying

for, I think it's really, really

568

00:26:30,645 --> 00:26:34,425

worth it to try to, you know, still

build those relationships and, um,

569

00:26:34,485 --> 00:26:35,595

and see if they can help you out.

570

00:26:36,375 --> 00:26:37,665

Yeah, a hundred percent.

571

00:26:37,695 --> 00:26:40,425

Like when I'm working with a lot

of people, they're like, Avery, I

572

00:26:40,425 --> 00:26:41,895

don't know anyone to get referrals.

573

00:26:42,165 --> 00:26:44,025

And the answer to that is bull crap.

574

00:26:44,025 --> 00:26:47,385

Unless you're like your brand new

to the country and you've never,

575

00:26:47,385 --> 00:26:50,445

like, you don't speak English, or

like you haven't really met people.

576

00:26:50,725 --> 00:26:53,485

Like, you at least know

someone who works somewhere.

577

00:26:53,815 --> 00:26:53,905

Mm-hmm.

578

00:26:53,911 --> 00:26:54,665

And sometimes, mm-hmm.

579

00:26:54,745 --> 00:26:59,185

Sometimes, sometimes that person's

gonna work at like, as a grocer at

580

00:26:59,185 --> 00:27:03,145

like, at like Smith's family grocer

and like, that's not gonna be useful.

581

00:27:03,415 --> 00:27:08,125

But like you've probably have at least,

you know, 20 contacts in your phone.

582

00:27:08,125 --> 00:27:12,595

Like open up your phone and go through

one by one and just be like, okay,

583

00:27:12,625 --> 00:27:14,275

Paul Adams, where does he work?

584

00:27:14,545 --> 00:27:16,375

Alejandra und, where does he work?

585

00:27:16,375 --> 00:27:17,785

Paul Alstrom, where does he work?

586

00:27:17,965 --> 00:27:19,285

You know, and think through.

587

00:27:19,649 --> 00:27:24,149

Do these people work for a company that

have an opening for a data analyst?

588

00:27:24,179 --> 00:27:24,735

Yes or no?

589

00:27:24,794 --> 00:27:25,215

Mm-hmm.

590

00:27:25,294 --> 00:27:28,620

And if they do, it doesn't matter if

they're in marketing or if they're

591

00:27:28,620 --> 00:27:32,490

in sales, or if they're, you know,

really doesn't really matter because

592

00:27:32,490 --> 00:27:33,814

the company just wants to kind of.

593

00:27:34,230 --> 00:27:36,060

To hire good people.

594

00:27:36,210 --> 00:27:38,370

And if that person's at that

company, that's probably because they

595

00:27:38,370 --> 00:27:39,870

think that person's a good person.

596

00:27:39,875 --> 00:27:40,125

Mm-hmm.

597

00:27:40,210 --> 00:27:43,200

And so if that person has a friend,

that's probably also another good person.

598

00:27:43,200 --> 00:27:46,920

And so just having any sort of

referral from any company employee,

599

00:27:46,920 --> 00:27:49,290

I think is worth exploring.

600

00:27:49,320 --> 00:27:51,510

And I think it gives you a

leg up in the application.

601

00:27:51,510 --> 00:27:55,410

So I think a job well done from you,

because you went for the hybrid, you

602

00:27:55,410 --> 00:27:59,190

went for the referral, and I mean,

that's what allows you to, you know,

603

00:27:59,190 --> 00:28:01,830

do an interview and then bam, you

have an offer like two days later.

604

00:28:04,035 --> 00:28:09,345

Yeah, it was, uh, it, it wasn't a

short process, you know, from, uh,

605

00:28:09,705 --> 00:28:15,975

starting getting into data at all to,

um, you know, accepting a job offer.

606

00:28:15,975 --> 00:28:22,455

But I think, um, the, the, the steps

that I took in the last, you know,

607

00:28:22,455 --> 00:28:25,875

couple of months of that journey, um,

really, really made the difference.

608

00:28:25,875 --> 00:28:26,835

And, um.

609

00:28:27,375 --> 00:28:30,645

Yeah, a lot of it was kind of

prompted by the accelerator

610

00:28:30,645 --> 00:28:32,355

program, so thank you for that.

611

00:28:33,315 --> 00:28:34,095

Yeah, of course.

612

00:28:34,095 --> 00:28:36,105

We're, I'm so glad it

it worked out for you.

613

00:28:36,345 --> 00:28:36,495

Mm-hmm.

614

00:28:36,735 --> 00:28:37,275

Um, okay.

615

00:28:37,275 --> 00:28:39,525

Before we let you go, I gotta ask you a

few more questions about the interview.

616

00:28:40,245 --> 00:28:40,335

Sure.

617

00:28:40,365 --> 00:28:41,325

Was it, was it technical?

618

00:28:42,675 --> 00:28:43,155

No.

619

00:28:43,455 --> 00:28:43,965

Um.

620

00:28:44,970 --> 00:28:50,190

Uh, and maybe part of it was because I

had projects to kind of show what I knew.

621

00:28:50,670 --> 00:28:55,980

Um, but we didn't, there wasn't like an

assessment for me, um, for any of the

622

00:28:55,980 --> 00:29:00,030

jobs that I interviewed for, you know,

kind of in that round of interviewing.

623

00:29:00,090 --> 00:29:03,540

Um, I talked about my projects a lot.

624

00:29:03,540 --> 00:29:07,200

They ask questions about the projects

themselves and kind of specifically what

625

00:29:07,200 --> 00:29:08,235

learned like the projects you had done.

626

00:29:08,605 --> 00:29:09,095

Learned.

627

00:29:09,100 --> 00:29:09,360

Yes.

628

00:29:09,360 --> 00:29:09,690

Yeah.

629

00:29:09,750 --> 00:29:13,649

So, um, the, the healthcare

one, um, I talked about.

630

00:29:14,580 --> 00:29:16,590

Um, oh, I forget which is which now.

631

00:29:16,620 --> 00:29:21,360

But, um, I talked about the, I

think Massachusetts education one.

632

00:29:21,360 --> 00:29:21,370

Yeah.

633

00:29:21,370 --> 00:29:21,545

Uhhuh.

634

00:29:21,810 --> 00:29:23,700

Um, I talked about that one.

635

00:29:24,120 --> 00:29:26,490

I talked a little bit about

the data visualization one

636

00:29:26,490 --> 00:29:27,630

that I had on my portfolio.

637

00:29:27,630 --> 00:29:33,600

But, um, yeah, I like, they would ask me

specific questions about, you know, like.

638

00:29:34,035 --> 00:29:35,295

What was your process with this?

639

00:29:35,295 --> 00:29:36,375

What did you know?

640

00:29:36,375 --> 00:29:41,235

How did you come to this conclusion

based on this data and, um, things

641

00:29:41,235 --> 00:29:47,775

like that rather than like, you

know, here is a, a data set.

642

00:29:48,015 --> 00:29:49,185

Can you query this?

643

00:29:49,185 --> 00:29:50,595

Like, I didn't have to do any of that.

644

00:29:50,595 --> 00:29:52,035

Like really, really technical stuff.

645

00:29:52,485 --> 00:29:56,445

I think because they could see that I

knew, you know, how to at least do it.

646

00:29:57,090 --> 00:30:01,230

Select from where statement, and then they

could ask me those deeper level questions.

647

00:30:01,740 --> 00:30:02,250

Um, yeah.

648

00:30:02,310 --> 00:30:06,060

Based on my portfolio, I think that's so

powerful because one of the things we talk

649

00:30:06,060 --> 00:30:13,320

about in DAA is that a lot of times the

people interviewing you are busy people

650

00:30:13,320 --> 00:30:15,210

and they don't wanna be interviewing you.

651

00:30:15,390 --> 00:30:18,090

And so they're coming in with

questions five minutes before

652

00:30:18,090 --> 00:30:19,020

they're actually doing the interview.

653

00:30:19,200 --> 00:30:22,080

That's not true of everyone, but a lot

of the times I've, I've hired people

654

00:30:22,085 --> 00:30:23,415

and I know that I've done that before.

655

00:30:23,515 --> 00:30:23,935

Mm-hmm.

656

00:30:24,020 --> 00:30:26,730

And so sometimes if

you give them projects.

657

00:30:27,014 --> 00:30:30,165

All of a sudden you just gave them

material for them to ask you questions

658

00:30:30,165 --> 00:30:33,945

about, and you kind of flipped the

interview where you're, you've almost made

659

00:30:33,945 --> 00:30:37,364

the interview about stuff that you know

and stuff that you've done versus them

660

00:30:37,364 --> 00:30:39,254

just like randomly asking you questions.

661

00:30:39,614 --> 00:30:44,205

Um, which I think is really, it

makes it way less nerve wracking and

662

00:30:44,205 --> 00:30:45,375

it makes you look more impressive.

663

00:30:45,375 --> 00:30:46,479

So I think, I think that's mm-hmm.

664

00:30:46,560 --> 00:30:47,175

A, a win-win.

665

00:30:47,324 --> 00:30:50,294

So overall, you felt prepared and

it was just the, the one interview.

666

00:30:51,645 --> 00:30:52,365

Uh, yes.

667

00:30:52,365 --> 00:30:58,635

So for the role that I had, um, it, it

was in person, um, which was helpful

668

00:30:58,635 --> 00:31:02,594

for me, um, because I, I tend to do

really well when I'm talking to people

669

00:31:02,594 --> 00:31:08,235

and, um, feel less nervous than, you

know, if I'm, um, if it's like a phone

670

00:31:08,235 --> 00:31:09,465

interview or something like that.

671

00:31:09,584 --> 00:31:12,495

Um, but we did, it was one.

672

00:31:12,840 --> 00:31:13,409

Day.

673

00:31:13,470 --> 00:31:16,080

Um, but interviews with

several people on the team.

674

00:31:16,470 --> 00:31:20,010

Um, but we had really similar

conversations kind of between that,

675

00:31:20,159 --> 00:31:23,730

um, as pertains to kind of their

role and, and the difference between

676

00:31:23,730 --> 00:31:25,260

the role that they're hiring for.

677

00:31:25,740 --> 00:31:28,080

Um, but yeah, I felt really prepared.

678

00:31:28,080 --> 00:31:33,960

I felt, um, like I knew what I was

talking about, kind of going in.

679

00:31:33,960 --> 00:31:37,230

I obviously had done these whole

projects and could speak on them.

680

00:31:37,679 --> 00:31:40,950

Um, and so that made me

feel really confident in.

681

00:31:41,355 --> 00:31:46,845

My skills and also in my like,

you know, presence and, and being

682

00:31:46,845 --> 00:31:48,915

able to really engage with them.

683

00:31:49,035 --> 00:31:51,945

Um, instead of being worried about,

you know, am I gonna remember how

684

00:31:51,945 --> 00:31:55,875

to, like what the syntax is for

this, you know, specific thing

685

00:31:55,875 --> 00:31:56,805

that they're gonna ask me about.

686

00:31:57,540 --> 00:31:57,929

Yeah.

687

00:31:58,050 --> 00:31:59,159

That's, that's awesome.

688

00:31:59,310 --> 00:32:02,189

Um, I love that the

projects brings confidence.

689

00:32:02,189 --> 00:32:03,149

That's an important takeaway.

690

00:32:03,149 --> 00:32:03,240

Mm-hmm.

691

00:32:04,199 --> 00:32:04,270

Um, yeah.

692

00:32:04,590 --> 00:32:04,980

Okay.

693

00:32:04,980 --> 00:32:07,169

And then, uh, we did have a question here.

694

00:32:07,199 --> 00:32:12,570

Um, you can answer this to your heart's

content, um, as much as, as you do or not.

695

00:32:12,780 --> 00:32:15,300

Um, but the question is, did you feel

the need to negotiate or were you

696

00:32:15,300 --> 00:32:17,250

pretty happy, uh, with your offer?

697

00:32:18,719 --> 00:32:20,459

So, um.

698

00:32:21,015 --> 00:32:24,945

As I mentioned, I had interviews

for three different roles, um, kind

699

00:32:24,945 --> 00:32:26,865

of, you know, all at the same time.

700

00:32:27,165 --> 00:32:30,855

Um, it was that like last round

of applications that I sent

701

00:32:30,855 --> 00:32:32,295

in after changing my resume.

702

00:32:32,895 --> 00:32:38,565

Um, had all those interviews within, you

know, that same week, the, um, the ones

703

00:32:38,565 --> 00:32:40,545

on that Wednesday where the last ones.

704

00:32:40,995 --> 00:32:47,385

Um, and a, the recruiter contacted

me, um, and actually said that I had.

705

00:32:48,284 --> 00:32:53,835

Um, gotten offers from all three and

that they wanted to, that they wanted

706

00:32:53,835 --> 00:32:57,524

to, um, you know, see what was my

preference and that sort of thing.

707

00:32:57,524 --> 00:33:04,605

And I didn't negotiate with numbers

necessarily, but I said that a, um,

708

00:33:04,965 --> 00:33:08,865

salary would put play a, a part in

my decision of which role to take.

709

00:33:09,405 --> 00:33:13,245

Um, and so I asked if they could give

me, you know, a range for each one.

710

00:33:14,115 --> 00:33:17,595

Um, so they came back with that and, um.

711

00:33:17,940 --> 00:33:22,440

You know, told me the ranges of what,

what they could offer for each role.

712

00:33:22,860 --> 00:33:28,140

Um, and then I was really happy that

the, the one that I had wanted the

713

00:33:28,140 --> 00:33:32,190

most did actually have, you know, the

highest offer as well, the highest range.

714

00:33:32,250 --> 00:33:35,370

Um, and so I, you know,

happily took that one.

715

00:33:35,910 --> 00:33:39,360

Um, so I didn't have to

necessarily negotiate.

716

00:33:39,480 --> 00:33:42,060

Um, and then when I like

accepted that offer, they.

717

00:33:42,449 --> 00:33:47,429

Um, said it was gonna be the, the highest

end of the, of the range they'd given me.

718

00:33:47,429 --> 00:33:48,780

They just gave me that top number.

719

00:33:48,959 --> 00:33:50,699

Um, so you got what?

720

00:33:50,699 --> 00:33:51,120

You got what you wanted?

721

00:33:51,659 --> 00:33:52,229

Exactly.

722

00:33:52,229 --> 00:33:52,409

Yeah.

723

00:33:52,409 --> 00:33:55,199

I didn't have to like,

you know, negotiate like.

724

00:33:55,770 --> 00:33:59,400

Face-to-face with somebody, but, um,

you know, letting them know that that

725

00:33:59,400 --> 00:34:00,720

was an important part of my decision.

726

00:34:01,200 --> 00:34:02,400

I, I think that's good.

727

00:34:02,460 --> 00:34:07,710

Um, we, you know, a lot of people will

talk about negotiating, um, and I think

728

00:34:07,710 --> 00:34:11,400

I'm probably not the, the best teacher

of negotiation, if I'm being honest.

729

00:34:11,400 --> 00:34:14,190

I've never really negotiated that

much, but so many students in

730

00:34:14,190 --> 00:34:15,565

our program have just been so.

731

00:34:16,060 --> 00:34:19,360

Happy with the offer that they get,

that, that, I mean, negotiating

732

00:34:19,360 --> 00:34:21,009

is always probably a good idea.

733

00:34:21,219 --> 00:34:23,620

Uh, but anyways, a lot of people

have just been super happy that

734

00:34:23,620 --> 00:34:24,370

they're like, I'll take it.

735

00:34:24,370 --> 00:34:25,989

I'm so, so stoked with this.

736

00:34:26,199 --> 00:34:26,440

Right?

737

00:34:26,440 --> 00:34:29,170

If someone offered me $10

million right now to go work

738

00:34:29,170 --> 00:34:30,880

for them, I'm not negotiating.

739

00:34:30,880 --> 00:34:32,679

I'm taking the 10 million

million dollars right.

740

00:34:32,880 --> 00:34:35,730

And, and I mean, uh, kind

of the same idea there.

741

00:34:35,820 --> 00:34:36,570

Um, mm-hmm.

742

00:34:36,810 --> 00:34:36,900

Okay.

743

00:34:37,050 --> 00:34:41,429

So one with this, uh, any advice that

you'd like to give to people with

744

00:34:41,429 --> 00:34:45,840

non-traditional backgrounds, people

with, um, maybe music backgrounds?

745

00:34:45,840 --> 00:34:48,810

What, what would you say and, and

that helped you in your journey?

746

00:34:48,810 --> 00:34:50,010

Or what, what advice would you give them?

747

00:34:50,835 --> 00:34:51,284

Yeah.

748

00:34:51,764 --> 00:34:56,835

Um, so number one piece of like

actionable advice is to do projects.

749

00:34:56,955 --> 00:35:01,215

Um, do projects that are based on

skills that you have or, you know,

750

00:35:01,215 --> 00:35:05,835

the industry that you have, and then

also ones that, um, show that you

751

00:35:05,835 --> 00:35:08,205

have knowledge for where you wanna go.

752

00:35:08,685 --> 00:35:12,735

Um, so if it's healthcare, if

it's, um, you know, marketing, like

753

00:35:12,735 --> 00:35:14,830

whatever you're trying to kind of.

754

00:35:15,570 --> 00:35:19,440

Break into, do projects with that

show that you know how to use the

755

00:35:19,440 --> 00:35:23,700

technical skills, um, in that industry.

756

00:35:24,330 --> 00:35:30,870

Um, and then just in general, I think,

um, something that I realized through

757

00:35:30,870 --> 00:35:35,130

my journey was that even if you are

like just starting and you don't really

758

00:35:35,130 --> 00:35:41,715

have a whole lot of like foundational

knowledge, um, to go off of, you are.

759

00:35:42,750 --> 00:35:46,470

Um, more capable and you know, a

lot more than you think that you do.

760

00:35:47,069 --> 00:35:50,009

Um, you know, everything

transfers to everything else.

761

00:35:50,370 --> 00:35:53,190

Um, I guarantee that there's

something that you, like do in your

762

00:35:53,190 --> 00:35:58,140

daily job that relates to, you know,

your, your data analytics learning.

763

00:35:58,740 --> 00:36:05,490

Um, and I think that if you have started

on this journey and you know, are, um.

764

00:36:05,895 --> 00:36:09,885

Even if you are at the beginning,

you've done the hard part of like

765

00:36:10,095 --> 00:36:11,685

making the decision and starting.

766

00:36:12,015 --> 00:36:17,145

Um, and so now it's just about consistency

and you know, keeping that going.

767

00:36:18,000 --> 00:36:19,230

I, I love that.

768

00:36:19,230 --> 00:36:21,240

I hope you guys listening

take that to heart.

769

00:36:21,480 --> 00:36:24,660

Keep in mind that was a music

therapist telling you that you have

770

00:36:24,660 --> 00:36:28,680

something in your current job that

is relatable to data analytics.

771

00:36:28,680 --> 00:36:31,980

If you asked me like the most opposite

of a data analytics job ever, I might

772

00:36:31,980 --> 00:36:35,640

say a music therapist, but you're

absolutely right Aaron, that no matter

773

00:36:35,640 --> 00:36:38,490

what you're doing, you can relate

something to what you're currently doing.

774

00:36:38,700 --> 00:36:41,040

It is experience for a data analyst job.

775

00:36:41,040 --> 00:36:44,190

So, you know, don't be discouraged

when you see, you know, one

776

00:36:44,190 --> 00:36:45,015

to two years of experience.

777

00:36:45,385 --> 00:36:46,735

Two to five years of experience.

778

00:36:46,735 --> 00:36:49,675

You have some sort of experience

that you can draw and I loved the

779

00:36:49,675 --> 00:36:51,325

advice, uh, on doing projects.

780

00:36:51,325 --> 00:36:54,535

So, uh, Aaron, thanks so

much for being on the show.

781

00:36:54,835 --> 00:36:58,405

Uh, we'll have your LinkedIn down

below in the show notes that people

782

00:36:58,405 --> 00:37:02,335

can connect with you and, uh, just

so excited for you and your journey.

783

00:37:02,335 --> 00:37:04,015

Aaron, thanks for sharing

it with all of us here.

784

00:37:04,795 --> 00:37:05,935

Thanks so much for having me.

785

00:37:06,780 --> 00:37:07,830

Yeah, no problem.

786

00:37:08,130 --> 00:37:09,570

Um, okay, great.

787

00:37:09,570 --> 00:37:10,890

Thanks everyone for listening.

788

00:37:10,890 --> 00:37:14,730

If you guys are listening live

on YouTube or LinkedIn, uh, we

789

00:37:14,730 --> 00:37:16,380

just wanna say hello to you guys.

790

00:37:16,650 --> 00:37:22,080

Um, also, if you guys did not know,

I'm doing a live version of the data

791

00:37:22,080 --> 00:37:27,120

analytics accelerator, um, starting

on Monday, and I want you guys to be

792

00:37:27,120 --> 00:37:29,310

part of that, um, awesome program.

793

00:37:29,310 --> 00:37:32,250

We're going to be, if you guys are

like, I want some more guidance, I

794

00:37:32,250 --> 00:37:34,020

want some more community, we're doing.

795

00:37:34,634 --> 00:37:36,915

Two hour live sessions every Monday.

796

00:37:37,004 --> 00:37:39,165

And then where I'm going to

be building the projects that

797

00:37:39,165 --> 00:37:42,765

Aaron talked about, we're gonna

build the SQL Hospital project.

798

00:37:42,765 --> 00:37:45,524

We're gonna build, uh, the

DoorDash marketing project.

799

00:37:45,524 --> 00:37:48,734

We're gonna build the education

Tableau project altogether

800

00:37:48,734 --> 00:37:49,754

on those Monday sessions.

801

00:37:49,754 --> 00:37:53,325

And then we're doing a live

office hour on Thursday.

802

00:37:53,325 --> 00:37:58,125

So if you're interested, you can go

to data career jumpstart.com/daa.

803

00:37:59,335 --> 00:38:03,265

Or you can just send me a DM on LinkedIn

and I'll get you the stuff you need.

804

00:38:03,265 --> 00:38:05,575

So I just wanna make sure y'all,

you guys know, 'cause that is an

805

00:38:05,575 --> 00:38:09,475

opportunity that, um, I haven't done

before where I'm actually building

806

00:38:09,475 --> 00:38:12,685

the projects and I'm going live for

three hours every week with you guys.

807

00:38:12,685 --> 00:38:15,055

So, uh, hopefully that's,

that's pretty exciting.

808

00:38:15,060 --> 00:38:17,335

That Great.

809

00:38:17,425 --> 00:38:18,265

Aaron, anything else?

810

00:38:19,555 --> 00:38:20,755

No, I don't think so.

811

00:38:20,755 --> 00:38:23,665

Thanks so much for having me and,

uh, good luck everybody listening.

812

00:38:24,265 --> 00:38:25,585

Yeah, sounds good.

813

00:38:25,585 --> 00:38:25,915

All right.

814

00:38:26,065 --> 00:38:26,995

Bye everyone.

815

00:38:27,595 --> 00:38:28,405

Let's see.