January 28, 2025 • 16 mins
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⌚ TIMESTAMPS
00:00 - Introduction
00:06 - Become laser-focused on your 'why'
03:34 - Focus on what matters to land the job
05:37 - Quit on being quiet, share your work!
07:51 - Networking
11:44 - Maximizing Your Time
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Avery (00:00):
Here are the five things you need to be doing to get ahead
of 99 percent of data analystsin the next three to six months.
Number one, become laserfocused on what you want.
If you're watching this video,chances are you want to become a data
analyst, but specifically what typeof data analyst do you want to become?
Do you want to become a financial analyst?
Do you want to becomea healthcare analyst?
What industry do you want to work in?
(00:20):
What companies do you want to work for?
What type of problemsdo you want to solve?
What tools do you want to use?
When do you want to land that job?
How much do you want to make in that role?
Do you want to be working remote?
Hybrid?
What type of impact do you wantto have at that organization?
You need to get into the nittygritty details of what you
actually want in your data career.
And once you've figured outthe what, Then you need to ask
(00:41):
why, why do you want that role?
Why that particular role, that particularcompany, why this career in general?
And then once you have that, whyyou need to ask why one more time,
what's the actual reason thatyou want to be working from home?
What's the actual reason you want tobe making a hundred thousand dollars.
Once you've figured out that why,you should ask yourself why again.
(01:02):
This is a method created bythe founder of Toyota, Mr.
Toyota himself.
When he was trying to figure out ananswer to a problem, he would ask why five
times until he found the ultimate rootcause of the desire or of the problem.
Once you've figured out what you wantand why you want it, then commit yourself
that you're actually going to do it.
That no matter the cost,you're going to figure it out
(01:22):
because your why is big enough.
There's that old phrase, whenthere's a will, there's a way.
And if your will is big enough,you'll figure out the way.
No matter your background, even ifyou don't have any sort of technical
experience, even if you're comingfrom a non STEM background or you
have no experience working at a deskjob at all, you will figure it out.
There's an old fable, uh, that's calledThe Crow and the Pitcher, that basically
(01:45):
there was a crow that was reallythirsty and it found a pitcher of water.
But the neck of the pitcher was too thinfor the crow to actually, you know, stoop
its neck down there and get a drink.
And I think a lot of us in thiscase would give up if we were the
crow and be like, Oh, look at this.
This pitcher is too small.
We're never going to beable to drink this water.
I'm just going to give up.
I'm going to say it's the economy.
(02:06):
I'm going to say it's the market.
I'm going to say it's just, youknow, bad luck, but not this crow.
This crow came up with a creativesolution and actually found small
stones that the crow could throw downinto the pitcher, ultimately raising
the level of water high enough thatthe crow could drink from the pitcher.
So I promise no matter your background,if you have a college degree, if you
(02:26):
don't have a college degree, if you'vebeen making six figures already, or
if you've only been making 10, 000a year, we can figure out a plan.
To get you to a data analyst job,but it's important that we need to
create a plan because when you failto plan, you should plan to fail.
Honestly, if you're just thinkingthat you're going to luck into a
data job, not in today's economy,it is so much harder to land a data
(02:48):
job today than it was a decade ago.
And you have to be intentional about it.
It's very rare that a data job isjust going to fall in your lap, even
if you're trying hard to land one.
You need to develop a plan, a personalroadmap of actual steps that you can
take one by one to land your data job.
This means when you sit down atyour computer to study, you should
know exactly what you're studyingand why you're studying it.
(03:09):
This rarely means that you shouldever sit down and be like, Hmm,
what am I going to do today?
No, you should know beforehand exactlywhat steps you're going to take.
I'm going to be posting on LinkedIn.
I'm going to leave five comments.
I'm going to work on my Tableau project,and then I'm going to call it a night.
This will help you get to your goalfaster, but it'll also keep your sanity.
When I'm tackling big projects, likecompletely pivoting my career, I
need to do it step by step, milestoneby milestone, and follow actionable
(03:33):
steps to get to the end goal.
And this actually leads me to numbertwo, which is to actually focus on what
lands you a job, not what feels good.
If I were to create a scatterplotof time it took to land the data job
against how skilled someone is atdata skills, say SQL, It would not
be a linear one to one correlation.
You'd like to think the people who arebetter at SQL would land data jobs more
(03:54):
quickly, but it's just not the case.
There's too many factors in play.
In fact, none of your data skillsreally correlate with how fast
you're going to land a job.
So why are you spending so muchtime learning new data skills
when that's actually not whatcorrelates to landing a job?
Later in this episode, I'll talkabout some of the things that I think
matter more than your data skillswhen you're landing a data job.
But it's really important that you'refocusing on what lands data jobs.
(04:17):
For example, if I stayed SQL 24 hours aday for the next 365 days, I'd be really
good at SQL, but I wouldn't magicallyland a data job because I'm good at SQL.
It would require me applying to data jobs.
There's no magical level that you'll hitin SQL, Python, or any other data tool.
That's going to magicallyget you a data job.
And at this point, honestly, ifyou haven't landed a technical
(04:38):
data interview and failed it, it'snot your skills holding you back.
It's something like your resumeor your LinkedIn profile.
Unless you're routinely failing technicalinterviews, you don't need to be working
on your technical skills all that much.
Once you have a good foundation.
So why is everyone stillworking on their data skills?
The phrase that best describes itis one that I don't really enjoy,
but I can't think of anything else.
(04:59):
I'm actually going to look inchat GPT right now to see if I
can come up with a better phrase.
Update.
I checked chat GPT.
I can't find a better phraseand it's mental masturbation.
It's the idea that what you're actuallydoing is making you feel good, but
it's really getting you nowhere.
Learning data skills, it makesyou feel more productive than
sending out 50 cold messages torecruiters and getting no responses.
(05:19):
That makes you feel rejection.
Learning data skills is fun.
It's your learning.
It makes you feel productive.
But you have to remember thatlearning data skills and landing a
data job are two different things.
They are related, but they'renot directly correlated.
So you have to lower your scopehere and actually be laser
focused on what you need to do.
What is the actual things, the stepsyou need to take to land a data job?
(05:40):
One easy thing that you can start doingto actually help you make some traction
on your data journey is number three.
And that is to quit being silentand actually share your work.
If you're not talking about whatyou're doing, you honestly don't exist.
Now, this can come in a variety of forms.
I challenge you to start postingon LinkedIn about what you're
learning, your daily data journey.
The reason I challenge you to do thatis because it literally changed my life.
(06:03):
And honestly, if I had never started doingthat, I wouldn't be making this video.
You would not be hearing my words.
I would just be some data scientistfrom the middle of nowhere in Utah.
But because I started talkingabout what I was doing, you
guys are hearing my voice today.
So start posting on LinkedIn.
Tell me what you learned today.
In fact, I challenge you right nowto pause this video and go post on
LinkedIn and share this video to talkabout one of the things you're going
(06:25):
to try to do is to talk more andexplain and document your process.
And I know some of you guys arethinking, uh, Avery, you are so cringe
and everyone's so cringe who posts onLinkedIn and maybe it is a little cringe.
Fine.
But are you willing to be a little cringegoing back up to number one and your why?
Your why strong enough to overcome that?
Personally, mine is, andI hope yours is as well.
(06:46):
If you can't post on LinkedIn forwhatever reason, or it's too scary to
get started, then just start talkingabout what you're doing in your resume.
Make sure your resume accuratelyis showing your career
pivot, build a portfolio.
Talk about what you'redoing on your portfolio.
It doesn't even have to befor, for the public eyes.
Other than when you're applying forjobs to hiring managers and recruiters,
instead of getting stuck in tutorial,how doing the same exercises that
(07:09):
the rest of the people watching thisYouTube video are doing, build a
project, talk about your project,do a writeup of your project, make a
video talking about what you've done.
Put it on a portfolio.
Think about this.
A recruiter or a hiring manager basicallylooks at your application for like
anywhere between three to seven seconds.
How are you going to stand outin those three to seven seconds?
How are they supposed to get an accuratedescription of who you are in that time?
(07:32):
The answer is they're not really goingto, but if you can provide them with
like some evidence, some stuff thatyou've actually done, like a project.
You're going to have a lot higherchance of earning their next 10
seconds, and then the next 60seconds, and then the next 60 minutes.
In today's economy, it's justnot enough to apply for jobs.
You have to actually be talkingabout what you're doing.
This leads me to number four,which is going to be controversial.
(07:54):
But you need to be living by theold fashioned maxim, it's not
what you know, it's who you know.
And that's just the truth.
70 percent of accepted job offerscome from being recruited or referred.
This basically means youneed to be networking.
Remember earlier how I toldyou skills aren't directly
correlated to getting hired?
Well, who you know and your networkis directly correlated at 70%.
Honestly, if that's the case andyou actually believe that like two
(08:16):
thirds of accepted job offers comefrom being recruited or referred,
Why aren't you spending two thirdsof your time working on your network?
The answer is, it doesn't feel good.
Networking sucks.
Especially at the beginning whenyou're just growing your network.
It feels pointless.
It feels awkward.
You don't know who to talk to.
You don't know what to say.
Look, I get it.
I was the same way.
I used to be you watching these videos.
(08:37):
And then I did number three and Istarted posting on LinkedIn and all
of a sudden my network was growing.
And one day I finally got thecourage to actually reach out to
Kate Strachney, who was a reallybig LinkedIn influencer at the time.
And I did a collaboration with her.
That was after, honestly, I sentdozens, if not hundreds of cold
messages that either kind of gotignored or didn't really lead anywhere.
They were all just dead ends.
(08:57):
And after I went to a bunchof in person data events and
I Didn't really meet anyone.
I honestly can name one personI met from those events.
Networking honestly feels pointlessuntil all of a sudden it doesn't.
And for me, one of the biggestchanges in my life is when I
actually reached out to Ken G.
Ken is a data scientist, YouTuber, whohas always had way more followers than me.
And one day I reached out and I actuallyinvited him to a platform that was
(09:21):
invite only at the time called Clubhouse.
It was basically like anaudio group call together.
It was kind of a weird product, but Ioffered him my only invite that I had.
And he really appreciated it.
And so we ended up doing a video togetherand he actually ended up introducing
me to people like Alex, the analyst,Josh Farmer, and a bunch of other data
content creators who I've now had thechance to interview on my podcast.
(09:43):
Getting connected to Ken, it.
Was lucky, I totally admit,I had a lot of luck in play.
He had to read my message, he hadto be interested, and Clubhouse
at the time, he had to be a goodperson and kind and want to help me.
But you can't just say it's luck, becauseI sent hundreds of other messages that
never got opened or never got replied to.
Networking, especially for introvertslike you and me, will always suck.
(10:05):
It's just if your why is bigenough, you embrace the suck.
This is what I meant earlier when I talkedabout, you know, learning SQL, learning
more SQL is fun, but it's not reallygetting you closer to your day to job.
When sending cold messages to recruiterswould get you closer to your job, but it
doesn't really feel like it until it does.
In fact, I had a stay at home momwho recently landed a data job.
(10:25):
She had been out of the workplacefor 20 years and her previous
roles were a teacher, so noteven closely related to data.
She landed a data job withonly one application, one
interview, and she got the offer.
She got lucky.
And if you just hear that, you wouldjust be like, oh, she got lucky.
And she did get lucky.
But what you aren't seeing behind thescenes is the hard work and dedication
she was putting in towards networking.
She found someone for this rolethat she could cold message.
(10:47):
Cold message them, no response.
Found another person,cold message, no response.
Found another person.
Cold message, no response.
I think most of uswould've given up, right?
Cold message someone else.
Response.
Said, sorry, can't help you.
I think we would've all given up there.
But not this person.
She sent another cold message toanother person that she found.
And this person said, oh, youhave an interesting resume.
(11:07):
Let me see what I can do.
Turns out that role wasn'teven supposed to be posted.
It was only an internal hire andthe recruiter had messed up and
actually opened it to the whole world.
So hundreds of you had applied for thatjob and you never stood any chance of
actually landing it because they had nointention of actually hiring externally.
But because my student had cold messagedthis person, their resume was in front
of the hiring manager's eyes already.
(11:28):
And the hiring manager said, well,this is a pretty interesting resume.
Let's take a look and let'sbring her in for an interview.
She got the role.
Networking will help you landjobs that aren't even open.
It'll open doors that are locked close.
And whether you like it or not,whether you're introverted or
not, that's the case for everyone.
The last thing that you can do, numberfive, to actually get ahead of data
analysts this year is mind the gap.
(11:50):
And what I mean by that iswe all have limited time.
We each have 24 hours in the day,no matter if you're Elon Musk or
the poorest person on planet Earth.
That's something that weall have is just 24 hours.
And some of you guys are working two jobs.
You're working like 80 hours a week.
You have kids.
I totally get that.
You're like, Oh my gosh, I don't knowwhen I'm going to do this, Avery.
Like how the heck amI going to learn this?
And my short answer is, I don'tknow how you do it either,
(12:12):
but here's my suggestion.
Mind the gap.
And what I mean by that is when yougo to the tube in London, they have
mind the gap painted on the ground.
And they're basicallysaying, pay attention.
To the space the empty space betweenthe edge of the platform and as you
step on the actual subway Obviouslygood advice if you're ever on the subway
But what does that have to do with youand your data career no matter how busy
(12:32):
you are and how many things you have?
On your schedule, you're always gonnahave little teeny tiny gaps in your
day I have them all the time andhonestly, I feel a lot of it with
Instagram scrolling looking on TwitterAnd watching YouTube videos kind of
like this and watching things like Mr.
Beast videos on YouTube.
My suggestion to you to get aheadof 99 percent of data analysts is to
fill that gap with videos like this.
(12:54):
Cut out as much fluff as you canin the gap and actually try to fill
it with things that are valuable,that are worth listening to.
I think that's one of the biggest thingsthat you can do in your data career is
actually be listening to stuff like this.
Because obviously if you're watching, it'sinvolving your eyes, a lot of time you're
going to be at a TV, at your phone, orat your desktop or something like that.
With audio, you can bedoing two things at once.
(13:15):
So for example, if you have any commuteright now, fill that commute with
listening to data YouTube videos.
If you found this video on YouTube,continue listening on YouTube.
If you found this via the podcast,keep listening on podcasts.
But obviously you don'tonly have to listen to me.
There's other great podcasts.
I really enjoy the Super Data ScienceShow, Plumbers of Data Science,
How to Land an Analytics Job, DataEngineering Podcast, DataViz Today.
(13:37):
In terms of YouTube channels,obviously Alex the Analyst is great.
I'm a big fan of Elijah Butler.
I just interviewed Tu Vu recentlyon my podcast and she's great.
I really like Mo Chen's videos and there'sobviously a lot of other good shows.
But fill your day and fillspecifically those gaps, specifically
with audio, with this good datacontent that's honestly free.
I promise as you do that, you willcontinue to learn and grow without
(14:00):
even having to spend more time.
So to recap, become laser focusedon what you actually want, a
title and the why behind it.
Then focus on what actually matters.
Cut out all the fluff and focuson the actual steps that's
going to land you a data role.
Then, quit being quietand actually speak up.
We want to hear what you're doingand you will benefit from it.
Remember, it's not what youknow, it's who you know.
(14:21):
And five, fill the gapwith content like this.
If you want to keep filling your gapwith my content, I highly suggest
this episode next and I'll haveit in the show notes down below.
Here are the five things you need to be doing to get ahead
of 99 percent of data analystsin the next three to six months.
Number one, become laserfocused on what you want.
If you're watching this video,chances are you want to become a data
analyst, but specifically what typeof data analyst do you want to become?
Do you want to become a financial analyst?
Do you want to becomea healthcare analyst?
What industry do you want to work in?
(00:20):
What companies do you want to work for?
What type of problemsdo you want to solve?
What tools do you want to use?
When do you want to land that job?
How much do you want to make in that role?
Do you want to be working remote?
Hybrid?
What type of impact do you wantto have at that organization?
You need to get into the nittygritty details of what you
actually want in your data career.
And once you've figured outthe what, Then you need to ask
(00:41):
why, why do you want that role?
Why that particular role, that particularcompany, why this career in general?
And then once you have that, whyyou need to ask why one more time,
what's the actual reason thatyou want to be working from home?
What's the actual reason you want tobe making a hundred thousand dollars.
Once you've figured out that why,you should ask yourself why again.
(01:02):
This is a method created bythe founder of Toyota, Mr.
Toyota himself.
When he was trying to figure out ananswer to a problem, he would ask why five
times until he found the ultimate rootcause of the desire or of the problem.
Once you've figured out what you wantand why you want it, then commit yourself
that you're actually going to do it.
That no matter the cost,you're going to figure it out
(01:22):
because your why is big enough.
There's that old phrase, whenthere's a will, there's a way.
And if your will is big enough,you'll figure out the way.
No matter your background, even ifyou don't have any sort of technical
experience, even if you're comingfrom a non STEM background or you
have no experience working at a deskjob at all, you will figure it out.
There's an old fable, uh, that's calledThe Crow and the Pitcher, that basically
(01:45):
there was a crow that was reallythirsty and it found a pitcher of water.
But the neck of the pitcher was too thinfor the crow to actually, you know, stoop
its neck down there and get a drink.
And I think a lot of us in thiscase would give up if we were the
crow and be like, Oh, look at this.
This pitcher is too small.
We're never going to beable to drink this water.
I'm just going to give up.
I'm going to say it's the economy.
(02:06):
I'm going to say it's the market.
I'm going to say it's just, youknow, bad luck, but not this crow.
This crow came up with a creativesolution and actually found small
stones that the crow could throw downinto the pitcher, ultimately raising
the level of water high enough thatthe crow could drink from the pitcher.
So I promise no matter your background,if you have a college degree, if you
(02:26):
don't have a college degree, if you'vebeen making six figures already, or
if you've only been making 10, 000a year, we can figure out a plan.
To get you to a data analyst job,but it's important that we need to
create a plan because when you failto plan, you should plan to fail.
Honestly, if you're just thinkingthat you're going to luck into a
data job, not in today's economy,it is so much harder to land a data
(02:48):
job today than it was a decade ago.
And you have to be intentional about it.
It's very rare that a data job isjust going to fall in your lap, even
if you're trying hard to land one.
You need to develop a plan, a personalroadmap of actual steps that you can
take one by one to land your data job.
This means when you sit down atyour computer to study, you should
know exactly what you're studyingand why you're studying it.
(03:09):
This rarely means that you shouldever sit down and be like, Hmm,
what am I going to do today?
No, you should know beforehand exactlywhat steps you're going to take.
I'm going to be posting on LinkedIn.
I'm going to leave five comments.
I'm going to work on my Tableau project,and then I'm going to call it a night.
This will help you get to your goalfaster, but it'll also keep your sanity.
When I'm tackling big projects, likecompletely pivoting my career, I
need to do it step by step, milestoneby milestone, and follow actionable
(03:33):
steps to get to the end goal.
And this actually leads me to numbertwo, which is to actually focus on what
lands you a job, not what feels good.
If I were to create a scatterplotof time it took to land the data job
against how skilled someone is atdata skills, say SQL, It would not
be a linear one to one correlation.
You'd like to think the people who arebetter at SQL would land data jobs more
(03:54):
quickly, but it's just not the case.
There's too many factors in play.
In fact, none of your data skillsreally correlate with how fast
you're going to land a job.
So why are you spending so muchtime learning new data skills
when that's actually not whatcorrelates to landing a job?
Later in this episode, I'll talkabout some of the things that I think
matter more than your data skillswhen you're landing a data job.
But it's really important that you'refocusing on what lands data jobs.
(04:17):
For example, if I stayed SQL 24 hours aday for the next 365 days, I'd be really
good at SQL, but I wouldn't magicallyland a data job because I'm good at SQL.
It would require me applying to data jobs.
There's no magical level that you'll hitin SQL, Python, or any other data tool.
That's going to magicallyget you a data job.
And at this point, honestly, ifyou haven't landed a technical
(04:38):
data interview and failed it, it'snot your skills holding you back.
It's something like your resumeor your LinkedIn profile.
Unless you're routinely failing technicalinterviews, you don't need to be working
on your technical skills all that much.
Once you have a good foundation.
So why is everyone stillworking on their data skills?
The phrase that best describes itis one that I don't really enjoy,
but I can't think of anything else.
(04:59):
I'm actually going to look inchat GPT right now to see if I
can come up with a better phrase.
Update.
I checked chat GPT.
I can't find a better phraseand it's mental masturbation.
It's the idea that what you're actuallydoing is making you feel good, but
it's really getting you nowhere.
Learning data skills, it makesyou feel more productive than
sending out 50 cold messages torecruiters and getting no responses.
(05:19):
That makes you feel rejection.
Learning data skills is fun.
It's your learning.
It makes you feel productive.
But you have to remember thatlearning data skills and landing a
data job are two different things.
They are related, but they'renot directly correlated.
So you have to lower your scopehere and actually be laser
focused on what you need to do.
What is the actual things, the stepsyou need to take to land a data job?
(05:40):
One easy thing that you can start doingto actually help you make some traction
on your data journey is number three.
And that is to quit being silentand actually share your work.
If you're not talking about whatyou're doing, you honestly don't exist.
Now, this can come in a variety of forms.
I challenge you to start postingon LinkedIn about what you're
learning, your daily data journey.
The reason I challenge you to do thatis because it literally changed my life.
(06:03):
And honestly, if I had never started doingthat, I wouldn't be making this video.
You would not be hearing my words.
I would just be some data scientistfrom the middle of nowhere in Utah.
But because I started talkingabout what I was doing, you
guys are hearing my voice today.
So start posting on LinkedIn.
Tell me what you learned today.
In fact, I challenge you right nowto pause this video and go post on
LinkedIn and share this video to talkabout one of the things you're going
(06:25):
to try to do is to talk more andexplain and document your process.
And I know some of you guys arethinking, uh, Avery, you are so cringe
and everyone's so cringe who posts onLinkedIn and maybe it is a little cringe.
Fine.
But are you willing to be a little cringegoing back up to number one and your why?
Your why strong enough to overcome that?
Personally, mine is, andI hope yours is as well.
(06:46):
If you can't post on LinkedIn forwhatever reason, or it's too scary to
get started, then just start talkingabout what you're doing in your resume.
Make sure your resume accuratelyis showing your career
pivot, build a portfolio.
Talk about what you'redoing on your portfolio.
It doesn't even have to befor, for the public eyes.
Other than when you're applying forjobs to hiring managers and recruiters,
instead of getting stuck in tutorial,how doing the same exercises that
(07:09):
the rest of the people watching thisYouTube video are doing, build a
project, talk about your project,do a writeup of your project, make a
video talking about what you've done.
Put it on a portfolio.
Think about this.
A recruiter or a hiring manager basicallylooks at your application for like
anywhere between three to seven seconds.
How are you going to stand outin those three to seven seconds?
How are they supposed to get an accuratedescription of who you are in that time?
(07:32):
The answer is they're not really goingto, but if you can provide them with
like some evidence, some stuff thatyou've actually done, like a project.
You're going to have a lot higherchance of earning their next 10
seconds, and then the next 60seconds, and then the next 60 minutes.
In today's economy, it's justnot enough to apply for jobs.
You have to actually be talkingabout what you're doing.
This leads me to number four,which is going to be controversial.
(07:54):
But you need to be living by theold fashioned maxim, it's not
what you know, it's who you know.
And that's just the truth.
70 percent of accepted job offerscome from being recruited or referred.
This basically means youneed to be networking.
Remember earlier how I toldyou skills aren't directly
correlated to getting hired?
Well, who you know and your networkis directly correlated at 70%.
Honestly, if that's the case andyou actually believe that like two
(08:16):
thirds of accepted job offers comefrom being recruited or referred,
Why aren't you spending two thirdsof your time working on your network?
The answer is, it doesn't feel good.
Networking sucks.
Especially at the beginning whenyou're just growing your network.
It feels pointless.
It feels awkward.
You don't know who to talk to.
You don't know what to say.
Look, I get it.
I was the same way.
I used to be you watching these videos.
(08:37):
And then I did number three and Istarted posting on LinkedIn and all
of a sudden my network was growing.
And one day I finally got thecourage to actually reach out to
Kate Strachney, who was a reallybig LinkedIn influencer at the time.
And I did a collaboration with her.
That was after, honestly, I sentdozens, if not hundreds of cold
messages that either kind of gotignored or didn't really lead anywhere.
They were all just dead ends.
(08:57):
And after I went to a bunchof in person data events and
I Didn't really meet anyone.
I honestly can name one personI met from those events.
Networking honestly feels pointlessuntil all of a sudden it doesn't.
And for me, one of the biggestchanges in my life is when I
actually reached out to Ken G.
Ken is a data scientist, YouTuber, whohas always had way more followers than me.
And one day I reached out and I actuallyinvited him to a platform that was
(09:21):
invite only at the time called Clubhouse.
It was basically like anaudio group call together.
It was kind of a weird product, but Ioffered him my only invite that I had.
And he really appreciated it.
And so we ended up doing a video togetherand he actually ended up introducing
me to people like Alex, the analyst,Josh Farmer, and a bunch of other data
content creators who I've now had thechance to interview on my podcast.
(09:43):
Getting connected to Ken, it.
Was lucky, I totally admit,I had a lot of luck in play.
He had to read my message, he hadto be interested, and Clubhouse
at the time, he had to be a goodperson and kind and want to help me.
But you can't just say it's luck, becauseI sent hundreds of other messages that
never got opened or never got replied to.
Networking, especially for introvertslike you and me, will always suck.
(10:05):
It's just if your why is bigenough, you embrace the suck.
This is what I meant earlier when I talkedabout, you know, learning SQL, learning
more SQL is fun, but it's not reallygetting you closer to your day to job.
When sending cold messages to recruiterswould get you closer to your job, but it
doesn't really feel like it until it does.
In fact, I had a stay at home momwho recently landed a data job.
(10:25):
She had been out of the workplacefor 20 years and her previous
roles were a teacher, so noteven closely related to data.
She landed a data job withonly one application, one
interview, and she got the offer.
She got lucky.
And if you just hear that, you wouldjust be like, oh, she got lucky.
And she did get lucky.
But what you aren't seeing behind thescenes is the hard work and dedication
she was putting in towards networking.
She found someone for this rolethat she could cold message.
(10:47):
Cold message them, no response.
Found another person,cold message, no response.
Found another person.
Cold message, no response.
I think most of uswould've given up, right?
Cold message someone else.
Response.
Said, sorry, can't help you.
I think we would've all given up there.
But not this person.
She sent another cold message toanother person that she found.
And this person said, oh, youhave an interesting resume.
(11:07):
Let me see what I can do.
Turns out that role wasn'teven supposed to be posted.
It was only an internal hire andthe recruiter had messed up and
actually opened it to the whole world.
So hundreds of you had applied for thatjob and you never stood any chance of
actually landing it because they had nointention of actually hiring externally.
But because my student had cold messagedthis person, their resume was in front
of the hiring manager's eyes already.
(11:28):
And the hiring manager said, well,this is a pretty interesting resume.
Let's take a look and let'sbring her in for an interview.
She got the role.
Networking will help you landjobs that aren't even open.
It'll open doors that are locked close.
And whether you like it or not,whether you're introverted or
not, that's the case for everyone.
The last thing that you can do, numberfive, to actually get ahead of data
analysts this year is mind the gap.
(11:50):
And what I mean by that iswe all have limited time.
We each have 24 hours in the day,no matter if you're Elon Musk or
the poorest person on planet Earth.
That's something that weall have is just 24 hours.
And some of you guys are working two jobs.
You're working like 80 hours a week.
You have kids.
I totally get that.
You're like, Oh my gosh, I don't knowwhen I'm going to do this, Avery.
Like how the heck amI going to learn this?
And my short answer is, I don'tknow how you do it either,
(12:12):
but here's my suggestion.
Mind the gap.
And what I mean by that is when yougo to the tube in London, they have
mind the gap painted on the ground.
And they're basicallysaying, pay attention.
To the space the empty space betweenthe edge of the platform and as you
step on the actual subway Obviouslygood advice if you're ever on the subway
But what does that have to do with youand your data career no matter how busy
(12:32):
you are and how many things you have?
On your schedule, you're always gonnahave little teeny tiny gaps in your
day I have them all the time andhonestly, I feel a lot of it with
Instagram scrolling looking on TwitterAnd watching YouTube videos kind of
like this and watching things like Mr.
Beast videos on YouTube.
My suggestion to you to get aheadof 99 percent of data analysts is to
fill that gap with videos like this.
(12:54):
Cut out as much fluff as you canin the gap and actually try to fill
it with things that are valuable,that are worth listening to.
I think that's one of the biggest thingsthat you can do in your data career is
actually be listening to stuff like this.
Because obviously if you're watching, it'sinvolving your eyes, a lot of time you're
going to be at a TV, at your phone, orat your desktop or something like that.
With audio, you can bedoing two things at once.
(13:15):
So for example, if you have any commuteright now, fill that commute with
listening to data YouTube videos.
If you found this video on YouTube,continue listening on YouTube.
If you found this via the podcast,keep listening on podcasts.
But obviously you don'tonly have to listen to me.
There's other great podcasts.
I really enjoy the Super Data ScienceShow, Plumbers of Data Science,
How to Land an Analytics Job, DataEngineering Podcast, DataViz Today.
(13:37):
In terms of YouTube channels,obviously Alex the Analyst is great.
I'm a big fan of Elijah Butler.
I just interviewed Tu Vu recentlyon my podcast and she's great.
I really like Mo Chen's videos and there'sobviously a lot of other good shows.
But fill your day and fillspecifically those gaps, specifically
with audio, with this good datacontent that's honestly free.
I promise as you do that, you willcontinue to learn and grow without
(14:00):
even having to spend more time.
So to recap, become laser focusedon what you actually want, a
title and the why behind it.
Then focus on what actually matters.
Cut out all the fluff and focuson the actual steps that's
going to land you a data role.
Then, quit being quietand actually speak up.
We want to hear what you're doingand you will benefit from it.
Remember, it's not what youknow, it's who you know.
(14:21):
And five, fill the gapwith content like this.
If you want to keep filling your gapwith my content, I highly suggest
this episode next and I'll haveit in the show notes down below.
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