February 25, 2025 • 17 mins
I tested DeepSeek-- an emerging AI platform that makes ChatGPT look ancient! I asked it to outline a comprehensive roadmap for becoming a data analyst. What it said scared me (Spoiler: it basically copied my SPN Method)!
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⌚ TIMESTAMPS
00:00 - Introduction
01:05 - Skills
01:27 - Do you need a degree? DeepSeek answers
01:59 - Projects and portfolio
02:43 - Networking and job search strategies
04:55 - Interview preparation
10:15 - FindADataJob.com and PremiumDataJobs.com
11:30 - InterviewSimulator.io
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Avery (00:00):
If you haven't heard of deep seek yet, just wait.
It is a new AI platform that supposedlyis maybe even better than open AI
and chat GPT reasons being number oneis currently free and open source,
meaning it is a lot more easilyaccessible to the entire world.
And number two, it has somepretty cool thinking properties
(00:20):
where it actually shows you it's.
Thought process, and you can see howthe computer algorithm and the AI is
actually thinking through the problem.
So I thought, why not askDeepSeek on how to become a data
analyst and see what it tells me.
So let's go.
So I'm on DeepSeek's website andI'm going to turn on the DeepThink
R1 model, which basically is theirbetter reasoning model right here.
(00:41):
And I'm going to ask, Iwant to be a data analyst.
What should I do?
And hit enter.
One thing about DeepSeek is itis a lot slower because it is
actually going through and thinkingthrough your process step by step.
So although OpenAI and ChatGPT may befaster, DeepSeek is actually taking
its time to think through the problem.
And you see those thoughts right here.
So it says, okay, the userwants to become a data analyst.
(01:03):
Let me start breakingdown what that entails.
First, I need to outline theessential skills they need.
Data analysis requires technical skillslike SQL, Excel, maybe Python or R.
I already love that the maybe is here.
If you guys have watched any ofmy videos or listened to any of my
podcast episodes, you know that Iam not a fan of learning Python.
First, and I think SQL and Excelare a better place to start.
Visualization tools like Tableauor Power BI are important too.
(01:24):
Yes, they should also understandstatistics and data cleaning.
Next education.
Do they need a degree, abackground in math stats or CS
helps, but it's not mandatory.
So far, I really agree withwhat DeepSake is saying.
Obviously it helps to have those.
STEM background, some sortof math or stats or computer
science, but it's not mandatory.
Maybe mention online courses andcertificates as alternatives.
Platforms like Coursera,edX, Datacamp come to mind.
(01:46):
Okay.
Certifications from Google orMicrosoft could also add credibility.
I'm going to be honest.
I don't think either of thoseadd a ton of credibility.
The Google and the Microsoft certificatecertificates still don't mean anything
in the data analytics world, butthey don't hurt your credibility.
So I guess that's fine.
Projects are crucial for experience.
Oh my gosh.
I agree with this.
That's part of the SPN method, right?
In order to land the data job,you have to have the skills.
You have to build projects andyou have to have a network.
(02:07):
So projects are crucial for experiences.
They should work on real worlddata sets to build a portfolio.
I totally agree with that.
Where can they find data sets?
Kaggle, government portals,maybe APIs, suggest projects like
sales analysis or COVID trends.
Totally.
We do sales and marketing analysisin the accelerator program.
Uh, and we do some healthcare analytics.
We don't do COVID because COVID hasbeen done a bajillion times, but
(02:28):
we do actually diabetes buildinga GitHub portfolio and personal
website would showcase their work.
Uh, yes, I would reverse thosewhere I would do a personal website
first versus a GitHub portfolio.
If you're just trying to land thedata analyst job, if you're going for
a data scientist or data engineer,that's a little bit different, but
I liked that it didn't just say Gab.
I liked that it said personal website,networking and job search strategies.
Yes.
That's the end part of the SPN method.
(02:49):
I like DeepSeek so far.
LinkedIn is important, it sure is, aswell as attending meetups or conferences,
tailoring resumes to highlight relevantskills and projects, applying to
entry level positions like junior dataanalyst or business analyst, maybe
internships if they're just starting out.
Continuous learning is key in this field.
That is so true.
Encourage joining communities,following blogs, podcasts.
Hey, shout out to, uh, podcasts.
(03:10):
If you want to follow a podcast,check out the Data Career Podcast.
Staying updated with trendslike AI and machine learning.
Also, soft skills likecommunication and problem solving
are often overlooked, but vital.
So far, so good.
I really agree withwhat DeepSeek is saying.
Wait, did I miss anything?
Okay.
It's so weird to have.
A, an AI model say something like that.
Oh, maybe the tools in more detail.
Excel for basic SQL for databases.
(03:31):
Python slash R for advanced analysis.
Visualization tools like Tableau,Power BI, maybe even Looker.
Cloud platforms are becoming morerelevant too, like AWS and Google Cloud.
Also the importance of understandingthe industry they want to work in.
Man, 100 percent I agree with that.
Domain knowledge can set them apart.
Yes, it can.
Like if you're trying to break into data,whatever previous experience, whatever
previous job, whatever previous educationyou've had, that's, what's like going
(03:54):
to be your in, into the world becausedata analysis is in every industry.
So it's like, that iswhere you need to focus on.
Uh, so I liked that.
I got that.
Domain knowledge can set them apart.
Maybe healthcare, finance,e commerce, et cetera.
Tailoring projects to specificindustries might help.
Once again, a hundred percent correct.
This is why in the accelerator.
We do nine different projectsin nine different industries.
So no matter what industry you'reinterested in, we have a project that you
can pitch to a hiring manager recruiter.
(04:16):
Hey, look, I have, I've done somethingvery similar to what your job requirement
is asking you to do, asking me to do.
Here's my evidence that I can do this.
Should I mention freelancing orvolunteer work as a way to get experience
that could be useful for building aresume, maybe suggest platforms like
Upwork or volunteering for nonprofits.
I love the volunteering for nonprofits.
I think that's great.
I think most people do.
Who try to go the freelancing route.
(04:36):
It's pretty difficult becausefreelancing is basically, you have
to be good at whatever you're doing.
So in this case, data analysis, butyou have to be good at data as well.
I mean, you're just starting outto be perfectly Frank, unless you
have freelancing experience someother way, you're bad at both.
So it's not my favorite place.
To start if I was you, but it didn't, itjust said, maybe we can consider them.
What about interview preparation?
They might need to practice SQL queries,case studies, and presenting their
(04:58):
projects, behavioral interviews as well.
Resources like sleek code forSQL practice or mock interviews.
Let me structure this step by stepstart with learning technical skills,
then education and certificates,building projects, networking, job
applications, and continuous learning.
Make sure each section is clearand actionable avoid jargon.
So it's accessible.
Maybe include resourcesand examples for each step.
Becoming data analyst involves buildingtechnical skills, getting practical
(05:19):
experience and showcasing your abilities.
Here's a step by step guideto help you get started.
All right.
Just like the thinking was very good.
And obviously that's not somethingyou get with open AI and chat GPT.
So I loved hearing how it was thinking.
And that way I can kind ofinterpret the context of the
results with what it was thinking.
Uh, let's go ahead and take a lookat what it officially suggests.
Build foundational skills, technicalskills, Excel, master pivot tables,
(05:42):
VLOOKUP and data visualization.
That is super simple.
I like that a lot.
SQL.
Learn to query databases.
Join aggregations.
Resources.
Mode.
Analytics.
SQL tutorial.
Kaggle.
Learn SQL.
Both two great resources for learning SQL.
Python slash R.
Focus on libraries like pandas,NumPy, and visualization tools.
Resources.
Data camp.
Free code camp.
NumPy.
I mean, it's a, it's fine to learn pandasis the big one in visualization tools.
(06:03):
NumPy actually pandas is built on NumPy.
So by knowing pandas, you kind of cando a lot of the stuff that NumPy can.
There are some things that you need touse NumPy for alone, but there's not
that many data visualization, learntools like Tableau, Power BI or Looker
Tableau, public Tableau training.
Great.
Statistics, grasp basics like hypothesis,testing distributions, and regression
Khan Academy, StatQuest YouTube channel.
Shout out StatQuest and Josh Starmer.
(06:25):
I've done a video with Josh in the past.
Uh, we'll have that inthe show notes down below.
I also love that it did statisticslast out of all of these, because
often people are like, don't you haveto know statistics to become data
analyst and you do, but it's maybeless important than you realize.
For instance, you can do a VLOOKUPreally without doing any sort of.
Tons of statistics.
There are aggregations there like sumsand averages, mins and maxes, but the
(06:46):
chances are you've probably learned thatin high school or college at some point.
So something more complicated likehypothesis testing or regression,
those are important too, but not asimportant as knowing how to do a VLOOKUP.
I love that it's includingsoft skills here too.
Soft skills, storytelling,present insights clearly
to non technical audiences.
Problem solving breakdown, complexquestions into data driven solutions.
A lot of the times, and honestly, like ifyou go look at the platforms, it suggests
(07:10):
like data camp and free code camp,they really miss on the storytelling.
And it's really important becausewe had cool math, like on the
podcast recently, and she's like,people don't take you seriously.
If you're unable to communicateyour story and your work.
Effectively.
So it's often overlooked.
One of the things we do inside theaccelerator is one, we post our stories
on LinkedIn in our about section.
(07:30):
And we try to post on LinkedInoften, but also we post our projects
on there with a written writeup.
Um, and that written form ofcommunication, we actually will teach you
how to do video communication as well.
Like if you like recorded yourself witha Google slide presentation or with
your dashboard, but those types of.
Communication and storytellingopportunities are really what can set
you apart as an aspiring data analyst.
Next section, formaleducation and certificates.
(07:51):
So degrees optional.
Love that it says optional.
That is very friendly.
I appreciate that.
I'd agree in math, statistics, CS, oreconomics is helpful, but not mandatory.
I agree.
Online certificates, Google dataanalytics certificate, and the
Microsoft data analyst associatepower BI plus Excel and the IBM data
analyst professional certificate.
Obviously those are all good and fine.
Gain practical experience.
I love that it's saying this.
This is awesome.
Projects, analyze data from KaggleUCI learning machine learning
(08:14):
repository or government portals, data.
gov.
I think that is a hundred percent correct.
Example projects, sales trend analysis,COVID 19 impact dashboard, customer
segmentation, obviously not a wholelot of like, this is just an intro.
Right.
So it's not really giving us any moreinformation on these, but that's great.
Portfolio share code on GitHub andcreate visualizations in Tableau public.
Build a personal website using GitHubpages or WordPress to showcase projects.
(08:36):
Love that it got GitHub pages.
That is something thata lot of people miss.
Now I need to make a differentvideo on this, but GitHub pages
is very separate from GitHub.
It is like it's from GitHub, butit's like a separate product.
It's free, but it's basically like theability to build personal websites.
And I love that it got it.
I personally recommend card now.
We'll have a link in the show notes.
To to check out card thing isthe best and easiest place to
(08:57):
start building your portfolio.
Freelance slash volunteer offerservices on Upwork or nonprofits.
I like helping nonprofits morebecause I think they could offer more
support and like a more formal role.
Once again, I think freelancingon Upwork, especially if you've
never freelanced before, it's notgoing to lead very far because.
Freelancing requires a tonof business experience.
You have to know how to market yourself.
You have to know how toask a lot of questions.
(09:20):
There's no one checking your work.
So I would lean on the volunteerside versus the freelance, but
I don't mind them mentioning it.
Okay.
Number four, network and apply for jobs.
LinkedIn optimize your profilewith keywords like data analysts
and connect with professionals.
You guys, I can't tell you howimportant this first line is.
And it really, if you just read it,you're like, okay, that makes sense.
What does that actually mean?
You guys, this is one thing wetalk about in the accelerator.
The more you put the term data analyston your LinkedIn profile and your
(09:43):
resume, the better you'll be off.
ATS is the LinkedInrecruiting algorithm is dumb.
One of the ways it actually like checksto see how relevant you are to, for
instance, if you're applying to a dataanalyst role is how many times do they
have the word data analyst on their.
LinkedIn page.
And that phrase can be anywhere thatcould be in your headline that can
be in your about section that canbe in your experience section that
can be in your education section.
(10:04):
For instance, if you just put aspiringdata analyst in your experience section,
that actually almost works as good to acomputer as putting the term data analyst.
So that is really key job platforms,entry level roles, junior data analyst,
business analyst, reporting analyst.
Those are all goods search onLinkedIn indeed, or specialized
sites like well found.
Yeah.
Well found angels lists.
(10:25):
I'm a fan of, but not reallyfor entry level roles.
They're more senior roles there.
Instead, I would trysomething like findadatajob.
com or premiumdatajobs.
com.
Those are two job boards that I run wherewe try to be more entry level friendly.
Meetups, attend events, data sciencemeetup, Pi data or virtual webinars.
I think that's great.
That's a form of networkingand obviously a great option.
(10:47):
Okay.
Number five, ACE interviews, technicalprep, practice SQL on leak code or hacker
rank review, statistical concepts andcase studies, behavioral questions.
Use the star method to answerquestions about teamwork and problem
solving and portfolio walkthrough.
Be ready to explain your projects,goals, process, and impact.
Once again.
This feels really good because mostpeople are all about the technical prep
(11:08):
and the technical prep is important,but I would say, honestly, at least half
of my students who land jobs throughthe accelerator program, never really
even have a formal technical interview.
The other 50 percent definitely do.
And it's good to be prepared usingthings like leak code or hacker.
I prefer things like strata scratch,data lemur or analyst builder.
Instead of these, they're justmore data oriented instead of like.
(11:28):
Computer science and stuff like that, Ithink, but I just want to point off that
most people ignore behavioral questions.
And that's one of the things I try notto ignore with interview simulator.
If you guys go to interview simulator.
io, this is my interviewplatform where you can practice
your behavioral questions.
And then I love that it has the portfoliowalkthrough as well and being able to
talk about your projects because really,if you can get an interview and you
(11:49):
can say, Hey, I have this portfolio.
I've done this project that's similarto what I would be doing on the job.
I think that is an opportunity for you to.
Try to take the interview kind ofby the reins and flip it on them.
And they ask you questions aboutyour project versus just like
asking random statistical concepts.
So that's going to make youfeel more comfortable and
make you look better as well.
Number six, keep learning, stayupdated, follow blogs like towards data
(12:10):
science and podcasts like data skeptic.
Those are both great.
I would add data career podcast to thepodcast, but if you're listening to this,
you're probably already following ourpodcast, advanced skills, explore machine
learning, scikit learn, cloud tools, AWS.
Google Big Query or A B testing.
I think those are, I mean, that's fine.
You're always going to be learning in thisworld, but it didn't really talk about job
applications and applying like you don'twant to just like go to advanced skills
(12:34):
without job hunting a ton because you canget paid to learn machine learning and
cloud tools and A B testing on the job.
Most entry level roles, even maybemiddle roles don't even require that.
Uh, example, learning path, Excel,SQL, Tableau month, one to two months,
three to four Python and statistics andmonth five to six build three or four
portfolio projects and apply for jobs.
(12:54):
Now, if you've listened to any of myepisodes previously, you know, that I
think that most people, um, if they'rewilling to put in, you know, 10 to
20 hours a week can cut this in half.
And there's like certain things that wecan do where it's like, we're not going to
spend an entire month learning statistics,an entire month learning Python.
You just don't need to, when you'relanding your first day at a job.
(13:14):
And also like why wait till monthfive and six to build your portfolio
projects and apply for jobs.
In the accelerator, you'll have a projectbuilt within your first 10 days, your
first project built 10 days guaranteed.
Like if you just put it in the hours,like done, we'll have your first project.
Build and then we want to startapplying for jobs, you know, well,
before we hit the six month mark,we're probably talking to the two
month mark, if I'm being honest,because applying for jobs isn't art and
(13:37):
you'll get better at it as you go on.
But overall, I don't hate thisplan at all by combining skills,
projects, and networking.
Holy crap.
Did they steal that from me?
Skills, projects, and networking.
That's the SPN method.
I came up with the SPN method.
I'm the only one who's ever put thosethings right next to each other.
Skills, projects, and networking.
Call it the SPN method.
I built it myself.
I have a notebook somewhere overhere where like, I just, I wrote
(13:59):
down my whole like framework and liketried to figure out what to call it.
And we ended up landing on SPN.
Wow.
You'll position yourselfstrongly for a data analyst role.
Stay smart, stay consistentand iterate based on feedback.
Overall, you guys, like, I feellike this was a pretty good roadmap.
Right here.
Like I'm pretty impressed by this and, uh,it's not the worst thing on planet earth.
(14:19):
Like it, it did a better jobalmost in the instructions.
I think of like the Python where it waslike maybe Python and R somewhere up here.
Right.
And, but I think it did great onmentioning the behavioral questions.
I think it did really good onthe networking and the, and
the projects and the portfolio.
I thought I did great talkingabout GitHub pages overall.
I think if you followedthis plan, you would be.
(14:40):
Pretty well off.
I mean, this plan is basically whatI outlined in my previous episodes.
It's basically following the SPN method.
I mean, literally it says bycombining skills, projects, and
networking, you'll position yourselfstrongly for a data analyst role.
And I agree like that, the SPNmethod will set you up exactly.
This way.
So, uh, I really like this from deep seek.
I'm going to play around with this more.
If you guys want to follow theSPN method, please consider
(15:03):
joining the accelerator program.
This is basically a coaching led andgroup cohort learning style where
you're basically going to do allof these things, but we're going to
give it to you exactly step by step.
You're not going to have to go figureout like, you know, how do I learn
data visualization and Tableau public?
Or like what courses should I take?
We'll give you the exact roadmap.
We'll teach you the exact projects.
(15:23):
We'll give you the exact data tobuild your projects, to learn the
skills and to grow your network.
We'll show you exactlyhow to actually optimize.
Like what does it actually meanto optimize your profile with
keywords like data analyst?
So that's of interest to you.
We'll have a link in the show notesdown below and let me know what
you guys want me to do next withdeep seek down in the comments.
Should I try to analyze data?
(15:44):
Should we compare it tosomething like chat GPT?
Let me know in the comments down below.
If you haven't heard of deep seek yet, just wait.
It is a new AI platform that supposedlyis maybe even better than open AI
and chat GPT reasons being number oneis currently free and open source,
meaning it is a lot more easilyaccessible to the entire world.
And number two, it has somepretty cool thinking properties
(00:20):
where it actually shows you it's.
Thought process, and you can see howthe computer algorithm and the AI is
actually thinking through the problem.
So I thought, why not askDeepSeek on how to become a data
analyst and see what it tells me.
So let's go.
So I'm on DeepSeek's website andI'm going to turn on the DeepThink
R1 model, which basically is theirbetter reasoning model right here.
(00:41):
And I'm going to ask, Iwant to be a data analyst.
What should I do?
And hit enter.
One thing about DeepSeek is itis a lot slower because it is
actually going through and thinkingthrough your process step by step.
So although OpenAI and ChatGPT may befaster, DeepSeek is actually taking
its time to think through the problem.
And you see those thoughts right here.
So it says, okay, the userwants to become a data analyst.
(01:03):
Let me start breakingdown what that entails.
First, I need to outline theessential skills they need.
Data analysis requires technical skillslike SQL, Excel, maybe Python or R.
I already love that the maybe is here.
If you guys have watched any ofmy videos or listened to any of my
podcast episodes, you know that Iam not a fan of learning Python.
First, and I think SQL and Excelare a better place to start.
Visualization tools like Tableauor Power BI are important too.
(01:24):
Yes, they should also understandstatistics and data cleaning.
Next education.
Do they need a degree, abackground in math stats or CS
helps, but it's not mandatory.
So far, I really agree withwhat DeepSake is saying.
Obviously it helps to have those.
STEM background, some sortof math or stats or computer
science, but it's not mandatory.
Maybe mention online courses andcertificates as alternatives.
Platforms like Coursera,edX, Datacamp come to mind.
(01:46):
Okay.
Certifications from Google orMicrosoft could also add credibility.
I'm going to be honest.
I don't think either of thoseadd a ton of credibility.
The Google and the Microsoft certificatecertificates still don't mean anything
in the data analytics world, butthey don't hurt your credibility.
So I guess that's fine.
Projects are crucial for experience.
Oh my gosh.
I agree with this.
That's part of the SPN method, right?
In order to land the data job,you have to have the skills.
You have to build projects andyou have to have a network.
(02:07):
So projects are crucial for experiences.
They should work on real worlddata sets to build a portfolio.
I totally agree with that.
Where can they find data sets?
Kaggle, government portals,maybe APIs, suggest projects like
sales analysis or COVID trends.
Totally.
We do sales and marketing analysisin the accelerator program.
Uh, and we do some healthcare analytics.
We don't do COVID because COVID hasbeen done a bajillion times, but
(02:28):
we do actually diabetes buildinga GitHub portfolio and personal
website would showcase their work.
Uh, yes, I would reverse thosewhere I would do a personal website
first versus a GitHub portfolio.
If you're just trying to land thedata analyst job, if you're going for
a data scientist or data engineer,that's a little bit different, but
I liked that it didn't just say Gab.
I liked that it said personal website,networking and job search strategies.
Yes.
That's the end part of the SPN method.
(02:49):
I like DeepSeek so far.
LinkedIn is important, it sure is, aswell as attending meetups or conferences,
tailoring resumes to highlight relevantskills and projects, applying to
entry level positions like junior dataanalyst or business analyst, maybe
internships if they're just starting out.
Continuous learning is key in this field.
That is so true.
Encourage joining communities,following blogs, podcasts.
Hey, shout out to, uh, podcasts.
(03:10):
If you want to follow a podcast,check out the Data Career Podcast.
Staying updated with trendslike AI and machine learning.
Also, soft skills likecommunication and problem solving
are often overlooked, but vital.
So far, so good.
I really agree withwhat DeepSeek is saying.
Wait, did I miss anything?
Okay.
It's so weird to have.
A, an AI model say something like that.
Oh, maybe the tools in more detail.
Excel for basic SQL for databases.
(03:31):
Python slash R for advanced analysis.
Visualization tools like Tableau,Power BI, maybe even Looker.
Cloud platforms are becoming morerelevant too, like AWS and Google Cloud.
Also the importance of understandingthe industry they want to work in.
Man, 100 percent I agree with that.
Domain knowledge can set them apart.
Yes, it can.
Like if you're trying to break into data,whatever previous experience, whatever
previous job, whatever previous educationyou've had, that's, what's like going
(03:54):
to be your in, into the world becausedata analysis is in every industry.
So it's like, that iswhere you need to focus on.
Uh, so I liked that.
I got that.
Domain knowledge can set them apart.
Maybe healthcare, finance,e commerce, et cetera.
Tailoring projects to specificindustries might help.
Once again, a hundred percent correct.
This is why in the accelerator.
We do nine different projectsin nine different industries.
So no matter what industry you'reinterested in, we have a project that you
can pitch to a hiring manager recruiter.
(04:16):
Hey, look, I have, I've done somethingvery similar to what your job requirement
is asking you to do, asking me to do.
Here's my evidence that I can do this.
Should I mention freelancing orvolunteer work as a way to get experience
that could be useful for building aresume, maybe suggest platforms like
Upwork or volunteering for nonprofits.
I love the volunteering for nonprofits.
I think that's great.
I think most people do.
Who try to go the freelancing route.
(04:36):
It's pretty difficult becausefreelancing is basically, you have
to be good at whatever you're doing.
So in this case, data analysis, butyou have to be good at data as well.
I mean, you're just starting outto be perfectly Frank, unless you
have freelancing experience someother way, you're bad at both.
So it's not my favorite place.
To start if I was you, but it didn't, itjust said, maybe we can consider them.
What about interview preparation?
They might need to practice SQL queries,case studies, and presenting their
(04:58):
projects, behavioral interviews as well.
Resources like sleek code forSQL practice or mock interviews.
Let me structure this step by stepstart with learning technical skills,
then education and certificates,building projects, networking, job
applications, and continuous learning.
Make sure each section is clearand actionable avoid jargon.
So it's accessible.
Maybe include resourcesand examples for each step.
Becoming data analyst involves buildingtechnical skills, getting practical
(05:19):
experience and showcasing your abilities.
Here's a step by step guideto help you get started.
All right.
Just like the thinking was very good.
And obviously that's not somethingyou get with open AI and chat GPT.
So I loved hearing how it was thinking.
And that way I can kind ofinterpret the context of the
results with what it was thinking.
Uh, let's go ahead and take a lookat what it officially suggests.
Build foundational skills, technicalskills, Excel, master pivot tables,
(05:42):
VLOOKUP and data visualization.
That is super simple.
I like that a lot.
SQL.
Learn to query databases.
Join aggregations.
Resources.
Mode.
Analytics.
SQL tutorial.
Kaggle.
Learn SQL.
Both two great resources for learning SQL.
Python slash R.
Focus on libraries like pandas,NumPy, and visualization tools.
Resources.
Data camp.
Free code camp.
NumPy.
I mean, it's a, it's fine to learn pandasis the big one in visualization tools.
(06:03):
NumPy actually pandas is built on NumPy.
So by knowing pandas, you kind of cando a lot of the stuff that NumPy can.
There are some things that you need touse NumPy for alone, but there's not
that many data visualization, learntools like Tableau, Power BI or Looker
Tableau, public Tableau training.
Great.
Statistics, grasp basics like hypothesis,testing distributions, and regression
Khan Academy, StatQuest YouTube channel.
Shout out StatQuest and Josh Starmer.
(06:25):
I've done a video with Josh in the past.
Uh, we'll have that inthe show notes down below.
I also love that it did statisticslast out of all of these, because
often people are like, don't you haveto know statistics to become data
analyst and you do, but it's maybeless important than you realize.
For instance, you can do a VLOOKUPreally without doing any sort of.
Tons of statistics.
There are aggregations there like sumsand averages, mins and maxes, but the
(06:46):
chances are you've probably learned thatin high school or college at some point.
So something more complicated likehypothesis testing or regression,
those are important too, but not asimportant as knowing how to do a VLOOKUP.
I love that it's includingsoft skills here too.
Soft skills, storytelling,present insights clearly
to non technical audiences.
Problem solving breakdown, complexquestions into data driven solutions.
A lot of the times, and honestly, like ifyou go look at the platforms, it suggests
(07:10):
like data camp and free code camp,they really miss on the storytelling.
And it's really important becausewe had cool math, like on the
podcast recently, and she's like,people don't take you seriously.
If you're unable to communicateyour story and your work.
Effectively.
So it's often overlooked.
One of the things we do inside theaccelerator is one, we post our stories
on LinkedIn in our about section.
(07:30):
And we try to post on LinkedInoften, but also we post our projects
on there with a written writeup.
Um, and that written form ofcommunication, we actually will teach you
how to do video communication as well.
Like if you like recorded yourself witha Google slide presentation or with
your dashboard, but those types of.
Communication and storytellingopportunities are really what can set
you apart as an aspiring data analyst.
Next section, formaleducation and certificates.
(07:51):
So degrees optional.
Love that it says optional.
That is very friendly.
I appreciate that.
I'd agree in math, statistics, CS, oreconomics is helpful, but not mandatory.
I agree.
Online certificates, Google dataanalytics certificate, and the
Microsoft data analyst associatepower BI plus Excel and the IBM data
analyst professional certificate.
Obviously those are all good and fine.
Gain practical experience.
I love that it's saying this.
This is awesome.
Projects, analyze data from KaggleUCI learning machine learning
(08:14):
repository or government portals, data.
gov.
I think that is a hundred percent correct.
Example projects, sales trend analysis,COVID 19 impact dashboard, customer
segmentation, obviously not a wholelot of like, this is just an intro.
Right.
So it's not really giving us any moreinformation on these, but that's great.
Portfolio share code on GitHub andcreate visualizations in Tableau public.
Build a personal website using GitHubpages or WordPress to showcase projects.
(08:36):
Love that it got GitHub pages.
That is something thata lot of people miss.
Now I need to make a differentvideo on this, but GitHub pages
is very separate from GitHub.
It is like it's from GitHub, butit's like a separate product.
It's free, but it's basically like theability to build personal websites.
And I love that it got it.
I personally recommend card now.
We'll have a link in the show notes.
To to check out card thing isthe best and easiest place to
(08:57):
start building your portfolio.
Freelance slash volunteer offerservices on Upwork or nonprofits.
I like helping nonprofits morebecause I think they could offer more
support and like a more formal role.
Once again, I think freelancingon Upwork, especially if you've
never freelanced before, it's notgoing to lead very far because.
Freelancing requires a tonof business experience.
You have to know how to market yourself.
You have to know how toask a lot of questions.
(09:20):
There's no one checking your work.
So I would lean on the volunteerside versus the freelance, but
I don't mind them mentioning it.
Okay.
Number four, network and apply for jobs.
LinkedIn optimize your profilewith keywords like data analysts
and connect with professionals.
You guys, I can't tell you howimportant this first line is.
And it really, if you just read it,you're like, okay, that makes sense.
What does that actually mean?
You guys, this is one thing wetalk about in the accelerator.
The more you put the term data analyston your LinkedIn profile and your
(09:43):
resume, the better you'll be off.
ATS is the LinkedInrecruiting algorithm is dumb.
One of the ways it actually like checksto see how relevant you are to, for
instance, if you're applying to a dataanalyst role is how many times do they
have the word data analyst on their.
LinkedIn page.
And that phrase can be anywhere thatcould be in your headline that can
be in your about section that canbe in your experience section that
can be in your education section.
(10:04):
For instance, if you just put aspiringdata analyst in your experience section,
that actually almost works as good to acomputer as putting the term data analyst.
So that is really key job platforms,entry level roles, junior data analyst,
business analyst, reporting analyst.
Those are all goods search onLinkedIn indeed, or specialized
sites like well found.
Yeah.
Well found angels lists.
(10:25):
I'm a fan of, but not reallyfor entry level roles.
They're more senior roles there.
Instead, I would trysomething like findadatajob.
com or premiumdatajobs.
com.
Those are two job boards that I run wherewe try to be more entry level friendly.
Meetups, attend events, data sciencemeetup, Pi data or virtual webinars.
I think that's great.
That's a form of networkingand obviously a great option.
(10:47):
Okay.
Number five, ACE interviews, technicalprep, practice SQL on leak code or hacker
rank review, statistical concepts andcase studies, behavioral questions.
Use the star method to answerquestions about teamwork and problem
solving and portfolio walkthrough.
Be ready to explain your projects,goals, process, and impact.
Once again.
This feels really good because mostpeople are all about the technical prep
(11:08):
and the technical prep is important,but I would say, honestly, at least half
of my students who land jobs throughthe accelerator program, never really
even have a formal technical interview.
The other 50 percent definitely do.
And it's good to be prepared usingthings like leak code or hacker.
I prefer things like strata scratch,data lemur or analyst builder.
Instead of these, they're justmore data oriented instead of like.
(11:28):
Computer science and stuff like that, Ithink, but I just want to point off that
most people ignore behavioral questions.
And that's one of the things I try notto ignore with interview simulator.
If you guys go to interview simulator.
io, this is my interviewplatform where you can practice
your behavioral questions.
And then I love that it has the portfoliowalkthrough as well and being able to
talk about your projects because really,if you can get an interview and you
(11:49):
can say, Hey, I have this portfolio.
I've done this project that's similarto what I would be doing on the job.
I think that is an opportunity for you to.
Try to take the interview kind ofby the reins and flip it on them.
And they ask you questions aboutyour project versus just like
asking random statistical concepts.
So that's going to make youfeel more comfortable and
make you look better as well.
Number six, keep learning, stayupdated, follow blogs like towards data
(12:10):
science and podcasts like data skeptic.
Those are both great.
I would add data career podcast to thepodcast, but if you're listening to this,
you're probably already following ourpodcast, advanced skills, explore machine
learning, scikit learn, cloud tools, AWS.
Google Big Query or A B testing.
I think those are, I mean, that's fine.
You're always going to be learning in thisworld, but it didn't really talk about job
applications and applying like you don'twant to just like go to advanced skills
(12:34):
without job hunting a ton because you canget paid to learn machine learning and
cloud tools and A B testing on the job.
Most entry level roles, even maybemiddle roles don't even require that.
Uh, example, learning path, Excel,SQL, Tableau month, one to two months,
three to four Python and statistics andmonth five to six build three or four
portfolio projects and apply for jobs.
(12:54):
Now, if you've listened to any of myepisodes previously, you know, that I
think that most people, um, if they'rewilling to put in, you know, 10 to
20 hours a week can cut this in half.
And there's like certain things that wecan do where it's like, we're not going to
spend an entire month learning statistics,an entire month learning Python.
You just don't need to, when you'relanding your first day at a job.
(13:14):
And also like why wait till monthfive and six to build your portfolio
projects and apply for jobs.
In the accelerator, you'll have a projectbuilt within your first 10 days, your
first project built 10 days guaranteed.
Like if you just put it in the hours,like done, we'll have your first project.
Build and then we want to startapplying for jobs, you know, well,
before we hit the six month mark,we're probably talking to the two
month mark, if I'm being honest,because applying for jobs isn't art and
(13:37):
you'll get better at it as you go on.
But overall, I don't hate thisplan at all by combining skills,
projects, and networking.
Holy crap.
Did they steal that from me?
Skills, projects, and networking.
That's the SPN method.
I came up with the SPN method.
I'm the only one who's ever put thosethings right next to each other.
Skills, projects, and networking.
Call it the SPN method.
I built it myself.
I have a notebook somewhere overhere where like, I just, I wrote
(13:59):
down my whole like framework and liketried to figure out what to call it.
And we ended up landing on SPN.
Wow.
You'll position yourselfstrongly for a data analyst role.
Stay smart, stay consistentand iterate based on feedback.
Overall, you guys, like, I feellike this was a pretty good roadmap.
Right here.
Like I'm pretty impressed by this and, uh,it's not the worst thing on planet earth.
(14:19):
Like it, it did a better jobalmost in the instructions.
I think of like the Python where it waslike maybe Python and R somewhere up here.
Right.
And, but I think it did great onmentioning the behavioral questions.
I think it did really good onthe networking and the, and
the projects and the portfolio.
I thought I did great talkingabout GitHub pages overall.
I think if you followedthis plan, you would be.
(14:40):
Pretty well off.
I mean, this plan is basically whatI outlined in my previous episodes.
It's basically following the SPN method.
I mean, literally it says bycombining skills, projects, and
networking, you'll position yourselfstrongly for a data analyst role.
And I agree like that, the SPNmethod will set you up exactly.
This way.
So, uh, I really like this from deep seek.
I'm going to play around with this more.
If you guys want to follow theSPN method, please consider
(15:03):
joining the accelerator program.
This is basically a coaching led andgroup cohort learning style where
you're basically going to do allof these things, but we're going to
give it to you exactly step by step.
You're not going to have to go figureout like, you know, how do I learn
data visualization and Tableau public?
Or like what courses should I take?
We'll give you the exact roadmap.
We'll teach you the exact projects.
(15:23):
We'll give you the exact data tobuild your projects, to learn the
skills and to grow your network.
We'll show you exactlyhow to actually optimize.
Like what does it actually meanto optimize your profile with
keywords like data analyst?
So that's of interest to you.
We'll have a link in the show notesdown below and let me know what
you guys want me to do next withdeep seek down in the comments.
Should I try to analyze data?
(15:44):
Should we compare it tosomething like chat GPT?
Let me know in the comments down below.
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