149: I Asked DeepSeek How to Become a Data Analyst (It scared me)

Avery: 00:00:00

If you haven't heard of deep seek yet, just wait.

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It is a new AI platform that supposedly

is maybe even better than open AI

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and chat GPT reasons being number one

is currently free and open source,

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meaning it is a lot more easily

accessible to the entire world.

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And number two, it has some

pretty cool thinking properties

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where it actually shows you it's.

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Thought process, and you can see how

the computer algorithm and the AI is

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actually thinking through the problem.

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So I thought, why not ask

DeepSeek on how to become a data

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analyst and see what it tells me.

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So let's go.

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So I'm on DeepSeek's website and

I'm going to turn on the DeepThink

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R1 model, which basically is their

better reasoning model right here.

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And I'm going to ask, I

want to be a data analyst.

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What should I do?

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And hit enter.

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One thing about DeepSeek is it

is a lot slower because it is

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actually going through and thinking

through your process step by step.

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So although OpenAI and ChatGPT may be

faster, DeepSeek is actually taking

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its time to think through the problem.

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And you see those thoughts right here.

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So it says, okay, the user

wants to become a data analyst.

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Let me start breaking

down what that entails.

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First, I need to outline the

essential skills they need.

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Data analysis requires technical skills

like SQL, Excel, maybe Python or R.

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I already love that the maybe is here.

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If you guys have watched any of

my videos or listened to any of my

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podcast episodes, you know that I

am not a fan of learning Python.

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First, and I think SQL and Excel

are a better place to start.

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Visualization tools like Tableau

or Power BI are important too.

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Yes, they should also understand

statistics and data cleaning.

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Next education.

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Do they need a degree, a

background in math stats or CS

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helps, but it's not mandatory.

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So far, I really agree with

what DeepSake is saying.

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Obviously it helps to have those.

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STEM background, some sort

of math or stats or computer

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science, but it's not mandatory.

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Maybe mention online courses and

certificates as alternatives.

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Platforms like Coursera,

edX, Datacamp come to mind.

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Okay.

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Certifications from Google or

Microsoft could also add credibility.

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I'm going to be honest.

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I don't think either of those

add a ton of credibility.

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The Google and the Microsoft certificate

certificates still don't mean anything

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in the data analytics world, but

they don't hurt your credibility.

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So I guess that's fine.

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Projects are crucial for experience.

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Oh my gosh.

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I agree with this.

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That's part of the SPN method, right?

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In order to land the data job,

you have to have the skills.

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You have to build projects and

you have to have a network.

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So projects are crucial for experiences.

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They should work on real world

data sets to build a portfolio.

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I totally agree with that.

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Where can they find data sets?

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Kaggle, government portals,

maybe APIs, suggest projects like

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sales analysis or COVID trends.

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Totally.

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We do sales and marketing analysis

in the accelerator program.

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Uh, and we do some healthcare analytics.

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We don't do COVID because COVID has

been done a bajillion times, but

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we do actually diabetes building

a GitHub portfolio and personal

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website would showcase their work.

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Uh, yes, I would reverse those

where I would do a personal website

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first versus a GitHub portfolio.

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If you're just trying to land the

data analyst job, if you're going for

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a data scientist or data engineer,

that's a little bit different, but

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I liked that it didn't just say Gab.

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I liked that it said personal website,

networking and job search strategies.

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Yes.

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That's the end part of the SPN method.

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I like DeepSeek so far.

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LinkedIn is important, it sure is, as

well as attending meetups or conferences,

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tailoring resumes to highlight relevant

skills and projects, applying to

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entry level positions like junior data

analyst or business analyst, maybe

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internships if they're just starting out.

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Continuous learning is key in this field.

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That is so true.

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Encourage joining communities,

following blogs, podcasts.

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Hey, shout out to, uh, podcasts.

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If you want to follow a podcast,

check out the Data Career Podcast.

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Staying updated with trends

like AI and machine learning.

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Also, soft skills like

communication and problem solving

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are often overlooked, but vital.

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So far, so good.

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I really agree with

what DeepSeek is saying.

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Wait, did I miss anything?

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Okay.

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It's so weird to have.

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A, an AI model say something like that.

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Oh, maybe the tools in more detail.

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Excel for basic SQL for databases.

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Python slash R for advanced analysis.

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Visualization tools like Tableau,

Power BI, maybe even Looker.

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Cloud platforms are becoming more

relevant too, like AWS and Google Cloud.

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Also the importance of understanding

the industry they want to work in.

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Man, 100 percent I agree with that.

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Domain knowledge can set them apart.

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Yes, it can.

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Like if you're trying to break into data,

whatever previous experience, whatever

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previous job, whatever previous education

you've had, that's, what's like going

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to be your in, into the world because

data analysis is in every industry.

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So it's like, that is

where you need to focus on.

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Uh, so I liked that.

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I got that.

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Domain knowledge can set them apart.

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Maybe healthcare, finance,

e commerce, et cetera.

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Tailoring projects to specific

industries might help.

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Once again, a hundred percent correct.

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This is why in the accelerator.

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We do nine different projects

in nine different industries.

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So no matter what industry you're

interested in, we have a project that you

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can pitch to a hiring manager recruiter.

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Hey, look, I have, I've done something

very similar to what your job requirement

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is asking you to do, asking me to do.

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Here's my evidence that I can do this.

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Should I mention freelancing or

volunteer work as a way to get experience

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that could be useful for building a

resume, maybe suggest platforms like

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Upwork or volunteering for nonprofits.

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I love the volunteering for nonprofits.

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I think that's great.

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I think most people do.

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Who try to go the freelancing route.

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It's pretty difficult because

freelancing is basically, you have

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to be good at whatever you're doing.

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So in this case, data analysis, but

you have to be good at data as well.

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I mean, you're just starting out

to be perfectly Frank, unless you

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have freelancing experience some

other way, you're bad at both.

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So it's not my favorite place.

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To start if I was you, but it didn't, it

just said, maybe we can consider them.

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What about interview preparation?

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They might need to practice SQL queries,

case studies, and presenting their

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projects, behavioral interviews as well.

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Resources like sleek code for

SQL practice or mock interviews.

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Let me structure this step by step

start with learning technical skills,

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then education and certificates,

building projects, networking, job

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applications, and continuous learning.

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Make sure each section is clear

and actionable avoid jargon.

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So it's accessible.

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Maybe include resources

and examples for each step.

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Becoming data analyst involves building

technical skills, getting practical

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experience and showcasing your abilities.

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Here's a step by step guide

to help you get started.

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All right.

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Just like the thinking was very good.

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And obviously that's not something

you get with open AI and chat GPT.

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So I loved hearing how it was thinking.

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And that way I can kind of

interpret the context of the

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results with what it was thinking.

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Uh, let's go ahead and take a look

at what it officially suggests.

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Build foundational skills, technical

skills, Excel, master pivot tables,

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VLOOKUP and data visualization.

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That is super simple.

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I like that a lot.

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SQL.

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Learn to query databases.

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Join aggregations.

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Resources.

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Mode.

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Analytics.

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SQL tutorial.

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Kaggle.

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Learn SQL.

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Both two great resources for learning SQL.

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Python slash R.

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Focus on libraries like pandas,

NumPy, and visualization tools.

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Resources.

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Data camp.

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Free code camp.

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NumPy.

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I mean, it's a, it's fine to learn pandas

is the big one in visualization tools.

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NumPy actually pandas is built on NumPy.

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So by knowing pandas, you kind of can

do a lot of the stuff that NumPy can.

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There are some things that you need to

use NumPy for alone, but there's not

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that many data visualization, learn

tools like Tableau, Power BI or Looker

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Tableau, public Tableau training.

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Great.

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Statistics, grasp basics like hypothesis,

testing distributions, and regression

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Khan Academy, StatQuest YouTube channel.

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Shout out StatQuest and Josh Starmer.

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I've done a video with Josh in the past.

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Uh, we'll have that in

the show notes down below.

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I also love that it did statistics

last out of all of these, because

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often people are like, don't you have

to know statistics to become data

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analyst and you do, but it's maybe

less important than you realize.

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For instance, you can do a VLOOKUP

really without doing any sort of.

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Tons of statistics.

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There are aggregations there like sums

and averages, mins and maxes, but the

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chances are you've probably learned that

in high school or college at some point.

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So something more complicated like

hypothesis testing or regression,

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those are important too, but not as

important as knowing how to do a VLOOKUP.

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I love that it's including

soft skills here too.

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Soft skills, storytelling,

present insights clearly

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to non technical audiences.

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Problem solving breakdown, complex

questions into data driven solutions.

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A lot of the times, and honestly, like if

you go look at the platforms, it suggests

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like data camp and free code camp,

they really miss on the storytelling.

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And it's really important because

we had cool math, like on the

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podcast recently, and she's like,

people don't take you seriously.

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If you're unable to communicate

your story and your work.

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Effectively.

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So it's often overlooked.

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One of the things we do inside the

accelerator is one, we post our stories

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on LinkedIn in our about section.

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And we try to post on LinkedIn

often, but also we post our projects

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on there with a written writeup.

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Um, and that written form of

communication, we actually will teach you

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how to do video communication as well.

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Like if you like recorded yourself with

a Google slide presentation or with

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your dashboard, but those types of.

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Communication and storytelling

opportunities are really what can set

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you apart as an aspiring data analyst.

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Next section, formal

education and certificates.

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So degrees optional.

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Love that it says optional.

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That is very friendly.

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I appreciate that.

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I'd agree in math, statistics, CS, or

economics is helpful, but not mandatory.

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I agree.

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Online certificates, Google data

analytics certificate, and the

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Microsoft data analyst associate

power BI plus Excel and the IBM data

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analyst professional certificate.

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Obviously those are all good and fine.

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Gain practical experience.

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I love that it's saying this.

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This is awesome.

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Projects, analyze data from Kaggle

UCI learning machine learning

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repository or government portals, data.

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gov.

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I think that is a hundred percent correct.

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Example projects, sales trend analysis,

COVID 19 impact dashboard, customer

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segmentation, obviously not a whole

lot of like, this is just an intro.

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Right.

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So it's not really giving us any more

information on these, but that's great.

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Portfolio share code on GitHub and

create visualizations in Tableau public.

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Build a personal website using GitHub

pages or WordPress to showcase projects.

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Love that it got GitHub pages.

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That is something that

a lot of people miss.

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Now I need to make a different

video on this, but GitHub pages

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is very separate from GitHub.

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It is like it's from GitHub, but

it's like a separate product.

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It's free, but it's basically like the

ability to build personal websites.

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And I love that it got it.

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I personally recommend card now.

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We'll have a link in the show notes.

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To to check out card thing is

the best and easiest place to

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start building your portfolio.

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Freelance slash volunteer offer

services on Upwork or nonprofits.

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I like helping nonprofits more

because I think they could offer more

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support and like a more formal role.

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Once again, I think freelancing

on Upwork, especially if you've

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never freelanced before, it's not

going to lead very far because.

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Freelancing requires a ton

of business experience.

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You have to know how to market yourself.

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You have to know how to

ask a lot of questions.

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There's no one checking your work.

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So I would lean on the volunteer

side versus the freelance, but

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I don't mind them mentioning it.

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Okay.

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Number four, network and apply for jobs.

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LinkedIn optimize your profile

with keywords like data analysts

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and connect with professionals.

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You guys, I can't tell you how

important this first line is.

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And it really, if you just read it,

you're like, okay, that makes sense.

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What does that actually mean?

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You guys, this is one thing we

talk about in the accelerator.

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The more you put the term data analyst

on your LinkedIn profile and your

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resume, the better you'll be off.

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ATS is the LinkedIn

recruiting algorithm is dumb.

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One of the ways it actually like checks

to see how relevant you are to, for

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instance, if you're applying to a data

analyst role is how many times do they

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have the word data analyst on their.

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LinkedIn page.

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And that phrase can be anywhere that

could be in your headline that can

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be in your about section that can

be in your experience section that

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can be in your education section.

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For instance, if you just put aspiring

data analyst in your experience section,

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that actually almost works as good to a

computer as putting the term data analyst.

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So that is really key job platforms,

entry level roles, junior data analyst,

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business analyst, reporting analyst.

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Those are all goods search on

LinkedIn indeed, or specialized

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sites like well found.

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Yeah.

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Well found angels lists.

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I'm a fan of, but not really

for entry level roles.

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They're more senior roles there.

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Instead, I would try

something like findadatajob.

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com or premiumdatajobs.

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com.

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Those are two job boards that I run where

we try to be more entry level friendly.

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Meetups, attend events, data science

meetup, Pi data or virtual webinars.

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I think that's great.

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That's a form of networking

and obviously a great option.

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Okay.

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Number five, ACE interviews, technical

prep, practice SQL on leak code or hacker

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rank review, statistical concepts and

case studies, behavioral questions.

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Use the star method to answer

questions about teamwork and problem

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solving and portfolio walkthrough.

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Be ready to explain your projects,

goals, process, and impact.

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Once again.

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This feels really good because most

people are all about the technical prep

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and the technical prep is important,

but I would say, honestly, at least half

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of my students who land jobs through

the accelerator program, never really

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even have a formal technical interview.

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The other 50 percent definitely do.

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And it's good to be prepared using

things like leak code or hacker.

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I prefer things like strata scratch,

data lemur or analyst builder.

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Instead of these, they're just

more data oriented instead of like.

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Computer science and stuff like that, I

think, but I just want to point off that

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most people ignore behavioral questions.

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And that's one of the things I try not

to ignore with interview simulator.

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If you guys go to interview simulator.

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io, this is my interview

platform where you can practice

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your behavioral questions.

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And then I love that it has the portfolio

walkthrough as well and being able to

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talk about your projects because really,

if you can get an interview and you

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can say, Hey, I have this portfolio.

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I've done this project that's similar

to what I would be doing on the job.

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I think that is an opportunity for you to.

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Try to take the interview kind of

by the reins and flip it on them.

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And they ask you questions about

your project versus just like

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asking random statistical concepts.

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So that's going to make you

feel more comfortable and

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make you look better as well.

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Number six, keep learning, stay

updated, follow blogs like towards data

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science and podcasts like data skeptic.

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Those are both great.

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I would add data career podcast to the

podcast, but if you're listening to this,

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you're probably already following our

podcast, advanced skills, explore machine

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learning, scikit learn, cloud tools, AWS.

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Google Big Query or A B testing.

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: 00:12:24

I think those are, I mean, that's fine.

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: 00:12:26

You're always going to be learning in this

world, but it didn't really talk about job

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: 00:12:30

applications and applying like you don't

want to just like go to advanced skills

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: 00:12:34

without job hunting a ton because you can

get paid to learn machine learning and

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: 00:12:38

cloud tools and A B testing on the job.

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: 00:12:40

Most entry level roles, even maybe

middle roles don't even require that.

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: 00:12:44

Uh, example, learning path, Excel,

SQL, Tableau month, one to two months,

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: 00:12:48

three to four Python and statistics and

month five to six build three or four

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: 00:12:52

portfolio projects and apply for jobs.

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: 00:12:54

Now, if you've listened to any of my

episodes previously, you know, that I

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: 00:12:58

think that most people, um, if they're

willing to put in, you know, 10 to

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: 00:13:02

20 hours a week can cut this in half.

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: 00:13:05

And there's like certain things that we

can do where it's like, we're not going to

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: 00:13:07

spend an entire month learning statistics,

an entire month learning Python.

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: 00:13:12

You just don't need to, when you're

landing your first day at a job.

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: 00:13:14

And also like why wait till month

five and six to build your portfolio

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: 00:13:19

projects and apply for jobs.

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: 00:13:20

In the accelerator, you'll have a project

built within your first 10 days, your

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: 00:13:23

first project built 10 days guaranteed.

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: 00:13:25

Like if you just put it in the hours,

like done, we'll have your first project.

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: 00:13:28

Build and then we want to start

applying for jobs, you know, well,

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: 00:13:31

before we hit the six month mark,

we're probably talking to the two

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: 00:13:34

month mark, if I'm being honest,

because applying for jobs isn't art and

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: 00:13:37

you'll get better at it as you go on.

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: 00:13:38

But overall, I don't hate this

plan at all by combining skills,

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: 00:13:41

projects, and networking.

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: 00:13:42

Holy crap.

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: 00:13:44

Did they steal that from me?

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: 00:13:45

Skills, projects, and networking.

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: 00:13:47

That's the SPN method.

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: 00:13:48

I came up with the SPN method.

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: 00:13:49

I'm the only one who's ever put those

things right next to each other.

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: 00:13:53

Skills, projects, and networking.

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: 00:13:54

Call it the SPN method.

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: 00:13:55

I built it myself.

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: 00:13:56

I have a notebook somewhere over

here where like, I just, I wrote

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: 00:13:59

down my whole like framework and like

tried to figure out what to call it.

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: 00:14:02

And we ended up landing on SPN.

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: 00:14:03

Wow.

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: 00:14:04

You'll position yourself

strongly for a data analyst role.

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: 00:14:06

Stay smart, stay consistent

and iterate based on feedback.

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: 00:14:09

Overall, you guys, like, I feel

like this was a pretty good roadmap.

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: 00:14:14

Right here.

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: 00:14:15

Like I'm pretty impressed by this and, uh,

it's not the worst thing on planet earth.

378

: 00:14:19

Like it, it did a better job

almost in the instructions.

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: 00:14:22

I think of like the Python where it was

like maybe Python and R somewhere up here.

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: 00:14:26

Right.

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: 00:14:26

And, but I think it did great on

mentioning the behavioral questions.

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: 00:14:29

I think it did really good on

the networking and the, and

383

: 00:14:33

the projects and the portfolio.

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: 00:14:35

I thought I did great talking

about GitHub pages overall.

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: 00:14:37

I think if you followed

this plan, you would be.

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: 00:14:40

Pretty well off.

387

: 00:14:41

I mean, this plan is basically what

I outlined in my previous episodes.

388

: 00:14:45

It's basically following the SPN method.

389

: 00:14:47

I mean, literally it says by

combining skills, projects, and

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: 00:14:49

networking, you'll position yourself

strongly for a data analyst role.

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: 00:14:52

And I agree like that, the SPN

method will set you up exactly.

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: 00:14:56

This way.

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: 00:14:56

So, uh, I really like this from deep seek.

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: 00:14:58

I'm going to play around with this more.

395

: 00:15:00

If you guys want to follow the

SPN method, please consider

396

: 00:15:03

joining the accelerator program.

397

: 00:15:04

This is basically a coaching led and

group cohort learning style where

398

: 00:15:09

you're basically going to do all

of these things, but we're going to

399

: 00:15:11

give it to you exactly step by step.

400

: 00:15:13

You're not going to have to go figure

out like, you know, how do I learn

401

: 00:15:16

data visualization and Tableau public?

402

: 00:15:18

Or like what courses should I take?

403

: 00:15:20

We'll give you the exact roadmap.

404

: 00:15:21

We'll teach you the exact projects.

405

: 00:15:23

We'll give you the exact data to

build your projects, to learn the

406

: 00:15:26

skills and to grow your network.

407

: 00:15:28

We'll show you exactly

how to actually optimize.

408

: 00:15:30

Like what does it actually mean

to optimize your profile with

409

: 00:15:32

keywords like data analyst?

410

: 00:15:34

So that's of interest to you.

411

: 00:15:35

We'll have a link in the show notes

down below and let me know what

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: 00:15:38

you guys want me to do next with

deep seek down in the comments.

413

: 00:15:42

Should I try to analyze data?

414

: 00:15:44

Should we compare it to

something like chat GPT?

415

: 00:15:46

Let me know in the comments down below.