229: i asked gpt-6 astra how to become a data analyst (it was wrong)

Speaker: 00:00:00

If you haven't heard, OpenAI just

released their latest model, GPT-6

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Astra, and it's supposedly really insane.

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This isn't just some small update.

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Sam Altman, the company's CEO,

says that we're now in the AGI era.

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It's supposedly amazing,

and everyone's freaking out.

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But how good is it actually?

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I wanted to test out for myself

firsthand, so I logged into my ChatGPT

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account and asked a simple question:

how to become a data analyst in 50 days?

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So let's see how it does.

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But before we do, just a quick note.

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If you're trying to become a data

analyst and want some personalized

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advice, I'll actually make you a

custom data analyst roadmap for free.

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All you need to do is go

to dataanalystroadmap.com,

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fill in a few questions, and it

will literally give you a real

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roadmap for you to follow that's

based on real intelligence, not

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just artificial intelligence.

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But let's go ahead and go to the

artificial intelligent roadmap now.

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All right.

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Here's what it said.

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It said, "I'll put together a

practical 49-day plan and check

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current learning resources to

keep it focused on useful skills."

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Now, I'm actually doing this inside

of what's called Codex, which is

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basically ChatGPT's coding version.

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And the reason I'm doing it inside

of Codex is I only had access to

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GPT-6 Astra in, uh, Codex or in Work.

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And the problem with using Work is it has

all the details about me and what I say

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in ChatGPT, so I, I first did it inside

of Work, and the answer was really biased

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because it already knew a lot about me

and what I suggest in the SPN method, and

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I wanted to give it an unbiased opinion

that doesn't have any information from

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me, at least from me actually using it.

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So here's what it said.

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"You can build a solid beginner

foundation and a portfolio in 49 days."

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I agree with that.

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"Getting hired within that time is

possible but depends on your starting

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skills, experience, and job market."

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I agree with that statement as well.

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"Assuming you're starting from scratch,

plan for three to four hours, uh,

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daily, roughly 100 to 150 to 200 hours."

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I think that's about the right amount for

my boot camp data analytics accelerator.

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I say you need to spend about 15 to

20 hours a week, so that's, what,

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like, three hours a day, essentially.

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Uh, and our program, uh, is eventually,

like, this long, so that makes sense.

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"I'd prioritize Excel, SQL, and

Power BI with basic statistics

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and communication throughout."

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I think that's a good plan so far.

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"Days one through seven, Excel and

data basics, sorting, filtering,

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missing values, duplicates,

dates, SUMIFs, COUNTIFs, XLOOKUP,

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pivot tables, and charts."

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I'm a little bit worried that this

knows about my curriculum inside of

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the accelerator program because this

is exactly what we cover in week one.

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"Finish with a clean data set and one-page

report answering five business questions."

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That's like literally what we do,

and then we publish it on LinkedIn.

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So this is-- I'm still worried that

it knows who I am, but I asked and

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it said it didn't, so, uh, maybe this

is somewhere deep in its memories.

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Two, SQL fundamentals.

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Okay, maybe this is a little bit different

because I actually recommend going to

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Power BI or, in my case, Tableau next.

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Um, it's going to SQL.

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The order doesn't really matter.

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It's just I think Power

BI and Tableau are easier.

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It's like a little bit l-less of a s-

learning curve than SQL for most people.

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And I re-- I wanna like

stack the wins, right?

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So that's why I personally,

after Excel, go into one of,

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you know, Power BI or Tableau.

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It says, uh, days eight through 14,

SQL fundamentals, select, where,

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order by, group by, having, case,

aggregates, and handling null.

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That's all very solid.

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I 100% agree with that.

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Finish with 25 to 30 practice

queries you can explain.

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Uh, I love this.

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Here's a fun fact for you.

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Did you know that the Google Data

Analytics certificate, which probably

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a lot of you guys listening or, you

know, watching have taken, that you

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only do 20 queries, 20 SQL queries

in the entire certificate program.

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It-- the program is supposed to

take six months, and you do q- 20

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queries, and here you are having,

you know, uh, ChatGPT-6 telling you,

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"You can do 25 queries in one week."

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And I 100% agree with that,

and that's what we do inside

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of the accelerator program.

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So that's makes sense.

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Days 15 to 21, SQL analysis, joins,

subqueries, CTEs, date calculations,

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and introductory window functions.

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Finish with an analysis of sales

or customers using multiple tables.

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Uh, this is really good advice because

I think you need to build the SQL

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fundamentals first, and then you kind

of get into more complicated things.

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I think joins are pretty complicated.

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Um, so moving that to

week two makes sense.

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CTEs and subqueries, that

makes sense in week two.

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And then introductory window functions,

I think that's exactly where I draw

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the line on how much SQL you should

know to land your first data job.

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You should kind of know what a window

function is, but you don't have to

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be perfect at them, 'cause a lot of

jobs don't even use SQL, and a lot of

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jobs don't even use window functions.

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But you should be familiar with what

they are, and if you needed to do one,

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you could, you know, you could create

one with, with AI and double-check it.

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Um, I like that we're analyzing

sales or customer's data.

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I think that makes sense.

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Um, that's one of the projects we

do inside the accelerator program.

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Days 22 through 28, Power BI.

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P- Power Query.

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That's interesting that it has Power

Query as, like, the first thing.

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Relationships, basic DAX, measures,

filters, and chart selection.

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Oh, this is interesting.

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I don't think this is putting a

big enough emphasis on the data

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visualization and chart selection.

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It's, like, really more focusing on,

like, the data modeling and the data

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prep and, like, the data infrastructure.

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And a lot of the time, you're not gonna

be in charge of that as a data analyst,

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especially as a junior data analyst.

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These are good things to know, but

I just don't think I'd emphasize it.

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I would focus more on data visualization,

'cause making charts is gonna be

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part of your job as a data analyst

pretty much no matter where you go.

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And so far, we're a month

in, and we, like, really

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haven't done much with charts.

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Um, it says you finish with an

interactive dashboard whose totals

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match your da- your source data.

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Okay, yeah, great, like creating

a project in a dashboard.

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Um, I wanna emphasize all this "finish

with," it's great to finish with

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these things, but if they just stay

on your computer, that's not enough.

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You have to post them to a portfolio.

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They have to be public.

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There's so many different ways that

you can build a portfolio, um, so

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many different platforms you can do.

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You can do your own personal websites

on Wix or Squarespace or Carrd.

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You could use LinkedIn,

you could use Substack.

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You could even use YouTube

if you wanted to make videos.

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Um, I've created my own portfolio

hosting platform called My

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Datafolio that you can check out.

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It'll have a link in the

description down below.

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That's what I think is best,

but, like, there's so many

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different options, you guys.

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My point here is your desktop,

your downloads folder does

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not count as a portfolio.

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Make sure all of these things actually

leave your computer and get out.

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Okay, uh, 29 through 35.

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First portfolio project.

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See, this is one thing I don't like

as well with, with these suggestions.

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It's like, why is this

not a portfolio project?

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Why is this not a portfolio project?

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Why is this not a portfolio project?

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You did all the work.

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Like, why not just turn

it into a portfolio piece?

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Like, why do we have to do

a separate portfolio piece?

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That's neither here nor there, I guess.

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Uh, days 29 through 35.

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Take a business question through

cleaning, sequel analysis,

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visualization, and recommendations.

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A complete case study with queries,

dashboard screenshots, and findings.

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Um, okay, this is kind of what I would

call, like, more of, like, a capstoney

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project, where it's like you're combining

maybe SQL and Power BI or Excel and

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Power BI or something like that.

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Uh, days 36 through 42,

second portfolio project.

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Use a different dataset, work

independently, and explain assumptions.

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You'll leave with a f- second case

study and five-minute presentation.

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Um, okay.

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What type of presentation?

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Days 43 through 49,

interviews and applications.

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What to practice: SQL exercises,

spreadsheet tasks, explaining

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projects, resume tailoring.

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Finish with a portfolio link, a focused

resume, and targeted applications.

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Okay, so, so far with this

plan, it's, it's good.

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Uh, I think, you know, it's talked

about different skills to learn.

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It's talked about creating projects and

putting them on a portfolio, but it really

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doesn't talk about networking at all,

which is a big part of landing a data job.

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You know, in order to land a data job, you

have to follow the SPN method: learn the

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right skills, build projects and put them

on a portfolio, and network like crazy.

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Um, and I think it put a really

big emphasis on the skills.

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Um, secondarily with the project, but,

like, really, what ends up getting most

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people hiring is the networking they're

doing, and I will include updating your

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LinkedIn and your resume as networking.

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And I think we're not focusing

on that enough, and I think that

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we're focusing on it too late.

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In my accelerator program, basically

in week s- one and two, you'll work

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on your LinkedIn, and then on week

three, you'll work on your resume.

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And that way, you can basically

start applying for jobs once

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you're 21 days in because it

takes a while to hear back, right?

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Um, you know, one thing I mentioned

earlier is getting hired within

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that time is possible, but it

depends on your starting skills,

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experience, and job market.

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I'll also say, well, if you're only

applying for jobs on day 43, you're

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not gonna land a job by day 49.

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It takes about a week, even if you're

going to be the winning candidate.

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It would take you a week, uh, at least

to probably even get the first interview.

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"On day one, also review 10 relevant

job postings in your target location.

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Record the current requirements

so you can adjust this plan to

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the roles you actually want."

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Ah, this is kind of interesting.

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So this is saying, like, look at

10 roles and, you know, adjust this

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plan based off of those 10 roles.

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Um, I think that's decent advice.

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I think there's also some advice, sound

advice in just, like, looking at 10 roles,

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seeing what they require the most, and

trying to learn the things that are the

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easiest to learn and require the most.

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That's one of the recommendations I

give is not to learn Python 'cause it's

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only required in 20% of data analyst

jobs, and it's really hard to learn.

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It's a steep learning curve, right?

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So it doesn't make sense to

spend a lot of time learning

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Python Use this daily routine.

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45 minutes, learn one concept.

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90 to 120 minutes, solve

problems or build something.

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30 minutes, check results

and revisit mistakes.

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15 minutes, explain one

finding in plain English.

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I like that it's putting a good focus

on actually explaining your findings

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and doing some of the reporting.

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One thing I mentioned earlier is we

need to make sure we publish all of our

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projects, and when my students publish

their projects, they have to actually

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write down what they did, why they did

it, and what they learned from it, and,

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like, what recommendations they'd have to

the business based off of their findings.

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And I think that's really important

to do 'cause that's, like, the most

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important part of a data analyst job.

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If you just analyze data for fun and

you don't actually say what's gonna…

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you know, what the business

should change, then we're just

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wasting our time, to be honest.

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Okay.

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Along the way, learn percentages.

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Okay.

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Weighted average, mean versus

median, outliers, sampling bias,

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and correlation versus causation.

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Practice check, practice checking

row counts, duplicate keys, and

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totals, especially after joins.

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Uh, I think that's pretty

sound advoice- advice.

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Keep your learning resources small.

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Excel, Microsoft's pivot table guide.

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Oh, man, this is where I

think I'm gonna disagree here.

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Like, it's just pointing to the

documentation from Microsoft Excel

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about pivot tables, which I think

is just boring, to be honest.

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Um, I don't think it's, like, the best

tutorial on pivot tables on planet Earth.

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Uh, also, does it give you any of the

data that you're supposed to have?

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No.

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So you're just supposed to read

this and not actually do it?

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That sounds so boring, and I don't think

I would ever learn from doing that.

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So, uh, I would, I would ignore

that advice here, and I would

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try to do some hands-on stuff.

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SQL Bolts provides interactive

browser, uh, exercises for

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queries, joins, and aggregation.

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Okay, that's fine.

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Power BI, Microsoft's l- learns

data analyst path cover prepping,

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modeling, and reporting data.

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Let's take a look at this

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Um, your path to, uh,

being a data analyst.

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So you have get started with

Microsoft, uh, Power BI, Power BI.

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Yeah, I'm biased obviously, but

I don't love getting like a data

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analyst roadmap from like a provider

like Microsoft, 'cause they're

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obviously going to be biased.

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It's like Google Sheets…

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Or sorry, Google Data Analyst certificate

teaches you Google Sheets versus

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Excel, and I know they're very similar,

but literally Excel is used, what?

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90 times more, 70 times more, I

think is the stat I have on that.

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So it's like you should learn, really

learn Excel instead of Google Sheets.

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Um, and I don't really think like y-

your training should be like, "Oh,

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you should learn Power BI 'cause we

created Power BI," type of a thing.

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That's just my opinion.

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Um, that, that could be fine.

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For your first project, try

Microsoft's financial sample workbook.

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I think that's gonna be boring.

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Investigate which products and

markets drive sales and profits, how

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performance changes over time, and

what deserves further investigation.

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E-portfolio project should show the

business question and data source.

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I agree with that.

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Cleaning decisions and quality checks.

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I agree with that.

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Your queries or calculations.

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I agree with that.

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Three supported findings.

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I mean, three is an arbitrary

number, but some supported findings.

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And then five, recommendations

and limitations.

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And I do agree with that.

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Um, but I think you should have the

recommendations, uh, and the findings up

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top as well, like in an intro section.

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You should BLUF, bottom line up front.

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Because if you're expecting a hiring

manager or recruiter to scroll through or

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whatever, you know, go through, "Oh, this

is the business questions and the source

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and the queries and all these things,"

before seeing the recommendations and the

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findings, that's just not gonna happen.

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So get the buy-in from them by having

the findings and recommendations

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up front in your portfolio project.

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For this deadline, I postpone

Python unless your target

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postings consistently require it.

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If they do, substitute some second

time, second project time with

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Kaggle's Python and Pandas lesson.

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Um, I actually think Kaggle's

lessons aren't bad at all, so good

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recommendation there, and I also

recommend to postpone Python because

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it's only required in 20% of data

jobs, and it's a steep learning curve.

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So that's a good advice.

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Your 49-Day Readiness Check.

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Can you take an unfamiliar dataset, clean

it, query it, verify the numbers, create a

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useful chart, and explain a recommendation

without following a tutorial?

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Use this as your benchmark

for beginning applications.

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Oh, no.

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That was so good until

the very end right here.

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"Use that as your benchmark

for beginning applications."

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No, no, no, no, no,

no, no, no, no, no, no.

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If, if you use that as a benchmark for

applications, you're probably never

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going to apply for jobs because even me,

who I've been doing this for 10 years

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now, of course I can take an unfamiliar

data set, I can clean, I can query,

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I can verify the numbers, create a

useful chart, explain a recommendation

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without following a tutorial.

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I can do that, right?

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But it's like, did I do it

the best that I could have?

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I don't know.

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Like, it, it…

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I could always make it better, and I,

I'm never 100% confidence in my actual

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analysis unless I've spent months on it.

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And like, this is just like such

a bummer line for your confidence.

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Like, you're never going to

feel ready to start applying for

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applications, so you really shouldn't

have a benchmark for applications.

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You should just start applying

for jobs and let the market

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: 00:15:12

tell you the benchmark, right?

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: 00:15:13

Because- The, a hiring manager or

recruiter, you know, if they're trying

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: 00:15:18

to see if you can actually do this, they

can't really tell from your resume or

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: 00:15:23

your LinkedIn or from your application.

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: 00:15:25

They would…

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: 00:15:26

They need to interview you or give you a

case study to see if you can actually take

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: 00:15:29

a data set and analyze it on your own.

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: 00:15:32

They can't really tell if you can or

can't from your resume or your LinkedIn.

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: 00:15:35

So until you get an interview, and you

fail the interview, like the tech portion,

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: 00:15:40

you just failed, or they gave you the

case study and you couldn't do it, I

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: 00:15:44

would not use this as a benchmark at all.

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: 00:15:46

Like, I think you should start

applying for jobs ASAP, and if you

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get an interview, that's really good.

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: 00:15:52

If you fail the interview, okay, we

move on and we get another interview,

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: 00:15:55

and we do better from the lessons we

learned from failing the interview.

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: 00:15:59

Um, man, I think this would

keep a lot of people stuck.

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: 00:16:03

Um, so I think that's really bad advice

to, to really make that your benchmark.

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: 00:16:08

And in fact, a lot of junior data analyst

roles, you know, especially the ones

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: 00:16:11

that maybe don't pay amazingly, like

you're not expected to be a senior data

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: 00:16:15

analyst where you take an unfamiliar

data set, you clean it, you query it,

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: 00:16:19

you verify all the numbers, you create

a chart and explain a recommendation.

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: 00:16:22

You might just be a SQL monkey.

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: 00:16:24

You might just write SQL queries

and put that under a report.

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: 00:16:27

Or you might just create useful charts.

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: 00:16:29

Like, you, maybe you don't

need to clean the data set.

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: 00:16:31

Like, it's not…

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: 00:16:33

This is, this is something you need

to be comfortable with eventually,

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: 00:16:36

but not, not before applying for jobs.

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: 00:16:39

Maybe not even before

landing your first job.

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: 00:16:42

Okay?

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: 00:16:42

So I think this is really discouraging,

and, uh, I would be kind of depressed.

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: 00:16:47

And if, if I were following this,

I don't know if I would ever

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: 00:16:49

start applying for, for roles.

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: 00:16:51

Um, that would be, that would

be kind of disappointing.

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: 00:16:53

So overall, I think the advice, it wasn't

terrible, but it didn't like blow me away.

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: 00:16:57

I certainly didn't feel like,

oh my gosh, there's AGI.

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: 00:17:00

It's here.

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: 00:17:00

It's teaching me everything.

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: 00:17:01

I thought it did a good job of

focusing on the right skills,

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: 00:17:04

Excel, SQL, and BI, and saying,

"Don't learn Python or R right now."

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: 00:17:08

But I think in terms of how to

learn them, it was pretty darn

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: 00:17:11

bland and pretty dang boring.

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: 00:17:13

Like, who wants to read

the Excel product manual?

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: 00:17:16

That's not learning, that's reading.

352

: 00:17:18

For me, learning is hands-on.

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: 00:17:20

It should be doing.

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: 00:17:21

It should be building something.

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: 00:17:22

Which that actually brings me to my

next point, which is the projects.

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: 00:17:24

I'm glad it mentioned projects and

doing projects, but I don't get

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: 00:17:27

why you have to wait like weeks

before doing your first project.

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: 00:17:31

Why not do it as part of the

learning process, and like do

359

: 00:17:34

it earlier if it's so important?

360

: 00:17:36

I also didn't think it emphasized

sharing these projects nearly enough.

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: 00:17:39

Like, share them on your resume,

share them on your LinkedIn,

362

: 00:17:42

share them on your portfolio.

363

: 00:17:43

Just share them with your

neighbor, like with anyone.

364

: 00:17:45

It's just, like, a project

isn't really useful if it's not

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: 00:17:47

shared, so I wish it would've

emphasized that a little bit more.

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: 00:17:50

And lastly, I felt like it focused way

too much on learning data analytics

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: 00:17:54

and not becoming a data analyst.

368

: 00:17:56

And listen to that again.

369

: 00:17:58

Learning data analytics and

becoming a data analyst are not

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: 00:18:01

the same thing, or at least I

don't think they're the same thing.

371

: 00:18:03

One is about learning the technical, like,

actual frameworks of analyzation and the

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: 00:18:07

tools to analyze, and the other is more

of like a street smart, hack your way, the

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: 00:18:12

actual grind, the effort of getting a job.

374

: 00:18:15

And I'd argue that that one is

actually more important because in the

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: 00:18:18

end, the job is what gets you paid.

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: 00:18:20

And if you don't do the second

one, you don't magically get paid.

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: 00:18:23

Like, if you're the best data analyst

on planet Earth, but you don't

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: 00:18:25

apply for jobs, you don't have a

resume, you're not gonna get paid.

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: 00:18:27

So it did an okay job,

but not a great job.

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: 00:18:30

If you want an excellent, free,

personalized data roadmap just for you

381

: 00:18:34

based off of real-life intelligence

and data and first principles,

382

: 00:18:38

then go to dataanalystroadmap.com.

383

: 00:18:41

You'll fill in a few questions, and

then we will give you a personalized

384

: 00:18:45

roadmap that you can literally

follow to land your first data job.

385

: 00:18:48

This one's made by real intelligence,

not artificial intelligence.

386

: 00:18:51

Remember that