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⌚ TIMESTAMPS
00:00 – GPT-6 Astra is here
02:15 – Excel and SQL first
07:45 – Start networking early
10:54 – Learn by doing
14:03 – The worst advice in the plan
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Transcripts
If you haven't heard, OpenAI just
released their latest model, GPT-6
2
:Astra, and it's supposedly really insane.
3
:This isn't just some small update.
4
:Sam Altman, the company's CEO,
says that we're now in the AGI era.
5
:It's supposedly amazing,
and everyone's freaking out.
6
:But how good is it actually?
7
:I wanted to test out for myself
firsthand, so I logged into my ChatGPT
8
:account and asked a simple question:
how to become a data analyst in 50 days?
9
:So let's see how it does.
10
:But before we do, just a quick note.
11
:If you're trying to become a data
analyst and want some personalized
12
:advice, I'll actually make you a
custom data analyst roadmap for free.
13
:All you need to do is go
to dataanalystroadmap.com,
14
:fill in a few questions, and it
will literally give you a real
15
:roadmap for you to follow that's
based on real intelligence, not
16
:just artificial intelligence.
17
:But let's go ahead and go to the
artificial intelligent roadmap now.
18
:All right.
19
:Here's what it said.
20
:It said, "I'll put together a
practical 49-day plan and check
21
:current learning resources to
keep it focused on useful skills."
22
:Now, I'm actually doing this inside
of what's called Codex, which is
23
:basically ChatGPT's coding version.
24
:And the reason I'm doing it inside
of Codex is I only had access to
25
:GPT-6 Astra in, uh, Codex or in Work.
26
:And the problem with using Work is it has
all the details about me and what I say
27
:in ChatGPT, so I, I first did it inside
of Work, and the answer was really biased
28
:because it already knew a lot about me
and what I suggest in the SPN method, and
29
:I wanted to give it an unbiased opinion
that doesn't have any information from
30
:me, at least from me actually using it.
31
:So here's what it said.
32
:"You can build a solid beginner
foundation and a portfolio in 49 days."
33
:I agree with that.
34
:"Getting hired within that time is
possible but depends on your starting
35
:skills, experience, and job market."
36
:I agree with that statement as well.
37
:"Assuming you're starting from scratch,
plan for three to four hours, uh,
38
:daily, roughly 100 to 150 to 200 hours."
39
:I think that's about the right amount for
my boot camp data analytics accelerator.
40
:I say you need to spend about 15 to
20 hours a week, so that's, what,
41
:like, three hours a day, essentially.
42
:Uh, and our program, uh, is eventually,
like, this long, so that makes sense.
43
:"I'd prioritize Excel, SQL, and
Power BI with basic statistics
44
:and communication throughout."
45
:I think that's a good plan so far.
46
:"Days one through seven, Excel and
data basics, sorting, filtering,
47
:missing values, duplicates,
dates, SUMIFs, COUNTIFs, XLOOKUP,
48
:pivot tables, and charts."
49
:I'm a little bit worried that this
knows about my curriculum inside of
50
:the accelerator program because this
is exactly what we cover in week one.
51
:"Finish with a clean data set and one-page
report answering five business questions."
52
:That's like literally what we do,
and then we publish it on LinkedIn.
53
:So this is-- I'm still worried that
it knows who I am, but I asked and
54
:it said it didn't, so, uh, maybe this
is somewhere deep in its memories.
55
:Two, SQL fundamentals.
56
:Okay, maybe this is a little bit different
because I actually recommend going to
57
:Power BI or, in my case, Tableau next.
58
:Um, it's going to SQL.
59
:The order doesn't really matter.
60
:It's just I think Power
BI and Tableau are easier.
61
:It's like a little bit l-less of a s-
learning curve than SQL for most people.
62
:And I re-- I wanna like
stack the wins, right?
63
:So that's why I personally,
after Excel, go into one of,
64
:you know, Power BI or Tableau.
65
:It says, uh, days eight through 14,
SQL fundamentals, select, where,
66
:order by, group by, having, case,
aggregates, and handling null.
67
:That's all very solid.
68
:I 100% agree with that.
69
:Finish with 25 to 30 practice
queries you can explain.
70
:Uh, I love this.
71
:Here's a fun fact for you.
72
:Did you know that the Google Data
Analytics certificate, which probably
73
:a lot of you guys listening or, you
know, watching have taken, that you
74
:only do 20 queries, 20 SQL queries
in the entire certificate program.
75
:It-- the program is supposed to
take six months, and you do q- 20
76
:queries, and here you are having,
you know, uh, ChatGPT-6 telling you,
77
:"You can do 25 queries in one week."
78
:And I 100% agree with that,
and that's what we do inside
79
:of the accelerator program.
80
:So that's makes sense.
81
:Days 15 to 21, SQL analysis, joins,
subqueries, CTEs, date calculations,
82
:and introductory window functions.
83
:Finish with an analysis of sales
or customers using multiple tables.
84
:Uh, this is really good advice because
I think you need to build the SQL
85
:fundamentals first, and then you kind
of get into more complicated things.
86
:I think joins are pretty complicated.
87
:Um, so moving that to
week two makes sense.
88
:CTEs and subqueries, that
makes sense in week two.
89
:And then introductory window functions,
I think that's exactly where I draw
90
:the line on how much SQL you should
know to land your first data job.
91
:You should kind of know what a window
function is, but you don't have to
92
:be perfect at them, 'cause a lot of
jobs don't even use SQL, and a lot of
93
:jobs don't even use window functions.
94
:But you should be familiar with what
they are, and if you needed to do one,
95
:you could, you know, you could create
one with, with AI and double-check it.
96
:Um, I like that we're analyzing
sales or customer's data.
97
:I think that makes sense.
98
:Um, that's one of the projects we
do inside the accelerator program.
99
:Days 22 through 28, Power BI.
100
:P- Power Query.
101
:That's interesting that it has Power
Query as, like, the first thing.
102
:Relationships, basic DAX, measures,
filters, and chart selection.
103
:Oh, this is interesting.
104
:I don't think this is putting a
big enough emphasis on the data
105
:visualization and chart selection.
106
:It's, like, really more focusing on,
like, the data modeling and the data
107
:prep and, like, the data infrastructure.
108
:And a lot of the time, you're not gonna
be in charge of that as a data analyst,
109
:especially as a junior data analyst.
110
:These are good things to know, but
I just don't think I'd emphasize it.
111
:I would focus more on data visualization,
'cause making charts is gonna be
112
:part of your job as a data analyst
pretty much no matter where you go.
113
:And so far, we're a month
in, and we, like, really
114
:haven't done much with charts.
115
:Um, it says you finish with an
interactive dashboard whose totals
116
:match your da- your source data.
117
:Okay, yeah, great, like creating
a project in a dashboard.
118
:Um, I wanna emphasize all this "finish
with," it's great to finish with
119
:these things, but if they just stay
on your computer, that's not enough.
120
:You have to post them to a portfolio.
121
:They have to be public.
122
:There's so many different ways that
you can build a portfolio, um, so
123
:many different platforms you can do.
124
:You can do your own personal websites
on Wix or Squarespace or Carrd.
125
:You could use LinkedIn,
you could use Substack.
126
:You could even use YouTube
if you wanted to make videos.
127
:Um, I've created my own portfolio
hosting platform called My
128
:Datafolio that you can check out.
129
:It'll have a link in the
description down below.
130
:That's what I think is best,
but, like, there's so many
131
:different options, you guys.
132
:My point here is your desktop,
your downloads folder does
133
:not count as a portfolio.
134
:Make sure all of these things actually
leave your computer and get out.
135
:Okay, uh, 29 through 35.
136
:First portfolio project.
137
:See, this is one thing I don't like
as well with, with these suggestions.
138
:It's like, why is this
not a portfolio project?
139
:Why is this not a portfolio project?
140
:Why is this not a portfolio project?
141
:You did all the work.
142
:Like, why not just turn
it into a portfolio piece?
143
:Like, why do we have to do
a separate portfolio piece?
144
:That's neither here nor there, I guess.
145
:Uh, days 29 through 35.
146
:Take a business question through
cleaning, sequel analysis,
147
:visualization, and recommendations.
148
:A complete case study with queries,
dashboard screenshots, and findings.
149
:Um, okay, this is kind of what I would
call, like, more of, like, a capstoney
150
:project, where it's like you're combining
maybe SQL and Power BI or Excel and
151
:Power BI or something like that.
152
:Uh, days 36 through 42,
second portfolio project.
153
:Use a different dataset, work
independently, and explain assumptions.
154
:You'll leave with a f- second case
study and five-minute presentation.
155
:Um, okay.
156
:What type of presentation?
157
:Days 43 through 49,
interviews and applications.
158
:What to practice: SQL exercises,
spreadsheet tasks, explaining
159
:projects, resume tailoring.
160
:Finish with a portfolio link, a focused
resume, and targeted applications.
161
:Okay, so, so far with this
plan, it's, it's good.
162
:Uh, I think, you know, it's talked
about different skills to learn.
163
:It's talked about creating projects and
putting them on a portfolio, but it really
164
:doesn't talk about networking at all,
which is a big part of landing a data job.
165
:You know, in order to land a data job, you
have to follow the SPN method: learn the
166
:right skills, build projects and put them
on a portfolio, and network like crazy.
167
:Um, and I think it put a really
big emphasis on the skills.
168
:Um, secondarily with the project, but,
like, really, what ends up getting most
169
:people hiring is the networking they're
doing, and I will include updating your
170
:LinkedIn and your resume as networking.
171
:And I think we're not focusing
on that enough, and I think that
172
:we're focusing on it too late.
173
:In my accelerator program, basically
in week s- one and two, you'll work
174
:on your LinkedIn, and then on week
three, you'll work on your resume.
175
:And that way, you can basically
start applying for jobs once
176
:you're 21 days in because it
takes a while to hear back, right?
177
:Um, you know, one thing I mentioned
earlier is getting hired within
178
:that time is possible, but it
depends on your starting skills,
179
:experience, and job market.
180
:I'll also say, well, if you're only
applying for jobs on day 43, you're
181
:not gonna land a job by day 49.
182
:It takes about a week, even if you're
going to be the winning candidate.
183
:It would take you a week, uh, at least
to probably even get the first interview.
184
:"On day one, also review 10 relevant
job postings in your target location.
185
:Record the current requirements
so you can adjust this plan to
186
:the roles you actually want."
187
:Ah, this is kind of interesting.
188
:So this is saying, like, look at
10 roles and, you know, adjust this
189
:plan based off of those 10 roles.
190
:Um, I think that's decent advice.
191
:I think there's also some advice, sound
advice in just, like, looking at 10 roles,
192
:seeing what they require the most, and
trying to learn the things that are the
193
:easiest to learn and require the most.
194
:That's one of the recommendations I
give is not to learn Python 'cause it's
195
:only required in 20% of data analyst
jobs, and it's really hard to learn.
196
:It's a steep learning curve, right?
197
:So it doesn't make sense to
spend a lot of time learning
198
:Python Use this daily routine.
199
:45 minutes, learn one concept.
200
:90 to 120 minutes, solve
problems or build something.
201
:30 minutes, check results
and revisit mistakes.
202
:15 minutes, explain one
finding in plain English.
203
:I like that it's putting a good focus
on actually explaining your findings
204
:and doing some of the reporting.
205
:One thing I mentioned earlier is we
need to make sure we publish all of our
206
:projects, and when my students publish
their projects, they have to actually
207
:write down what they did, why they did
it, and what they learned from it, and,
208
:like, what recommendations they'd have to
the business based off of their findings.
209
:And I think that's really important
to do 'cause that's, like, the most
210
:important part of a data analyst job.
211
:If you just analyze data for fun and
you don't actually say what's gonna…
212
:you know, what the business
should change, then we're just
213
:wasting our time, to be honest.
214
:Okay.
215
:Along the way, learn percentages.
216
:Okay.
217
:Weighted average, mean versus
median, outliers, sampling bias,
218
:and correlation versus causation.
219
:Practice check, practice checking
row counts, duplicate keys, and
220
:totals, especially after joins.
221
:Uh, I think that's pretty
sound advoice- advice.
222
:Keep your learning resources small.
223
:Excel, Microsoft's pivot table guide.
224
:Oh, man, this is where I
think I'm gonna disagree here.
225
:Like, it's just pointing to the
documentation from Microsoft Excel
226
:about pivot tables, which I think
is just boring, to be honest.
227
:Um, I don't think it's, like, the best
tutorial on pivot tables on planet Earth.
228
:Uh, also, does it give you any of the
data that you're supposed to have?
229
:No.
230
:So you're just supposed to read
this and not actually do it?
231
:That sounds so boring, and I don't think
I would ever learn from doing that.
232
:So, uh, I would, I would ignore
that advice here, and I would
233
:try to do some hands-on stuff.
234
:SQL Bolts provides interactive
browser, uh, exercises for
235
:queries, joins, and aggregation.
236
:Okay, that's fine.
237
:Power BI, Microsoft's l- learns
data analyst path cover prepping,
238
:modeling, and reporting data.
239
:Let's take a look at this
240
:Um, your path to, uh,
being a data analyst.
241
:So you have get started with
Microsoft, uh, Power BI, Power BI.
242
:Yeah, I'm biased obviously, but
I don't love getting like a data
243
:analyst roadmap from like a provider
like Microsoft, 'cause they're
244
:obviously going to be biased.
245
:It's like Google Sheets…
246
:Or sorry, Google Data Analyst certificate
teaches you Google Sheets versus
247
:Excel, and I know they're very similar,
but literally Excel is used, what?
248
:90 times more, 70 times more, I
think is the stat I have on that.
249
:So it's like you should learn, really
learn Excel instead of Google Sheets.
250
:Um, and I don't really think like y-
your training should be like, "Oh,
251
:you should learn Power BI 'cause we
created Power BI," type of a thing.
252
:That's just my opinion.
253
:Um, that, that could be fine.
254
:For your first project, try
Microsoft's financial sample workbook.
255
:I think that's gonna be boring.
256
:Investigate which products and
markets drive sales and profits, how
257
:performance changes over time, and
what deserves further investigation.
258
:E-portfolio project should show the
business question and data source.
259
:I agree with that.
260
:Cleaning decisions and quality checks.
261
:I agree with that.
262
:Your queries or calculations.
263
:I agree with that.
264
:Three supported findings.
265
:I mean, three is an arbitrary
number, but some supported findings.
266
:And then five, recommendations
and limitations.
267
:And I do agree with that.
268
:Um, but I think you should have the
recommendations, uh, and the findings up
269
:top as well, like in an intro section.
270
:You should BLUF, bottom line up front.
271
:Because if you're expecting a hiring
manager or recruiter to scroll through or
272
:whatever, you know, go through, "Oh, this
is the business questions and the source
273
:and the queries and all these things,"
before seeing the recommendations and the
274
:findings, that's just not gonna happen.
275
:So get the buy-in from them by having
the findings and recommendations
276
:up front in your portfolio project.
277
:For this deadline, I postpone
Python unless your target
278
:postings consistently require it.
279
:If they do, substitute some second
time, second project time with
280
:Kaggle's Python and Pandas lesson.
281
:Um, I actually think Kaggle's
lessons aren't bad at all, so good
282
:recommendation there, and I also
recommend to postpone Python because
283
:it's only required in 20% of data
jobs, and it's a steep learning curve.
284
:So that's a good advice.
285
:Your 49-Day Readiness Check.
286
:Can you take an unfamiliar dataset, clean
it, query it, verify the numbers, create a
287
:useful chart, and explain a recommendation
without following a tutorial?
288
:Use this as your benchmark
for beginning applications.
289
:Oh, no.
290
:That was so good until
the very end right here.
291
:"Use that as your benchmark
for beginning applications."
292
:No, no, no, no, no,
no, no, no, no, no, no.
293
:If, if you use that as a benchmark for
applications, you're probably never
294
:going to apply for jobs because even me,
who I've been doing this for 10 years
295
:now, of course I can take an unfamiliar
data set, I can clean, I can query,
296
:I can verify the numbers, create a
useful chart, explain a recommendation
297
:without following a tutorial.
298
:I can do that, right?
299
:But it's like, did I do it
the best that I could have?
300
:I don't know.
301
:Like, it, it…
302
:I could always make it better, and I,
I'm never 100% confidence in my actual
303
:analysis unless I've spent months on it.
304
:And like, this is just like such
a bummer line for your confidence.
305
:Like, you're never going to
feel ready to start applying for
306
:applications, so you really shouldn't
have a benchmark for applications.
307
:You should just start applying
for jobs and let the market
308
:tell you the benchmark, right?
309
:Because- The, a hiring manager or
recruiter, you know, if they're trying
310
:to see if you can actually do this, they
can't really tell from your resume or
311
:your LinkedIn or from your application.
312
:They would…
313
:They need to interview you or give you a
case study to see if you can actually take
314
:a data set and analyze it on your own.
315
:They can't really tell if you can or
can't from your resume or your LinkedIn.
316
:So until you get an interview, and you
fail the interview, like the tech portion,
317
:you just failed, or they gave you the
case study and you couldn't do it, I
318
:would not use this as a benchmark at all.
319
:Like, I think you should start
applying for jobs ASAP, and if you
320
:get an interview, that's really good.
321
:If you fail the interview, okay, we
move on and we get another interview,
322
:and we do better from the lessons we
learned from failing the interview.
323
:Um, man, I think this would
keep a lot of people stuck.
324
:Um, so I think that's really bad advice
to, to really make that your benchmark.
325
:And in fact, a lot of junior data analyst
roles, you know, especially the ones
326
:that maybe don't pay amazingly, like
you're not expected to be a senior data
327
:analyst where you take an unfamiliar
data set, you clean it, you query it,
328
:you verify all the numbers, you create
a chart and explain a recommendation.
329
:You might just be a SQL monkey.
330
:You might just write SQL queries
and put that under a report.
331
:Or you might just create useful charts.
332
:Like, you, maybe you don't
need to clean the data set.
333
:Like, it's not…
334
:This is, this is something you need
to be comfortable with eventually,
335
:but not, not before applying for jobs.
336
:Maybe not even before
landing your first job.
337
:Okay?
338
:So I think this is really discouraging,
and, uh, I would be kind of depressed.
339
:And if, if I were following this,
I don't know if I would ever
340
:start applying for, for roles.
341
:Um, that would be, that would
be kind of disappointing.
342
:So overall, I think the advice, it wasn't
terrible, but it didn't like blow me away.
343
:I certainly didn't feel like,
oh my gosh, there's AGI.
344
:It's here.
345
:It's teaching me everything.
346
:I thought it did a good job of
focusing on the right skills,
347
:Excel, SQL, and BI, and saying,
"Don't learn Python or R right now."
348
:But I think in terms of how to
learn them, it was pretty darn
349
:bland and pretty dang boring.
350
:Like, who wants to read
the Excel product manual?
351
:That's not learning, that's reading.
352
:For me, learning is hands-on.
353
:It should be doing.
354
:It should be building something.
355
:Which that actually brings me to my
next point, which is the projects.
356
:I'm glad it mentioned projects and
doing projects, but I don't get
357
:why you have to wait like weeks
before doing your first project.
358
:Why not do it as part of the
learning process, and like do
359
:it earlier if it's so important?
360
:I also didn't think it emphasized
sharing these projects nearly enough.
361
:Like, share them on your resume,
share them on your LinkedIn,
362
:share them on your portfolio.
363
:Just share them with your
neighbor, like with anyone.
364
:It's just, like, a project
isn't really useful if it's not
365
:shared, so I wish it would've
emphasized that a little bit more.
366
:And lastly, I felt like it focused way
too much on learning data analytics
367
:and not becoming a data analyst.
368
:And listen to that again.
369
:Learning data analytics and
becoming a data analyst are not
370
:the same thing, or at least I
don't think they're the same thing.
371
:One is about learning the technical, like,
actual frameworks of analyzation and the
372
:tools to analyze, and the other is more
of like a street smart, hack your way, the
373
:actual grind, the effort of getting a job.
374
:And I'd argue that that one is
actually more important because in the
375
:end, the job is what gets you paid.
376
:And if you don't do the second
one, you don't magically get paid.
377
:Like, if you're the best data analyst
on planet Earth, but you don't
378
:apply for jobs, you don't have a
resume, you're not gonna get paid.
379
:So it did an okay job,
but not a great job.
380
:If you want an excellent, free,
personalized data roadmap just for you
381
:based off of real-life intelligence
and data and first principles,
382
:then go to dataanalystroadmap.com.
383
:You'll fill in a few questions, and
then we will give you a personalized
384
:roadmap that you can literally
follow to land your first data job.
385
:This one's made by real intelligence,
not artificial intelligence.
386
:Remember that
