How do you make data analytics fun and engaging? In this episode, I chat with YouTube sensation Thu Vu. We discuss Python's growing significance, trends in the data job market, plus get a sneak peek into her new initiative, Python for AI Projects.

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⌚ TIMESTAMPS

05:54 - Creating cool projects with Local LLMs

13:48 - Learning and Teaching Python for AI

24:09 - Trends in Data and Tech Job Market

🔗 CONNECT WITH THU VU

🎥 YouTube Channel: https://www.youtube.com/@Thuvu5

🤝 LinkedIn: https://www.linkedin.com/in/thu-hien-vu-3766b174/

📸 Instagram: https://www.instagram.com/thuvu.analytics/

🎵 TikTok: https://www.tiktok.com/@thuvu.datanalytics

💻 Website: https://thuhienvu.com/

Free Data Science & AI tips

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Master Python for AI projects

python-course-earlybird.framer.website

🔗 CONNECT WITH AVERY

🎥 YouTube Channel: https://www.youtube.com/@averysmith

🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/

📸 Instagram: https://instagram.com/datacareerjumpstart

🎵 TikTok: https://www.tiktok.com/@verydata

💻 Website: https://www.datacareerjumpstart.com/

Mentioned in this episode:

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None of the projects that I posted on my channel, I knew

2

beforehand that it would work.

3

It was just sometimes

it's completely absurd.

4

And I thought, yeah, like,

how could I make it work?

5

And then several days, like tinkering

with my code and try to like, look at

6

other tutorials, look up things on Stack

Overflow and see if anyone has any.

7

ever done something like this.

8

Yeah.

9

So it's also a lot of

like findings for me.

10

Sometimes you have to be creative

and solve your own challenge and your

11

own problems because yeah, you always

encounter something in your project.

12

The good mindset is just, uh,

like there's got to be a solution.

13

So don't give up.

14

When you first see an error

or see like a problem.

15

Avery: All right.

16

If you are watching on YouTube or

you've ever looked up data analytics on

17

YouTube, you've probably seen, uh, Tu

Vu, our guest today, one of her videos,

18

because they are absolutely amazing.

19

Uh, in the past, she's been a data

analytics consultant with companies

20

like PWC, uh, and she's a prolific.

21

Content creator in the data analytics

space to welcome to the podcast.

22

Thu Vu: Thanks, Abby.

23

It's, it's really, really

a great introduction.

24

So kind of you.

25

Of course,

26

Avery: it's

27

Thu Vu: my pleasure

28

Avery: to be here.

29

Of course.

30

I'm so glad to have you here.

31

One thing that I love about your videos,

uh, is you do some pretty cool projects.

32

Uh, you do some pretty cool.

33

Things with, with ai, things with

machine learning, things with just data

34

analytics and data science in general.

35

And I think we need more of

that on YouTube, more like

36

actual projects being done.

37

I think you do a great job of doing that.

38

Thu Vu: Yeah, absolutely.

39

I think, um, people talk about data

science or machine learning or AI a

40

lot, but I think what I personally

missed, it was like some kind of

41

like a hands-on demonstration of how

you're gonna use a new technology,

42

let's say, uh, like a, a network.

43

analytics or some AI, some cool AI

models or large language models, how

44

you can apply it to your own problem

and also demonstrate it like end to end

45

almost, um, how you start with an idea,

how you get, um, inspired, how, uh, how

46

you think about the problem, how you

frame it and how you Kind of go step by

47

step, explore it further and further.

48

And then in the end you have something

that you can show to other people.

49

And hopefully it's a little bit useful

and um, yeah, hopefully you have fun.

50

So that is kind of the idea that I kind

of like, yeah, it was not, The first

51

thing that I, um, actually started

when I, um, yeah, when I started

52

making videos on YouTube, um, it just

occurred to me that people really

53

liked it and people really appreciate

the effort to think something so much,

54

uh, yeah, think through, uh, some, a

particular topic in such a great detail.

55

Also kind of hopefully inspire other

use cases for people to try out.

56

Yeah, so that, that was

kind of like the motivation.

57

And up until now, that's kind

of like, that is the type of

58

video that I really like making,

although it takes a lot of effort.

59

And of course, like I lost

some hair because of it, but

60

yeah, it was fun and hopefully

helpful for other people as well.

61

Avery: I think so.

62

And I think it's very impressive because

it's not easy to make technical videos and

63

make technical videos engaging for like

a YouTube audience, which, uh, I think a

64

lot of people watch a video for like 35

seconds and then skip to the next one.

65

So your, your ability to, to kind of

capture these technical things in a, you

66

know, shorter video, but not like too

short, uh, I think is very impressive.

67

Uh, have you always enjoyed

making fun projects like this?

68

Thu Vu: I think at the beginning,

it was kind of a struggle.

69

I think it was like, yeah, I think without

a lot of, like, experience with, uh,

70

you know, like making tutorials, it was

also kind of like always like climbing

71

such a, like a big mountain every time.

72

Like how you talk through everything,

how you explain everything, every

73

little step that you make, uh, making

the, like the screen recording and also

74

kind of like, So kind of like nice B

rolls, you know, like in YouTube, you

75

have like all these kind of like fun

thing that you film yourself and then

76

combine it in, in like a good storyline.

77

I think that that was kind

of a struggle for me in data

78

science or machine learning.

79

You can always find some kind

of like a project in terms of a

80

blog post or, um, like a Jupyter

notebook or a GitHub repository.

81

And that, um, yeah, that's usually

how people think about these projects.

82

But like, how you present it in a video in

an engaging way, I think it was like the,

83

the biggest, yeah, it was a challenge.

84

At the beginning I was scared.

85

I was totally like, I, I didn't really,

I could not really enjoy filming myself

86

and like make such a complex explanation.

87

I was kind of uncomfortable with it.

88

At the start, but it

got better and better.

89

I know how to kind of like prepare

my scripts, how to kind of like,

90

think about it, maybe a few days,

come up, come back to the project

91

and refine what I want to tell.

92

And, uh, it, the workflow gets better.

93

Better and better.

94

Uh, so it's also less scary and I'm

also not a native English speaker.

95

So sometimes there are a lot

of concepts I want to explain,

96

uh, and I like words for it.

97

I love the way to, to explain it.

98

And that's also frustrated.

99

Uh, but yeah, it's, uh, yeah, all of

these struggles, I think it's kind of

100

like, it's worth the effort for me and I

think it's really rewarding to, to create.

101

That's kind of my, uh, like

how, how it went for me.

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Avery: It's, it's really impressive,

especially, especially like

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in a, in a second or, or how

many languages do you speak?

104

Thu Vu: I speak, uh, Vietnamese as a,

uh, as a mother tongue and my, uh, yeah,

105

definitely the next language is English.

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And I also speak Dutch because

I've been living in the

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Netherlands for the last 10 years.

108

That's so

109

Avery: impressive.

110

That's so impressive to be creating this

good of content in your second language.

111

Now let's talk about a little

bit more about projects.

112

So one of the cool things that you've

done is you've built a project to analyze

113

your finances, uh, with like a local LLMs.

114

Like you basically created your,

your own version of like chat GPT to

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specifically look at your finances.

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Now that's crazy because.

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Like I think most people would

probably just like do something

118

easier to do that, but you're

like, no, I want to make it cool.

119

I want to make it hard.

120

Have you always been interested

in like creating kind of cool

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personal pet projects like this?

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Yeah.

123

Yeah.

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Thu Vu: Yeah.

125

Thanks for pointing it out.

126

I think that's also kind of like one

of the projects that I got some really,

127

like some people saying on YouTube.

128

Oh, like you, you just invented something

that was completely unnecessary because

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like on the, like the bank banking app

that you are using, probably you also have

130

kind of the insights feature where you

can also have the same, do the same thing.

131

But yeah, yeah, I know about that

feature, but I was like, huh, how can

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I use, uh, an LLM to help me with this?

133

And because I download my bank statements

all the time, uh, and I like in.

134

Yeah, I just like looking at

it myself and see in detail

135

what kind of expenses I make.

136

Yeah, so that was the

start of the challenge.

137

And, uh, yeah, that video got a lot

of, uh, nice, um, uh, yeah, nice

138

feedback because I think people

really like to have feedback.

139

Something that is a bit private,

like, uh, when you have an LLM

140

and you, uh, you cannot post your

blank statements on ChatGBT or

141

Cloud AI to help you analyze it.

142

So yeah, like using a local LLM is

a great way to kind of like test out

143

this idea and see how well it works.

144

it might work.

145

And I also have, yeah, it was

like a, like a trial and error.

146

I also didn't know if it would

work, um, at the beginning.

147

I, I think, yeah, none of the projects

that I posted on my channel, I

148

knew beforehand that it would work.

149

It was just sometimes

it's completely absurd.

150

And I thought, yeah, like,

how could I make it work?

151

Uh, and then several days, like

tinkering with my code and try

152

to like, look at other tutorials.

153

tutorials, uh, look up, uh, things

on Stack Overflow and see if anyone

154

has ever done something like this.

155

Yeah.

156

So it's also a lot of

like findings for me.

157

Sometimes you have to be

creative and solve your own

158

challenge and your own problems.

159

Um, because yeah, you always

encounter something, uh, in your

160

project and, um, the good mindset

is just, uh, like there's got to be.

161

And a solution.

162

So don't give up when you first

see an error or see like a problem.

163

And that project definitely didn't work

well at the beginning because I know

164

like the LLM was was really unreliable.

165

So I had to like change

the temperature for the.

166

LLM, and then tweak something in the

workflow and try to validate the output of

167

the LLM with Pydantic and all these kind

of things, uh, just for a toy project.

168

So I was like, yeah, it was a lot of work.

169

Um, but it was, it was fun.

170

Um, yeah.

171

Yeah, I think nowadays there are so

many new frameworks that help you do

172

these kind of projects, maybe like

in an easier way, or like, um, Python

173

packages that you can use, I think,

like, instruct, um, like some new

174

Python packages that lets you output

things from LLM in a structured format.

175

That is something that I only

knew later, but that was much

176

later after I posted that project.

177

Avery: Yeah, I think there's so many

good things that people can take from,

178

from what you just said, because I think

oftentimes people, you know, look, look at

179

you and maybe look at me and they're like,

Oh, these people are experts with data.

180

They know what they're doing.

181

And the truth is that no one

actually really knows a hundred

182

percent what they're doing in data

ever because it's ever evolving.

183

It's ever expanding.

184

Uh, there's always new things.

185

And.

186

Uh, you know, I, I can't, I mean, I

guess I can speak for you cause you

187

just mentioned this, but like, uh, I

get stuck all the time and it's still

188

like a process to, to troubleshoot

and to, like you said, use chat GPT to

189

try to solve or go to stack overflow

and, and get through those problems.

190

So even though you're creating these

videos, you've done a lot of them,

191

you have like, like a decade of

experience, you're still getting stuck

192

and you still have to troubleshoot.

193

Thu Vu: Yeah, exactly.

194

No, I, I think anyone can

do this kind of projects.

195

Um, yeah, given that you put

in the time and put a little

196

bit effort and some patience.

197

Um, and I've seen also a

really, really cool project on

198

your, on your channel as well.

199

And I, I thought like you really put a lot

of attention to all these details on the

200

videos or visualizations that you've made.

201

I thought like, yeah, it's really.

202

Really cool.

203

Yeah.

204

Maybe something sometimes I just thought,

okay, I can create this visualization

205

of like, um, you know, animating

something just, just for the fun of it.

206

Uh, and you did it sometimes.

207

And I thought, Oh, like you have some

really, uh, really great, uh, insights

208

on how you can show things differently.

209

And so I think, yeah, like, yeah, with

making YouTube, it's, it's really fun

210

to look at other people's, um, work

and see how you can learn from them.

211

And yeah.

212

And I guess for many people as well,

uh, in the audience, yeah, you can

213

definitely just like sometimes come

across something on YouTube and then you

214

thought, huh, I can maybe do this as well.

215

Um, yeah, so that's a great way

to learn from each other as well.

216

And it's definitely, I'm not a,

yeah, know it all kind of person,

217

definitely in data science or

218

Avery: machine learning.

219

Well, you definitely know a lot and people

can learn from you, uh, a lot and, and

220

yeah, I've made some project videos.

221

In the past.

222

I haven't made any

project videos recently.

223

I find them that it appears

that the YouTube audience

224

doesn't like them as much.

225

I know I've, I've spoken to

Luke Bruce in the past as well.

226

And he has like this awesome

video on his channel where he was

227

analyzing his mountain bike data.

228

And I was like, that video rocks.

229

And he's like, I know, right.

230

It should have way more views.

231

And I don't know.

232

It's, it's, it, I think you did

a great job of, of making it

233

digestible for the YouTube audience.

234

Cause I think these personal projects

where Like, for example, I, I looked

235

at like what states Google the most

every single hour for like a quarter

236

of a year, you know, you've done this

analyzing my financial data with an LLM.

237

Luke's done the mountain biking one.

238

I think these projects are fun.

239

And I think, I think it's as data

scientists or data analysts, like we

240

want to use data in our real normal life,

not just in our work or our business.

241

So these types of like personal

projects, I think can be really fun.

242

Thu Vu: Yeah, yeah, absolutely.

243

Um, no, I think, I think you're right

that, uh, not all the videos that

244

you make or all the work that you

make would, uh, get the recognition

245

that you think it deserves.

246

Yeah, it's, um, it's, it's hard

and, uh, I'm sure some people

247

also post things on LinkedIn.

248

I also have some friends who, uh,

post, um, try to post on LinkedIn more

249

often, but really like doesn't get

much views or, uh, like interaction.

250

And then.

251

Yeah.

252

You feel discouraged.

253

Um, and I'm sure a lot of

people also relate to that.

254

I also don't know how some videos

got, uh, seen and some people, some

255

videos just got completely tanked

and no one really look at it ever.

256

It's, it's really hard to, to, to kind

of like predict that even though, yeah, I

257

really want to predict it, but yeah, yeah.

258

I think there's some kind of secrets.

259

I tried to make the first, um,

like the opening of the video.

260

really engaging.

261

Like the third, the first 30 seconds or

so, uh, that's what I learned from all

262

the YouTube gurus and, uh, try to kind of

like make the best edits out of it, uh,

263

and see if people keep watching longer.

264

And usually they do, but overall the

quality of the whole project is the

265

video is, is, is more important, I guess.

266

And I hope that is,

267

Avery: that's true.

268

It's, it's definitely hard.

269

Um, let's talk about, so like with

these cool projects that we, that we've

270

been talking about, if people want

to build their own, obviously they

271

can kind of look at the stuff, you

know, you and I have done on YouTube.

272

Um, I know that you give you your GitHub,

uh, in the description a lot of time,

273

uh, for most of my projects, you can

get all of the GitHub stuff for free.

274

It's kind of like open source like that.

275

But you also just recently

created something called Python

276

for AI projects, which basically.

277

is an opportunity, a platform where

people can, you know, build some

278

pretty cool projects for an AI with

Python, kind of with your guidance.

279

Is that right?

280

Thu Vu: Yeah.

281

Yeah, that's right.

282

Um, it's kind of like my, uh, really

my, I put my heart into this project

283

because I believe that many people

struggle to learn the basics of Python.

284

Python and AI, because they don't

have the really like the most

285

beginner friendly kind of, uh,

guidance or kind of like a road map.

286

Um, so that's why I decided to create

this, uh, this giant kind of like, uh,

287

curriculum that teach people Python from

scratch, all the fundamentals, and also

288

hands on stuff on how to learn, how to,

how to use Visual Studio Code, how to

289

use AI assistance for your work, and also

learn the basics of machine learning,

290

deep learning, and AI, and with some

project walkthroughs as well for people

291

to really follow and create their own

projects with kind of like the idea.

292

Yeah, using the, uh, large language

models and kind of like, uh, yeah,

293

just like some projects I did also on

my YouTube channel, um, one projects

294

about extracting information, uh, from

PDFs using, uh, large language models

295

and how you structure it in a nice

format in a nice, uh, table format.

296

And also, yeah, I'm also planning to

add a few more advanced projects like

297

fine tuning and LLM and all these things

that, yeah, I also kind of like, I really

298

always wanted to do it also myself.

299

Now it's also an opportunity to kind

of like explore it further and help

300

other people to learn them as well.

301

Avery: Very cool.

302

I, I might have to check that out because

yeah, I definitely, I don't know how to

303

do anything with like an LLM from scratch.

304

So there's some, there's probably some

things in there, uh, that I could learn.

305

So that would be a lot of fun.

306

We'll have the, uh, the link

in the show notes down below.

307

Thu Vu: Yeah, definitely.

308

Yeah.

309

No, I, I think, uh, well, you, you, you

know, Python and you, yeah, probably

310

you can pick it up very quickly and,

uh, all the machine learning AI stuff.

311

Uh, I teach someone as really like

someone who has never worked, never

312

built a machine learning model before.

313

Try to teach the, like the fundamentals

and the building blocks for you

314

probably is, is much less relevant.

315

Um, but yeah.

316

That's, uh, indeed.

317

Yeah.

318

That's kind of my project

319

Avery: at the moment.

320

Very cool.

321

I can tell that you're, you're

really excited and passionate about

322

it, uh, which I think is very cool.

323

Um, we've talked a lot about projects

and, and your, your great YouTube

324

channel, and I've kind of given a

little bit of your background, but,

325

uh, I'm guessing a lot of people

listening don't a hundred percent know.

326

Uh, your, your background.

327

So could you just tell us like what

you studied in school and then maybe

328

what your first job was out of school?

329

Thu Vu: Uh, yeah, yeah.

330

Thanks for, for asking about this.

331

I think, uh, yeah, I, I also

haven't really shared about

332

it a lot on my channel.

333

Um, my, about my background.

334

So if

335

Avery: you don't want to talk about

it, we don't have to just so you know.

336

Thu Vu: Oh no, no, no.

337

Of course.

338

Uh, no, of course I can talk about it.

339

Yeah,

340

Avery: so don't need

341

Thu Vu: to add it in an edit.

342

Um, yeah, so I, uh, yeah, so

my first, uh, degree that, um,

343

at school is, uh, economics.

344

And, um, back then I was, yeah,

I was still living in Vietnam.

345

Um, and I got my bachelor in economics and

then I go to, I went to the Netherlands

346

to study a master, uh, in economics.

347

as well.

348

So this is, yeah, it was kind of

like a very, uh, theoretical degree.

349

And, uh, although, yeah, you learn

some basic stuff, econometrics,

350

um, linear regression, which

were like basic statistics.

351

And that was quite useful later on as a,

like a, when I would start working as a

352

data analyst and then learning a bit more

data science y stuff, machine learning.

353

And that was in 2015.

354

So I moved to the Netherlands when I was

Yeah, around, yeah, 22 ish, um, back then.

355

I started working in the Netherlands

and stayed, um, decided to stay,

356

uh, even though it was really

not the first plan, uh, when I

357

moved to the Netherlands to study.

358

Um, but, yeah.

359

I found out that it was like

really, really a great country.

360

And I fell in love with the culture,

the food, not so much the weather,

361

not so much, but people were great.

362

The working environment was really, really

transparent, really nice, very efficient.

363

And also people are very direct.

364

And I really liked the way that,

you know, people are honest to each

365

other and, um, and, uh, Yeah, you,

they give you really straightforward

366

feedback that you can improve on.

367

When I start my internship, uh, after

right after my master's, I felt really, I

368

felt really good about, uh, working here.

369

Um, so that's how I

started my career actually.

370

And I started working as a

data, kind of like a research

371

assistant in my internship.

372

And then later I got a job

offer as a data analyst.

373

So I got really lucky to, you know,

Actually start in this career because at

374

the beginning when I learned economics,

the thing that I would think about was

375

more like a researcher or maybe working

in policy, working in maybe a little

376

bit like even started a PhD and that

I even applied for a PhD in Amsterdam.

377

I still remember.

378

And thank God I didn't, I didn't got it.

379

I didn't get it.

380

, uh, otherwise I would be like, I

don't know where I would be right now.

381

And yeah.

382

And a few, yeah, a few years later I

start working at BWC, um, Pricewaterhouse

383

Coopers and I start working as

a, a consultant for six months.

384

Six years.

385

There, I also learned a lot of

different, uh, new things and worked

386

for different, in different projects

and I found it really incredibly,

387

um, really, uh, I learned so much.

388

Um, it was really helpful to work

with so many different people and

389

you pick up new things every time.

390

Yeah, and that when I was working there

at BWC, I decided to learn a bachelor.

391

degree in computer science, I

decided to take it because I feel

392

like I still missed something.

393

I, my technical skills were still kind

of like not so, I was not so confident.

394

I was, I was still like, yeah, I was

probably, you know, like an imposter

395

feeling and also the drive to learn more

in a more kind of like structured way.

396

Um, that's how I decided

to take the degree.

397

It's an online degree that you

can take via Coursera, actually.

398

It's very nice.

399

Yeah, it's a, it was a

lot of work, actually.

400

It was a master, um, bachelor degree

with, I don't know, 22 modules.

401

And yeah, I still have the final

project that I have to finish.

402

Um, so it was in the end, it was like

six years now that I haven't finished.

403

So I still feel ashamed when I talk about

it, but yeah, uh, in the end, yeah, I

404

work on, uh, the YouTube channel a lot

and, uh, it was all kind of like all

405

go into each other, uh, kind of, yeah.

406

So that's kind of like my, my, my,

work and my personal history and

407

like my, uh, yeah, my story so far.

408

Avery: Can you just, can you just

submit a URL to, of your YouTube

409

channel to the degree and just

be like, here's my final project.

410

Thu Vu: Yeah.

411

Yeah.

412

Yeah.

413

Like for like my own project or

414

Avery: just like your whole,

your whole YouTube channel.

415

I feel like that should

count as your final project.

416

I feel like they should, they should,

uh, give you, give you credit for

417

that because you've done some,

some pretty cool things on there.

418

Thu Vu: Yeah, that's a great idea.

419

I will, I will try it out.

420

Avery: That's, that's great.

421

Yeah, I think it's, I think it's one

thing that's, uh, I want to just pull from

422

your, your story there, uh, was you going

back to school once you had a data job.

423

And one of the things I try to, I,

I try to help people who are like

424

brand new to data and who like

want to become a data analyst.

425

And obviously going to back back

to school is always an option.

426

Um, but a lot of the times if you get

your foot in the door first with any

427

sort of data job at the beginning, it's

going to be so much easier to go back

428

to school for a variety of reasons.

429

And so like a lot of the cool things

that you do, like, like the LLM

430

stuff, uh, use Docker in that video.

431

A lot of that stuff is, is things

that you don't necessarily need

432

when you first land your data job,

but, but they can help you become a

433

better data analyst down the road.

434

And so I kind of like how you, you kind

of gotten your foot in the data door

435

with, with the data stuff you had from

your economics degree, and then you, you

436

upscaled after you were already there.

437

So that way you can, you can become a data

analyst or sorry, become a better data

438

analyst, you know, have a bigger impact

at your company, uh, hopefully get, get

439

compensated more and better because of it.

440

Um, but I love that you did that.

441

After you get you started, basically,

442

Thu Vu: yeah, yeah, definitely.

443

And, and I think this is,

uh, you're totally right.

444

It's so much easier when you get an

internship or you get a, like a really

445

beginner, uh, like an entry level

job in data science or data analysis.

446

Even like us, just a small, uh, portion

of your job is, uh, data related.

447

You can always like show it a little

bit more that you have some experience.

448

And this is really a big advantage.

449

So, yeah, I would always advise anyone

to, when they start, just think, uh, step

450

by step and, uh, take anything that you

may find, like, you can learn something,

451

um, regarding the data skills, and then

you can go, uh, can move on from there.

452

That's so much easier, indeed.

453

Avery: One of, one of your latest videos,

you explored data trends, um, and you

454

found some pretty interesting things,

uh, that was going on with the data

455

job market, the tech market in general,

what was like your favorite trend that

456

you kind of discovered in this video?

457

Thu Vu: Yeah.

458

Yeah.

459

I think the favorite trend for me is like.

460

The new development, when you think

about like technical skills, I find

461

that like Python is, has been really

so become so much more ubiquitous.

462

So, so much more universal

compared to a few years ago.

463

I think definitely a few years ago, it

was like, uh, for the discord analysis or

464

some particular software, like SAS, even

if you ever, uh, even ever used to use it.

465

But right now, also within my work,

a lot of, in a lot of projects,

466

we are migrating all the code

base from SAS, from R to Python.

467

So it was like a nice.

468

An interesting observation, and especially

with the development of AI right now,

469

Python is supporting a lot of cool tools.

470

For example, like, um, uh, things like

lang chain and all these different

471

frameworks to create, um, an AI

powered application, an AI agent, all

472

these frameworks are all in Python.

473

Yeah, that, that, that's really

like a Uh, yeah, like a really cool

474

thing to, to, to, um, to recognize.

475

Further, I think there's also some

interesting trends that I noticed, um,

476

in kind of like the freelancing space.

477

It seems, it seems like, They're

more freelancing jobs than right

478

now, than, than a few years ago.

479

And I'm not sure why, but I feel like

companies are more like, probably they

480

are experimenting with things a lot.

481

And that's why you see.

482

Probably some of them have a little

bit budget, uh, or even individuals

483

or small business owners, they have

a little bit budget and they want to

484

hire someone to do something for them.

485

I recently have a friend who worked a

lot on, um, uh, who knows a lot on RAC,

486

so, um, uh, retrieval, uh, augmented

generation kind of projects using LLMs.

487

And then, um, yeah, so that

person connects it to me.

488

Uh, asking, like, do you have someone

or you can help me with, uh, uh,

489

building and, uh, kind of like a tool to

extract this and that information from

490

like, uh, a hundred PDFs that he has.

491

And so, yeah, so I introduced my friend

to, uh, to that, um, to that person to,

492

uh, to help him with, with this task.

493

And I think this is also kind of like an

example of like how people are recognizing

494

the role of, uh, AI and automation.

495

And they want to get some, something done.

496

And so, yeah, it doesn't need to

be a fixed contract, a fixed job.

497

It's more like a experiment sometimes.

498

And so, yeah, it's a, I find it

also really interesting and I keep

499

thinking about how, uh, how people

can find these kind of projects.

500

Uh, they can, yeah, like people who

need to get things done and people who

501

has the skill, how can they, uh, meet

each other more often or how they can

502

more effectively, uh, meet it, uh,

like kind of like, um, come across each

503

other's, uh, and connect to each other.

504

Yeah, that there are the two trends

that I, yeah, that I really like.

505

And I also kind of like got a bit

surprised, but also not so surprised,

506

uh, how, how that, how, yeah.

507

Avery: It's fascinating.

508

We live in a really exciting

time where you can start a side

509

hustle or start your own business.

510

That's, that's what I did three

years ago, three and a half years

511

ago was I started to freelance and I

started to make more money freelancing

512

than I did in my regular job.

513

And I was like, okay, I'm

just going to do that.

514

Oh, really?

515

Yeah.

516

Yeah.

517

That's why I left Exxon was to start doing

freelance projects and start an agency.

518

Yeah.

519

I ended up switching mostly

to teaching because I figured

520

out I really enjoy teaching.

521

So that's what I do.

522

Full time now pretty much.

523

Um, but yeah, the freelancing

stuff is super fascinating and I

524

think there's a great opportunity,

uh, for people to get into that.

525

Uh, and I also love that you, you

brought up the, the Python trend.

526

I think it just became, you

had mentioned that video.

527

It just became the most common or

most frequently used language on

528

GitHub, uh, which was a big deal.

529

Um, so Python, definitely

a thing of the future.

530

Another trend, uh, that I really

liked, especially since I helped

531

people land their first day at

a job is you looked at like.

532

The number of data jobs

over the last few years.

533

And if like, we've seen a lot of layoffs

or if we've seen a decrease in jobs,

534

because you know, a lot of people are

like, Oh, the economy kind of stinks.

535

And you know, the job

market's really bad right now.

536

Uh, and your conclusion was, you know,

maybe it's not as bad as people might say.

537

The, the, the graph was kind of a little

bit downward in terms of like number

538

of jobs, but it was relatively flat.

539

And that's actually, I did.

540

Yeah.

541

Uh, a similar video recently where I

looked at, um, the growth of, of data

542

jobs from a different data source and the

data source you used and basically came

543

to the same conclusion that the, if you

544

were up specifically for like data

analysts were still up around like 20%.

545

But it was year over year,

but it was a flat 20 percent

546

for like the last year or two.

547

So I was really comforted to

see, like, you kind of came to a

548

similar conclusion that I did with

a totally different, uh, data set,

549

completely independent of each other.

550

Thu Vu: Oh, that's really cool.

551

Um, that that's really cool to see.

552

Indeed.

553

Um, when I was using, yeah, I

actually use the, uh, kind of like

554

the data from, um, from, from, uh,

collected by Luke, uh, Luke Burrus.

555

And, uh, yeah, he's the man behind

all this, like, web scraping stuff.

556

And, uh, I also, I was also a bit

doubting, uh, I didn't want to

557

make a conclusion that, oh, this is

like decreasing that we are seeing.

558

Indeed, it's more like flattened out

and, uh, depending on how you see it.

559

Um, and as you say, it's more like

a, uh, glass half full or empty.

560

You, yeah, like it's quite, I

think it's quite normal to see some

561

fluctuation over the year over year.

562

And, uh, it's, uh, it's definitely,

yeah, I don't think it's something that

563

I would worry about, but more like,

uh, what kind of jobs are being posted?

564

Like, the job compositions are changing

rather than the number of jobs.

565

I think.

566

Probably within the same job

title, you probably have something

567

new in the job descriptions.

568

And I, I didn't, um, really have the

chance to really dive into that in,

569

in that, um, data job trend video.

570

But I think it would be really cool to

see how the, uh, the job functions or

571

the job, uh, description is changing.

572

And how you can maybe learn from that.

573

What can you prepare to meet

that demand in the future?

574

I'm sure there will be more like,

uh, really things that are more

575

like, uh, data AI engineering kind

of role that are emerging in data

576

science, in the, like, data science,

uh, Uh, machine learning space.

577

And so, yeah, I, I think, uh, yeah,

it's probably like, it's better

578

to, to, to, um, a little bit, put

a little bit like, uh, yeah, yeah.

579

Take that with a little bit grain of salt.

580

When you look at the chart, um, probably

it doesn't really tell the full story.

581

Avery: I agree.

582

It's, it's, it would

be really interesting.

583

That data sets very rich.

584

Um, but once you get into text

analysis and NLP, you just have

585

to have more data science skills.

586

It's like a whole separate.

587

Part of data science, which just

takes longer to do than things like

588

counting and line charts and, uh,

bar charts and stuff like that.

589

Um, right, right.

590

Definitely.

591

Which, which maybe it's a, it's a

great project, uh, for your Python

592

for AI projects, uh, group that

you're doing with, with the course.

593

So maybe that we'll look

forward to seeing that.

594

On the curriculum in the future to thank

you so much for being on the podcast.

595

If you guys haven't checked out

her channel, please go do so.

596

Now we'll have a link to it in the

show notes down below, as well as

597

her Python for AI projects too.

598

Thank you so much for being on the show.

599

Thu Vu: Yeah.

600

Thank you so much for having me here.

601

I agree.

602

And yeah, it was a great pleasure

to meet you here on this podcast.

603

Avery: Same.