144: Why Should You Build Projects as a Data Analyst (Thu Vu’s Story)

Thu Vu: 00:00:00

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

2

: 00:00:03

beforehand that it would work.

3

: 00:00:06

It was just sometimes

it's completely absurd.

4

: 00:00:08

And I thought, yeah, like,

how could I make it work?

5

: 00:00:11

And then several days, like tinkering

with my code and try to like, look at

6

: 00:00:15

other tutorials, look up things on Stack

Overflow and see if anyone has any.

7

: 00:00:20

ever done something like this.

8

: 00:00:21

Yeah.

9

: 00:00:22

So it's also a lot of

like findings for me.

10

: 00:00:24

Sometimes you have to be creative

and solve your own challenge and your

11

: 00:00:28

own problems because yeah, you always

encounter something in your project.

12

: 00:00:33

The good mindset is just, uh,

like there's got to be a solution.

13

: 00:00:37

So don't give up.

14

: 00:00:38

When you first see an error

or see like a problem.

15

: 00:00:42

Avery: All right.

16

: 00:00:42

If you are watching on YouTube or

you've ever looked up data analytics on

17

: 00:00:46

YouTube, you've probably seen, uh, Tu

Vu, our guest today, one of her videos,

18

: 00:00:50

because they are absolutely amazing.

19

: 00:00:52

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

analytics consultant with companies

20

: 00:00:56

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

21

: 00:00:58

Content creator in the data analytics

space to welcome to the podcast.

22

: 00:01:03

Thu Vu: Thanks, Abby.

23

: 00:01:04

It's, it's really, really

a great introduction.

24

: 00:01:06

So kind of you.

25

: 00:01:07

Of course,

26

: 00:01:08

Avery: it's

27

: 00:01:08

Thu Vu: my pleasure

28

: 00:01:09

Avery: to be here.

29

: 00:01:09

Of course.

30

: 00:01:10

I'm so glad to have you here.

31

: 00:01:11

One thing that I love about your videos,

uh, is you do some pretty cool projects.

32

: 00:01:16

Uh, you do some pretty cool.

33

: 00:01:18

Things with, with ai, things with

machine learning, things with just data

34

: 00:01:22

analytics and data science in general.

35

: 00:01:23

And I think we need more of

that on YouTube, more like

36

: 00:01:26

actual projects being done.

37

: 00:01:27

I think you do a great job of doing that.

38

: 00:01:29

Thu Vu: Yeah, absolutely.

39

: 00:01:30

I think, um, people talk about data

science or machine learning or AI a

40

: 00:01:34

lot, but I think what I personally

missed, it was like some kind of

41

: 00:01:38

like a hands-on demonstration of how

you're gonna use a new technology,

42

: 00:01:43

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

43

: 00:01:46

analytics or some AI, some cool AI

models or large language models, how

44

: 00:01:52

you can apply it to your own problem

and also demonstrate it like end to end

45

: 00:01:57

almost, um, how you start with an idea,

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

46

: 00:02:03

you think about the problem, how you

frame it and how you Kind of go step by

47

: 00:02:07

step, explore it further and further.

48

: 00:02:10

And then in the end you have something

that you can show to other people.

49

: 00:02:13

And hopefully it's a little bit useful

and um, yeah, hopefully you have fun.

50

: 00:02:18

So that is kind of the idea that I kind

of like, yeah, it was not, The first

51

: 00:02:25

thing that I, um, actually started

when I, um, yeah, when I started

52

: 00:02:29

making videos on YouTube, um, it just

occurred to me that people really

53

: 00:02:33

liked it and people really appreciate

the effort to think something so much,

54

: 00:02:38

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

particular topic in such a great detail.

55

: 00:02:45

Also kind of hopefully inspire other

use cases for people to try out.

56

: 00:02:50

Yeah, so that, that was

kind of like the motivation.

57

: 00:02:54

And up until now, that's kind

of like, that is the type of

58

: 00:02:57

video that I really like making,

although it takes a lot of effort.

59

: 00:03:00

And of course, like I lost

some hair because of it, but

60

: 00:03:03

yeah, it was fun and hopefully

helpful for other people as well.

61

: 00:03:08

Avery: I think so.

62

: 00:03:08

And I think it's very impressive because

it's not easy to make technical videos and

63

: 00:03:13

make technical videos engaging for like

a YouTube audience, which, uh, I think a

64

: 00:03:17

lot of people watch a video for like 35

seconds and then skip to the next one.

65

: 00:03:21

So your, your ability to, to kind of

capture these technical things in a, you

66

: 00:03:26

know, shorter video, but not like too

short, uh, I think is very impressive.

67

: 00:03:29

Uh, have you always enjoyed

making fun projects like this?

68

: 00:03:33

Thu Vu: I think at the beginning,

it was kind of a struggle.

69

: 00:03:35

I think it was like, yeah, I think without

a lot of, like, experience with, uh,

70

: 00:03:40

you know, like making tutorials, it was

also kind of like always like climbing

71

: 00:03:44

such a, like a big mountain every time.

72

: 00:03:46

Like how you talk through everything,

how you explain everything, every

73

: 00:03:50

little step that you make, uh, making

the, like the screen recording and also

74

: 00:03:55

kind of like, So kind of like nice B

rolls, you know, like in YouTube, you

75

: 00:03:58

have like all these kind of like fun

thing that you film yourself and then

76

: 00:04:03

combine it in, in like a good storyline.

77

: 00:04:05

I think that that was kind

of a struggle for me in data

78

: 00:04:09

science or machine learning.

79

: 00:04:10

You can always find some kind

of like a project in terms of a

80

: 00:04:13

blog post or, um, like a Jupyter

notebook or a GitHub repository.

81

: 00:04:18

And that, um, yeah, that's usually

how people think about these projects.

82

: 00:04:22

But like, how you present it in a video in

an engaging way, I think it was like the,

83

: 00:04:27

the biggest, yeah, it was a challenge.

84

: 00:04:30

At the beginning I was scared.

85

: 00:04:32

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

I could not really enjoy filming myself

86

: 00:04:38

and like make such a complex explanation.

87

: 00:04:42

I was kind of uncomfortable with it.

88

: 00:04:44

At the start, but it

got better and better.

89

: 00:04:46

I know how to kind of like prepare

my scripts, how to kind of like,

90

: 00:04:50

think about it, maybe a few days,

come up, come back to the project

91

: 00:04:54

and refine what I want to tell.

92

: 00:04:57

And, uh, it, the workflow gets better.

93

: 00:04:59

Better and better.

94

: 00:05:01

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

also not a native English speaker.

95

: 00:05:06

So sometimes there are a lot

of concepts I want to explain,

96

: 00:05:09

uh, and I like words for it.

97

: 00:05:11

I love the way to, to explain it.

98

: 00:05:13

And that's also frustrated.

99

: 00:05:15

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

these struggles, I think it's kind of

100

: 00:05:19

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

think it's really rewarding to, to create.

101

: 00:05:24

That's kind of my, uh, like

how, how it went for me.

102

: 00:05:27

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

especially, especially like

103

: 00:05:30

in a, in a second or, or how

many languages do you speak?

104

: 00:05:33

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

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

105

: 00:05:40

definitely the next language is English.

106

: 00:05:43

And I also speak Dutch because

I've been living in the

107

: 00:05:46

Netherlands for the last 10 years.

108

: 00:05:49

That's so

109

: 00:05:50

Avery: impressive.

110

: 00:05:50

That's so impressive to be creating this

good of content in your second language.

111

: 00:05:54

Now let's talk about a little

bit more about projects.

112

: 00:05:57

So one of the cool things that you've

done is you've built a project to analyze

113

: 00:06:02

your finances, uh, with like a local LLMs.

114

: 00:06:05

Like you basically created your,

your own version of like chat GPT to

115

: 00:06:09

specifically look at your finances.

116

: 00:06:12

Now that's crazy because.

117

: 00:06:13

Like I think most people would

probably just like do something

118

: 00:06:16

easier to do that, but you're

like, no, I want to make it cool.

119

: 00:06:19

I want to make it hard.

120

: 00:06:20

Have you always been interested

in like creating kind of cool

121

: 00:06:22

personal pet projects like this?

122

: 00:06:24

Yeah.

123

: 00:06:25

Yeah.

124

: 00:06:25

Thu Vu: Yeah.

125

: 00:06:26

Thanks for pointing it out.

126

: 00:06:27

I think that's also kind of like one

of the projects that I got some really,

127

: 00:06:31

like some people saying on YouTube.

128

: 00:06:33

Oh, like you, you just invented something

that was completely unnecessary because

129

: 00:06:38

like on the, like the bank banking app

that you are using, probably you also have

130

: 00:06:42

kind of the insights feature where you

can also have the same, do the same thing.

131

: 00:06:47

But yeah, yeah, I know about that

feature, but I was like, huh, how can

132

: 00:06:51

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

133

: 00:06:54

And because I download my bank statements

all the time, uh, and I like in.

134

: 00:06:58

Yeah, I just like looking at

it myself and see in detail

135

: 00:07:02

what kind of expenses I make.

136

: 00:07:04

Yeah, so that was the

start of the challenge.

137

: 00:07:07

And, uh, yeah, that video got a lot

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

138

: 00:07:13

feedback because I think people

really like to have feedback.

139

: 00:07:16

Something that is a bit private,

like, uh, when you have an LLM

140

: 00:07:20

and you, uh, you cannot post your

blank statements on ChatGBT or

141

: 00:07:25

Cloud AI to help you analyze it.

142

: 00:07:28

So yeah, like using a local LLM is

a great way to kind of like test out

143

: 00:07:32

this idea and see how well it works.

144

: 00:07:35

it might work.

145

: 00:07:36

And I also have, yeah, it was

like a, like a trial and error.

146

: 00:07:40

I also didn't know if it would

work, um, at the beginning.

147

: 00:07:44

I, I think, yeah, none of the projects

that I posted on my channel, I

148

: 00:07:49

knew beforehand that it would work.

149

: 00:07:52

It was just sometimes

it's completely absurd.

150

: 00:07:54

And I thought, yeah, like,

how could I make it work?

151

: 00:07:58

Uh, and then several days, like

tinkering with my code and try

152

: 00:08:02

to like, look at other tutorials.

153

: 00:08:03

tutorials, uh, look up, uh, things

on Stack Overflow and see if anyone

154

: 00:08:08

has ever done something like this.

155

: 00:08:11

Yeah.

156

: 00:08:11

So it's also a lot of

like findings for me.

157

: 00:08:13

Sometimes you have to be

creative and solve your own

158

: 00:08:16

challenge and your own problems.

159

: 00:08:18

Um, because yeah, you always

encounter something, uh, in your

160

: 00:08:22

project and, um, the good mindset

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

161

: 00:08:27

And a solution.

162

: 00:08:28

So don't give up when you first

see an error or see like a problem.

163

: 00:08:33

And that project definitely didn't work

well at the beginning because I know

164

: 00:08:37

like the LLM was was really unreliable.

165

: 00:08:41

So I had to like change

the temperature for the.

166

: 00:08:44

LLM, and then tweak something in the

workflow and try to validate the output of

167

: 00:08:51

the LLM with Pydantic and all these kind

of things, uh, just for a toy project.

168

: 00:08:56

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

169

: 00:08:59

Um, but it was, it was fun.

170

: 00:09:00

Um, yeah.

171

: 00:09:01

Yeah, I think nowadays there are so

many new frameworks that help you do

172

: 00:09:06

these kind of projects, maybe like

in an easier way, or like, um, Python

173

: 00:09:11

packages that you can use, I think,

like, instruct, um, like some new

174

: 00:09:17

Python packages that lets you output

things from LLM in a structured format.

175

: 00:09:24

That is something that I only

knew later, but that was much

176

: 00:09:27

later after I posted that project.

177

: 00:09:30

Avery: Yeah, I think there's so many

good things that people can take from,

178

: 00:09:34

from what you just said, because I think

oftentimes people, you know, look, look at

179

: 00:09:38

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

Oh, these people are experts with data.

180

: 00:09:42

They know what they're doing.

181

: 00:09:43

And the truth is that no one

actually really knows a hundred

182

: 00:09:45

percent what they're doing in data

ever because it's ever evolving.

183

: 00:09:48

It's ever expanding.

184

: 00:09:49

Uh, there's always new things.

185

: 00:09:51

And.

186

: 00:09:51

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

guess I can speak for you cause you

187

: 00:09:54

just mentioned this, but like, uh, I

get stuck all the time and it's still

188

: 00:09:57

like a process to, to troubleshoot

and to, like you said, use chat GPT to

189

: 00:10:01

try to solve or go to stack overflow

and, and get through those problems.

190

: 00:10:05

So even though you're creating these

videos, you've done a lot of them,

191

: 00:10:07

you have like, like a decade of

experience, you're still getting stuck

192

: 00:10:11

and you still have to troubleshoot.

193

: 00:10:12

Thu Vu: Yeah, exactly.

194

: 00:10:13

No, I, I think anyone can

do this kind of projects.

195

: 00:10:16

Um, yeah, given that you put

in the time and put a little

196

: 00:10:20

bit effort and some patience.

197

: 00:10:22

Um, and I've seen also a

really, really cool project on

198

: 00:10:26

your, on your channel as well.

199

: 00:10:27

And I, I thought like you really put a lot

of attention to all these details on the

200

: 00:10:34

videos or visualizations that you've made.

201

: 00:10:36

I thought like, yeah, it's really.

202

: 00:10:38

Really cool.

203

: 00:10:39

Yeah.

204

: 00:10:39

Maybe something sometimes I just thought,

okay, I can create this visualization

205

: 00:10:44

of like, um, you know, animating

something just, just for the fun of it.

206

: 00:10:48

Uh, and you did it sometimes.

207

: 00:10:50

And I thought, Oh, like you have some

really, uh, really great, uh, insights

208

: 00:10:54

on how you can show things differently.

209

: 00:10:57

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

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

210

: 00:11:02

to look at other people's, um, work

and see how you can learn from them.

211

: 00:11:07

And yeah.

212

: 00:11:08

And I guess for many people as well,

uh, in the audience, yeah, you can

213

: 00:11:12

definitely just like sometimes come

across something on YouTube and then you

214

: 00:11:17

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

215

: 00:11:20

Um, yeah, so that's a great way

to learn from each other as well.

216

: 00:11:23

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

yeah, know it all kind of person,

217

: 00:11:28

definitely in data science or

218

: 00:11:29

Avery: machine learning.

219

: 00:11:30

Well, you definitely know a lot and people

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

220

: 00:11:34

yeah, I've made some project videos.

221

: 00:11:36

In the past.

222

: 00:11:36

I haven't made any

project videos recently.

223

: 00:11:38

I find them that it appears

that the YouTube audience

224

: 00:11:43

doesn't like them as much.

225

: 00:11:44

I know I've, I've spoken to

Luke Bruce in the past as well.

226

: 00:11:47

And he has like this awesome

video on his channel where he was

227

: 00:11:50

analyzing his mountain bike data.

228

: 00:11:52

And I was like, that video rocks.

229

: 00:11:54

And he's like, I know, right.

230

: 00:11:55

It should have way more views.

231

: 00:11:56

And I don't know.

232

: 00:11:57

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

a great job of, of making it

233

: 00:12:00

digestible for the YouTube audience.

234

: 00:12:01

Cause I think these personal projects

where Like, for example, I, I looked

235

: 00:12:05

at like what states Google the most

every single hour for like a quarter

236

: 00:12:09

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

analyzing my financial data with an LLM.

237

: 00:12:13

Luke's done the mountain biking one.

238

: 00:12:15

I think these projects are fun.

239

: 00:12:16

And I think, I think it's as data

scientists or data analysts, like we

240

: 00:12:20

want to use data in our real normal life,

not just in our work or our business.

241

: 00:12:24

So these types of like personal

projects, I think can be really fun.

242

: 00:12:27

Thu Vu: Yeah, yeah, absolutely.

243

: 00:12:28

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

that, uh, not all the videos that

244

: 00:12:32

you make or all the work that you

make would, uh, get the recognition

245

: 00:12:36

that you think it deserves.

246

: 00:12:37

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

and, uh, I'm sure some people

247

: 00:12:41

also post things on LinkedIn.

248

: 00:12:44

I also have some friends who, uh,

post, um, try to post on LinkedIn more

249

: 00:12:48

often, but really like doesn't get

much views or, uh, like interaction.

250

: 00:12:53

And then.

251

: 00:12:54

Yeah.

252

: 00:12:54

You feel discouraged.

253

: 00:12:55

Um, and I'm sure a lot of

people also relate to that.

254

: 00:13:01

I also don't know how some videos

got, uh, seen and some people, some

255

: 00:13:05

videos just got completely tanked

and no one really look at it ever.

256

: 00:13:10

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

of like predict that even though, yeah, I

257

: 00:13:14

really want to predict it, but yeah, yeah.

258

: 00:13:17

I think there's some kind of secrets.

259

: 00:13:19

I tried to make the first, um,

like the opening of the video.

260

: 00:13:23

really engaging.

261

: 00:13:24

Like the third, the first 30 seconds or

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

262

: 00:13:28

the YouTube gurus and, uh, try to kind of

like make the best edits out of it, uh,

263

: 00:13:34

and see if people keep watching longer.

264

: 00:13:36

And usually they do, but overall the

quality of the whole project is the

265

: 00:13:40

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

266

: 00:13:43

And I hope that is,

267

: 00:13:45

Avery: that's true.

268

: 00:13:46

It's, it's definitely hard.

269

: 00:13:48

Um, let's talk about, so like with

these cool projects that we, that we've

270

: 00:13:51

been talking about, if people want

to build their own, obviously they

271

: 00:13:55

can kind of look at the stuff, you

know, you and I have done on YouTube.

272

: 00:13:58

Um, I know that you give you your GitHub,

uh, in the description a lot of time,

273

: 00:14:02

uh, for most of my projects, you can

get all of the GitHub stuff for free.

274

: 00:14:06

It's kind of like open source like that.

275

: 00:14:08

But you also just recently

created something called Python

276

: 00:14:10

for AI projects, which basically.

277

: 00:14:12

is an opportunity, a platform where

people can, you know, build some

278

: 00:14:16

pretty cool projects for an AI with

Python, kind of with your guidance.

279

: 00:14:20

Is that right?

280

: 00:14:21

Thu Vu: Yeah.

281

: 00:14:21

Yeah, that's right.

282

: 00:14:22

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

my, I put my heart into this project

283

: 00:14:27

because I believe that many people

struggle to learn the basics of Python.

284

: 00:14:31

Python and AI, because they don't

have the really like the most

285

: 00:14:35

beginner friendly kind of, uh,

guidance or kind of like a road map.

286

: 00:14:40

Um, so that's why I decided to create

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

287

: 00:14:47

curriculum that teach people Python from

scratch, all the fundamentals, and also

288

: 00:14:53

hands on stuff on how to learn, how to,

how to use Visual Studio Code, how to

289

: 00:14:59

use AI assistance for your work, and also

learn the basics of machine learning,

290

: 00:15:04

deep learning, and AI, and with some

project walkthroughs as well for people

291

: 00:15:11

to really follow and create their own

projects with kind of like the idea.

292

: 00:15:16

Yeah, using the, uh, large language

models and kind of like, uh, yeah,

293

: 00:15:21

just like some projects I did also on

my YouTube channel, um, one projects

294

: 00:15:26

about extracting information, uh, from

PDFs using, uh, large language models

295

: 00:15:31

and how you structure it in a nice

format in a nice, uh, table format.

296

: 00:15:36

And also, yeah, I'm also planning to

add a few more advanced projects like

297

: 00:15:42

fine tuning and LLM and all these things

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

298

: 00:15:49

always wanted to do it also myself.

299

: 00:15:53

Now it's also an opportunity to kind

of like explore it further and help

300

: 00:15:57

other people to learn them as well.

301

: 00:15:59

Avery: Very cool.

302

: 00:15:59

I, I might have to check that out because

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

303

: 00:16:04

do anything with like an LLM from scratch.

304

: 00:16:06

So there's some, there's probably some

things in there, uh, that I could learn.

305

: 00:16:09

So that would be a lot of fun.

306

: 00:16:10

We'll have the, uh, the link

in the show notes down below.

307

: 00:16:14

Thu Vu: Yeah, definitely.

308

: 00:16:15

Yeah.

309

: 00:16:16

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

know, Python and you, yeah, probably

310

: 00:16:20

you can pick it up very quickly and,

uh, all the machine learning AI stuff.

311

: 00:16:25

Uh, I teach someone as really like

someone who has never worked, never

312

: 00:16:31

built a machine learning model before.

313

: 00:16:33

Try to teach the, like the fundamentals

and the building blocks for you

314

: 00:16:37

probably is, is much less relevant.

315

: 00:16:40

Um, but yeah.

316

: 00:16:41

That's, uh, indeed.

317

: 00:16:42

Yeah.

318

: 00:16:42

That's kind of my project

319

: 00:16:45

Avery: at the moment.

320

: 00:16:46

Very cool.

321

: 00:16:46

I can tell that you're, you're

really excited and passionate about

322

: 00:16:49

it, uh, which I think is very cool.

323

: 00:16:50

Um, we've talked a lot about projects

and, and your, your great YouTube

324

: 00:16:54

channel, and I've kind of given a

little bit of your background, but,

325

: 00:16:57

uh, I'm guessing a lot of people

listening don't a hundred percent know.

326

: 00:17:00

Uh, your, your background.

327

: 00:17:01

So could you just tell us like what

you studied in school and then maybe

328

: 00:17:04

what your first job was out of school?

329

: 00:17:06

Thu Vu: Uh, yeah, yeah.

330

: 00:17:07

Thanks for, for asking about this.

331

: 00:17:09

I think, uh, yeah, I, I also

haven't really shared about

332

: 00:17:14

it a lot on my channel.

333

: 00:17:16

Um, my, about my background.

334

: 00:17:18

So if

335

: 00:17:19

Avery: you don't want to talk about

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

336

: 00:17:21

Thu Vu: Oh no, no, no.

337

: 00:17:22

Of course.

338

: 00:17:23

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

339

: 00:17:25

Yeah,

340

: 00:17:25

Avery: so don't need

341

: 00:17:26

Thu Vu: to add it in an edit.

342

: 00:17:28

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

my first, uh, degree that, um,

343

: 00:17:34

at school is, uh, economics.

344

: 00:17:37

And, um, back then I was, yeah,

I was still living in Vietnam.

345

: 00:17:41

Um, and I got my bachelor in economics and

then I go to, I went to the Netherlands

346

: 00:17:47

to study a master, uh, in economics.

347

: 00:17:50

as well.

348

: 00:17:51

So this is, yeah, it was kind of

like a very, uh, theoretical degree.

349

: 00:17:55

And, uh, although, yeah, you learn

some basic stuff, econometrics,

350

: 00:17:59

um, linear regression, which

were like basic statistics.

351

: 00:18:03

And that was quite useful later on as a,

like a, when I would start working as a

352

: 00:18:08

data analyst and then learning a bit more

data science y stuff, machine learning.

353

: 00:18:15

And that was in 2015.

354

: 00:18:17

So I moved to the Netherlands when I was

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

355

: 00:18:23

I started working in the Netherlands

and stayed, um, decided to stay,

356

: 00:18:28

uh, even though it was really

not the first plan, uh, when I

357

: 00:18:33

moved to the Netherlands to study.

358

: 00:18:35

Um, but, yeah.

359

: 00:18:36

I found out that it was like

really, really a great country.

360

: 00:18:39

And I fell in love with the culture,

the food, not so much the weather,

361

: 00:18:43

not so much, but people were great.

362

: 00:18:46

The working environment was really, really

transparent, really nice, very efficient.

363

: 00:18:52

And also people are very direct.

364

: 00:18:54

And I really liked the way that,

you know, people are honest to each

365

: 00:18:57

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

they give you really straightforward

366

: 00:19:02

feedback that you can improve on.

367

: 00:19:04

When I start my internship, uh, after

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

368

: 00:19:09

felt really good about, uh, working here.

369

: 00:19:12

Um, so that's how I

started my career actually.

370

: 00:19:16

And I started working as a

data, kind of like a research

371

: 00:19:20

assistant in my internship.

372

: 00:19:21

And then later I got a job

offer as a data analyst.

373

: 00:19:26

So I got really lucky to, you know,

Actually start in this career because at

374

: 00:19:31

the beginning when I learned economics,

the thing that I would think about was

375

: 00:19:36

more like a researcher or maybe working

in policy, working in maybe a little

376

: 00:19:41

bit like even started a PhD and that

I even applied for a PhD in Amsterdam.

377

: 00:19:47

I still remember.

378

: 00:19:49

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

379

: 00:19:51

I didn't get it.

380

: 00:19:53

, uh, otherwise I would be like, I

don't know where I would be right now.

381

: 00:19:57

And yeah.

382

: 00:19:58

And a few, yeah, a few years later I

start working at BWC, um, Pricewaterhouse

383

: 00:20:05

Coopers and I start working as

a, a consultant for six months.

384

: 00:20:11

Six years.

385

: 00:20:12

There, I also learned a lot of

different, uh, new things and worked

386

: 00:20:16

for different, in different projects

and I found it really incredibly,

387

: 00:20:20

um, really, uh, I learned so much.

388

: 00:20:23

Um, it was really helpful to work

with so many different people and

389

: 00:20:27

you pick up new things every time.

390

: 00:20:29

Yeah, and that when I was working there

at BWC, I decided to learn a bachelor.

391

: 00:20:36

degree in computer science, I

decided to take it because I feel

392

: 00:20:40

like I still missed something.

393

: 00:20:42

I, my technical skills were still kind

of like not so, I was not so confident.

394

: 00:20:47

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

probably, you know, like an imposter

395

: 00:20:53

feeling and also the drive to learn more

in a more kind of like structured way.

396

: 00:20:59

Um, that's how I decided

to take the degree.

397

: 00:21:03

It's an online degree that you

can take via Coursera, actually.

398

: 00:21:07

It's very nice.

399

: 00:21:08

Yeah, it's a, it was a

lot of work, actually.

400

: 00:21:10

It was a master, um, bachelor degree

with, I don't know, 22 modules.

401

: 00:21:16

And yeah, I still have the final

project that I have to finish.

402

: 00:21:21

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

six years now that I haven't finished.

403

: 00:21:26

So I still feel ashamed when I talk about

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

404

: 00:21:34

work on, uh, the YouTube channel a lot

and, uh, it was all kind of like all

405

: 00:21:38

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

406

: 00:21:41

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

work and my personal history and

407

: 00:21:46

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

408

: 00:21:51

Avery: Can you just, can you just

submit a URL to, of your YouTube

409

: 00:21:55

channel to the degree and just

be like, here's my final project.

410

: 00:22:00

Thu Vu: Yeah.

411

: 00:22:00

Yeah.

412

: 00:22:02

Yeah.

413

: 00:22:02

Like for like my own project or

414

: 00:22:05

Avery: just like your whole,

your whole YouTube channel.

415

: 00:22:07

I feel like that should

count as your final project.

416

: 00:22:09

I feel like they should, they should,

uh, give you, give you credit for

417

: 00:22:12

that because you've done some,

some pretty cool things on there.

418

: 00:22:17

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

419

: 00:22:18

I will, I will try it out.

420

: 00:22:20

Avery: That's, that's great.

421

: 00:22:21

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

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

422

: 00:22:25

your, your story there, uh, was you going

back to school once you had a data job.

423

: 00:22:30

And one of the things I try to, I,

I try to help people who are like

424

: 00:22:32

brand new to data and who like

want to become a data analyst.

425

: 00:22:37

And obviously going to back back

to school is always an option.

426

: 00:22:40

Um, but a lot of the times if you get

your foot in the door first with any

427

: 00:22:43

sort of data job at the beginning, it's

going to be so much easier to go back

428

: 00:22:47

to school for a variety of reasons.

429

: 00:22:49

And so like a lot of the cool things

that you do, like, like the LLM

430

: 00:22:52

stuff, uh, use Docker in that video.

431

: 00:22:55

A lot of that stuff is, is things

that you don't necessarily need

432

: 00:22:58

when you first land your data job,

but, but they can help you become a

433

: 00:23:00

better data analyst down the road.

434

: 00:23:02

And so I kind of like how you, you kind

of gotten your foot in the data door

435

: 00:23:05

with, with the data stuff you had from

your economics degree, and then you, you

436

: 00:23:09

upscaled after you were already there.

437

: 00:23:11

So that way you can, you can become a data

analyst or sorry, become a better data

438

: 00:23:14

analyst, you know, have a bigger impact

at your company, uh, hopefully get, get

439

: 00:23:18

compensated more and better because of it.

440

: 00:23:20

Um, but I love that you did that.

441

: 00:23:21

After you get you started, basically,

442

: 00:23:23

Thu Vu: yeah, yeah, definitely.

443

: 00:23:25

And, and I think this is,

uh, you're totally right.

444

: 00:23:28

It's so much easier when you get an

internship or you get a, like a really

445

: 00:23:32

beginner, uh, like an entry level

job in data science or data analysis.

446

: 00:23:37

Even like us, just a small, uh, portion

of your job is, uh, data related.

447

: 00:23:42

You can always like show it a little

bit more that you have some experience.

448

: 00:23:47

And this is really a big advantage.

449

: 00:23:50

So, yeah, I would always advise anyone

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

450

: 00:23:55

by step and, uh, take anything that you

may find, like, you can learn something,

451

: 00:24:01

um, regarding the data skills, and then

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

452

: 00:24:07

That's so much easier, indeed.

453

: 00:24:09

Avery: One of, one of your latest videos,

you explored data trends, um, and you

454

: 00:24:13

found some pretty interesting things,

uh, that was going on with the data

455

: 00:24:16

job market, the tech market in general,

what was like your favorite trend that

456

: 00:24:20

you kind of discovered in this video?

457

: 00:24:22

Thu Vu: Yeah.

458

: 00:24:22

Yeah.

459

: 00:24:22

I think the favorite trend for me is like.

460

: 00:24:25

The new development, when you think

about like technical skills, I find

461

: 00:24:32

that like Python is, has been really

so become so much more ubiquitous.

462

: 00:24:37

So, so much more universal

compared to a few years ago.

463

: 00:24:42

I think definitely a few years ago, it

was like, uh, for the discord analysis or

464

: 00:24:47

some particular software, like SAS, even

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

465

: 00:24:54

But right now, also within my work,

a lot of, in a lot of projects,

466

: 00:24:58

we are migrating all the code

base from SAS, from R to Python.

467

: 00:25:03

So it was like a nice.

468

: 00:25:05

An interesting observation, and especially

with the development of AI right now,

469

: 00:25:11

Python is supporting a lot of cool tools.

470

: 00:25:15

For example, like, um, uh, things like

lang chain and all these different

471

: 00:25:19

frameworks to create, um, an AI

powered application, an AI agent, all

472

: 00:25:26

these frameworks are all in Python.

473

: 00:25:28

Yeah, that, that, that's really

like a Uh, yeah, like a really cool

474

: 00:25:33

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

475

: 00:25:35

Further, I think there's also some

interesting trends that I noticed, um,

476

: 00:25:41

in kind of like the freelancing space.

477

: 00:25:44

It seems, it seems like, They're

more freelancing jobs than right

478

: 00:25:50

now, than, than a few years ago.

479

: 00:25:53

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

companies are more like, probably they

480

: 00:25:59

are experimenting with things a lot.

481

: 00:26:01

And that's why you see.

482

: 00:26:04

Probably some of them have a little

bit budget, uh, or even individuals

483

: 00:26:08

or small business owners, they have

a little bit budget and they want to

484

: 00:26:12

hire someone to do something for them.

485

: 00:26:14

I recently have a friend who worked a

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

486

: 00:26:20

so, um, uh, retrieval, uh, augmented

generation kind of projects using LLMs.

487

: 00:26:27

And then, um, yeah, so that

person connects it to me.

488

: 00:26:31

Uh, asking, like, do you have someone

or you can help me with, uh, uh,

489

: 00:26:34

building and, uh, kind of like a tool to

extract this and that information from

490

: 00:26:39

like, uh, a hundred PDFs that he has.

491

: 00:26:42

And so, yeah, so I introduced my friend

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

492

: 00:26:48

uh, to help him with, with this task.

493

: 00:26:51

And I think this is also kind of like an

example of like how people are recognizing

494

: 00:26:55

the role of, uh, AI and automation.

495

: 00:26:58

And they want to get some, something done.

496

: 00:27:00

And so, yeah, it doesn't need to

be a fixed contract, a fixed job.

497

: 00:27:05

It's more like a experiment sometimes.

498

: 00:27:08

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

also really interesting and I keep

499

: 00:27:12

thinking about how, uh, how people

can find these kind of projects.

500

: 00:27:16

Uh, they can, yeah, like people who

need to get things done and people who

501

: 00:27:21

has the skill, how can they, uh, meet

each other more often or how they can

502

: 00:27:25

more effectively, uh, meet it, uh,

like kind of like, um, come across each

503

: 00:27:29

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

504

: 00:27:32

Yeah, that there are the two trends

that I, yeah, that I really like.

505

: 00:27:36

And I also kind of like got a bit

surprised, but also not so surprised,

506

: 00:27:40

uh, how, how that, how, yeah.

507

: 00:27:43

Avery: It's fascinating.

508

: 00:27:44

We live in a really exciting

time where you can start a side

509

: 00:27:47

hustle or start your own business.

510

: 00:27:48

That's, that's what I did three

years ago, three and a half years

511

: 00:27:51

ago was I started to freelance and I

started to make more money freelancing

512

: 00:27:55

than I did in my regular job.

513

: 00:27:56

And I was like, okay, I'm

just going to do that.

514

: 00:27:57

Oh, really?

515

: 00:27:58

Yeah.

516

: 00:27:58

Yeah.

517

: 00:27:59

That's why I left Exxon was to start doing

freelance projects and start an agency.

518

: 00:28:04

Yeah.

519

: 00:28:05

I ended up switching mostly

to teaching because I figured

520

: 00:28:08

out I really enjoy teaching.

521

: 00:28:09

So that's what I do.

522

: 00:28:10

Full time now pretty much.

523

: 00:28:11

Um, but yeah, the freelancing

stuff is super fascinating and I

524

: 00:28:13

think there's a great opportunity,

uh, for people to get into that.

525

: 00:28:16

Uh, and I also love that you, you

brought up the, the Python trend.

526

: 00:28:19

I think it just became, you

had mentioned that video.

527

: 00:28:21

It just became the most common or

most frequently used language on

528

: 00:28:25

GitHub, uh, which was a big deal.

529

: 00:28:27

Um, so Python, definitely

a thing of the future.

530

: 00:28:29

Another trend, uh, that I really

liked, especially since I helped

531

: 00:28:32

people land their first day at

a job is you looked at like.

532

: 00:28:35

The number of data jobs

over the last few years.

533

: 00:28:38

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

or if we've seen a decrease in jobs,

534

: 00:28:42

because you know, a lot of people are

like, Oh, the economy kind of stinks.

535

: 00:28:45

And you know, the job

market's really bad right now.

536

: 00:28:48

Uh, and your conclusion was, you know,

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

537

: 00:28:51

The, the, the graph was kind of a little

bit downward in terms of like number

538

: 00:28:55

of jobs, but it was relatively flat.

539

: 00:28:57

And that's actually, I did.

540

: 00:28:59

Yeah.

541

: 00:28:59

Uh, a similar video recently where I

looked at, um, the growth of, of data

542

: 00:29:04

jobs from a different data source and the

data source you used and basically came

543

: 00:29:08

to the same conclusion that the, if you

compared it to: 2019

544

: 00:29:14

were up specifically for like data

analysts were still up around like 20%.

545

: 00:29:19

But it was year over year,

but it was a flat 20 percent

546

: 00:29:22

for like the last year or two.

547

: 00:29:23

So I was really comforted to

see, like, you kind of came to a

548

: 00:29:26

similar conclusion that I did with

a totally different, uh, data set,

549

: 00:29:30

completely independent of each other.

550

: 00:29:32

Thu Vu: Oh, that's really cool.

551

: 00:29:33

Um, that that's really cool to see.

552

: 00:29:34

Indeed.

553

: 00:29:35

Um, when I was using, yeah, I

actually use the, uh, kind of like

554

: 00:29:39

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

collected by Luke, uh, Luke Burrus.

555

: 00:29:44

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

all this, like, web scraping stuff.

556

: 00:29:47

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

doubting, uh, I didn't want to

557

: 00:29:52

make a conclusion that, oh, this is

like decreasing that we are seeing.

558

: 00:29:56

Indeed, it's more like flattened out

and, uh, depending on how you see it.

559

: 00:30:01

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

a, uh, glass half full or empty.

560

: 00:30:07

You, yeah, like it's quite, I

think it's quite normal to see some

561

: 00:30:11

fluctuation over the year over year.

562

: 00:30:13

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

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

563

: 00:30:18

I would worry about, but more like,

uh, what kind of jobs are being posted?

564

: 00:30:24

Like, the job compositions are changing

rather than the number of jobs.

565

: 00:30:30

I think.

566

: 00:30:31

Probably within the same job

title, you probably have something

567

: 00:30:35

new in the job descriptions.

568

: 00:30:37

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

chance to really dive into that in,

569

: 00:30:41

in that, um, data job trend video.

570

: 00:30:44

But I think it would be really cool to

see how the, uh, the job functions or

571

: 00:30:48

the job, uh, description is changing.

572

: 00:30:52

And how you can maybe learn from that.

573

: 00:30:55

What can you prepare to meet

that demand in the future?

574

: 00:30:59

I'm sure there will be more like,

uh, really things that are more

575

: 00:31:02

like, uh, data AI engineering kind

of role that are emerging in data

576

: 00:31:07

science, in the, like, data science,

uh, Uh, machine learning space.

577

: 00:31:12

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

it's probably like, it's better

578

: 00:31:17

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

a little bit like, uh, yeah, yeah.

579

: 00:31:22

Take that with a little bit grain of salt.

580

: 00:31:24

When you look at the chart, um, probably

it doesn't really tell the full story.

581

: 00:31:28

Avery: I agree.

582

: 00:31:29

It's, it's, it would

be really interesting.

583

: 00:31:30

That data sets very rich.

584

: 00:31:32

Um, but once you get into text

analysis and NLP, you just have

585

: 00:31:36

to have more data science skills.

586

: 00:31:38

It's like a whole separate.

587

: 00:31:39

Part of data science, which just

takes longer to do than things like

588

: 00:31:43

counting and line charts and, uh,

bar charts and stuff like that.

589

: 00:31:46

Um, right, right.

590

: 00:31:48

Definitely.

591

: 00:31:48

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

great project, uh, for your Python

592

: 00:31:53

for AI projects, uh, group that

you're doing with, with the course.

593

: 00:31:56

So maybe that we'll look

forward to seeing that.

594

: 00:31:58

On the curriculum in the future to thank

you so much for being on the podcast.

595

: 00:32:04

If you guys haven't checked out

her channel, please go do so.

596

: 00:32:06

Now we'll have a link to it in the

show notes down below, as well as

597

: 00:32:10

her Python for AI projects too.

598

: 00:32:12

Thank you so much for being on the show.

599

: 00:32:13

Thu Vu: Yeah.

600

: 00:32:13

Thank you so much for having me here.

601

: 00:32:15

I agree.

602

: 00:32:16

And yeah, it was a great pleasure

to meet you here on this podcast.

603

: 00:32:20

Avery: Same.