January 21, 2025 • 34 mins
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
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🎥 YouTube Channel: https://www.youtube.com/@Thuvu5
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💻 Website: https://thuhienvu.com/
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python-course-earlybird.framer.website
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🎥 YouTube Channel: https://www.youtube.com/@averysmith
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Thu Vu (00:00):
None of the projects that I posted on my channel, I knew
beforehand that it would work.
It was just sometimesit's completely absurd.
And I thought, yeah, like,how could I make it work?
And then several days, like tinkeringwith my code and try to like, look at
other tutorials, look up things on StackOverflow and see if anyone has any.
(00:20):
ever done something like this.
Yeah.
So it's also a lot oflike findings for me.
Sometimes you have to be creativeand solve your own challenge and your
own problems because yeah, you alwaysencounter something in your project.
The good mindset is just, uh,like there's got to be a solution.
So don't give up.
When you first see an erroror see like a problem.
Avery (00:42):
All right.
If you are watching on YouTube oryou've ever looked up data analytics on
YouTube, you've probably seen, uh, TuVu, our guest today, one of her videos,
because they are absolutely amazing.
Uh, in the past, she's been a dataanalytics consultant with companies
like PWC, uh, and she's a prolific.
Content creator in the data analyticsspace to welcome to the podcast.
Thu Vu (01:03):
Thanks, Abby.
It's, it's really, reallya great introduction.
So kind of you.
Of course,
Avery (01:08):
it's
Thu Vu (01:08):
my pleasure
Avery (01:09):
to be here.
Of course.
I'm so glad to have you here.
One thing that I love about your videos,uh, is you do some pretty cool projects.
Uh, you do some pretty cool.
Things with, with ai, things withmachine learning, things with just data
analytics and data science in general.
And I think we need more ofthat on YouTube, more like
actual projects being done.
I think you do a great job of doing that.
Thu Vu (01:29):
Yeah, absolutely.
I think, um, people talk about datascience or machine learning or AI a
lot, but I think what I personallymissed, it was like some kind of
like a hands-on demonstration of howyou're gonna use a new technology,
let's say, uh, like a, a network.
analytics or some AI, some cool AImodels or large language models, how
(01:52):
you can apply it to your own problemand also demonstrate it like end to end
almost, um, how you start with an idea,how you get, um, inspired, how, uh, how
you think about the problem, how youframe it and how you Kind of go step by
step, explore it further and further.
And then in the end you have somethingthat you can show to other people.
(02:13):
And hopefully it's a little bit usefuland um, yeah, hopefully you have fun.
So that is kind of the idea that I kindof like, yeah, it was not, The first
thing that I, um, actually startedwhen I, um, yeah, when I started
making videos on YouTube, um, it justoccurred to me that people really
liked it and people really appreciatethe effort to think something so much,
(02:38):
uh, yeah, think through, uh, some, aparticular topic in such a great detail.
Also kind of hopefully inspire otheruse cases for people to try out.
Yeah, so that, that waskind of like the motivation.
And up until now, that's kindof like, that is the type of
video that I really like making,although it takes a lot of effort.
(03:00):
And of course, like I lostsome hair because of it, but
yeah, it was fun and hopefullyhelpful for other people as well.
Avery (03:08):
I think so.
And I think it's very impressive becauseit's not easy to make technical videos and
make technical videos engaging for likea YouTube audience, which, uh, I think a
lot of people watch a video for like 35seconds and then skip to the next one.
So your, your ability to, to kind ofcapture these technical things in a, you
know, shorter video, but not like tooshort, uh, I think is very impressive.
(03:29):
Uh, have you always enjoyedmaking fun projects like this?
Thu Vu (03:33):
I think at the beginning, it was kind of a struggle.
I think it was like, yeah, I think withouta lot of, like, experience with, uh,
you know, like making tutorials, it wasalso kind of like always like climbing
such a, like a big mountain every time.
Like how you talk through everything,how you explain everything, every
little step that you make, uh, makingthe, like the screen recording and also
(03:55):
kind of like, So kind of like nice Brolls, you know, like in YouTube, you
have like all these kind of like funthing that you film yourself and then
combine it in, in like a good storyline.
I think that that was kindof a struggle for me in data
science or machine learning.
You can always find some kindof like a project in terms of a
blog post or, um, like a Jupyternotebook or a GitHub repository.
(04:18):
And that, um, yeah, that's usuallyhow people think about these projects.
But like, how you present it in a video inan engaging way, I think it was like the,
the biggest, yeah, it was a challenge.
At the beginning I was scared.
I was totally like, I, I didn't really,I could not really enjoy filming myself
(04:38):
and like make such a complex explanation.
I was kind of uncomfortable with it.
At the start, but itgot better and better.
I know how to kind of like preparemy scripts, how to kind of like,
think about it, maybe a few days,come up, come back to the project
and refine what I want to tell.
And, uh, it, the workflow gets better.
(04:59):
Better and better.
Uh, so it's also less scary and I'malso not a native English speaker.
So sometimes there are a lotof concepts I want to explain,
uh, and I like words for it.
I love the way to, to explain it.
And that's also frustrated.
Uh, but yeah, it's, uh, yeah, all ofthese struggles, I think it's kind of
like, it's worth the effort for me and Ithink it's really rewarding to, to create.
(05:24):
That's kind of my, uh, likehow, how it went for me.
Avery (05:27):
It's, it's really impressive, especially, especially like
in a, in a second or, or howmany languages do you speak?
Thu Vu (05:33):
I speak, uh, Vietnamese as a, uh, as a mother tongue and my, uh, yeah,
definitely the next language is English.
And I also speak Dutch becauseI've been living in the
Netherlands for the last 10 years.
That's so
Avery (05:50):
impressive.
That's so impressive to be creating thisgood of content in your second language.
Now let's talk about a littlebit more about projects.
So one of the cool things that you'vedone is you've built a project to analyze
your finances, uh, with like a local LLMs.
Like you basically created your,your own version of like chat GPT to
specifically look at your finances.
(06:12):
Now that's crazy because.
Like I think most people wouldprobably just like do something
easier to do that, but you'relike, no, I want to make it cool.
I want to make it hard.
Have you always been interestedin like creating kind of cool
personal pet projects like this?
Yeah.
Yeah.
Thu Vu (06:25):
Yeah.
Thanks for pointing it out.
I think that's also kind of like oneof the projects that I got some really,
like some people saying on YouTube.
Oh, like you, you just invented somethingthat was completely unnecessary because
like on the, like the bank banking appthat you are using, probably you also have
kind of the insights feature where youcan also have the same, do the same thing.
(06:47):
But yeah, yeah, I know about thatfeature, but I was like, huh, how can
I use, uh, an LLM to help me with this?
And because I download my bank statementsall the time, uh, and I like in.
Yeah, I just like looking atit myself and see in detail
what kind of expenses I make.
Yeah, so that was thestart of the challenge.
(07:07):
And, uh, yeah, that video got a lotof, uh, nice, um, uh, yeah, nice
feedback because I think peoplereally like to have feedback.
Something that is a bit private,like, uh, when you have an LLM
and you, uh, you cannot post yourblank statements on ChatGBT or
Cloud AI to help you analyze it.
(07:28):
So yeah, like using a local LLM isa great way to kind of like test out
this idea and see how well it works.
it might work.
And I also have, yeah, it waslike a, like a trial and error.
I also didn't know if it wouldwork, um, at the beginning.
I, I think, yeah, none of the projectsthat I posted on my channel, I
(07:49):
knew beforehand that it would work.
It was just sometimesit's completely absurd.
And I thought, yeah, like,how could I make it work?
Uh, and then several days, liketinkering with my code and try
to like, look at other tutorials.
tutorials, uh, look up, uh, thingson Stack Overflow and see if anyone
has ever done something like this.
(08:11):
Yeah.
So it's also a lot oflike findings for me.
Sometimes you have to becreative and solve your own
challenge and your own problems.
Um, because yeah, you alwaysencounter something, uh, in your
project and, um, the good mindsetis just, uh, like there's got to be.
And a solution.
So don't give up when you firstsee an error or see like a problem.
(08:33):
And that project definitely didn't workwell at the beginning because I know
like the LLM was was really unreliable.
So I had to like changethe temperature for the.
LLM, and then tweak something in theworkflow and try to validate the output of
the LLM with Pydantic and all these kindof things, uh, just for a toy project.
(08:56):
So I was like, yeah, it was a lot of work.
Um, but it was, it was fun.
Um, yeah.
Yeah, I think nowadays there are somany new frameworks that help you do
these kind of projects, maybe likein an easier way, or like, um, Python
packages that you can use, I think,like, instruct, um, like some new
(09:17):
Python packages that lets you outputthings from LLM in a structured format.
That is something that I onlyknew later, but that was much
later after I posted that project.
Avery (09:30):
Yeah, I think there's so many good things that people can take from,
from what you just said, because I thinkoftentimes people, you know, look, look at
you and maybe look at me and they're like,Oh, these people are experts with data.
They know what they're doing.
And the truth is that no oneactually really knows a hundred
percent what they're doing in dataever because it's ever evolving.
It's ever expanding.
Uh, there's always new things.
(09:51):
And.
Uh, you know, I, I can't, I mean, Iguess I can speak for you cause you
just mentioned this, but like, uh, Iget stuck all the time and it's still
like a process to, to troubleshootand to, like you said, use chat GPT to
try to solve or go to stack overflowand, and get through those problems.
So even though you're creating thesevideos, you've done a lot of them,
you have like, like a decade ofexperience, you're still getting stuck
and you still have to troubleshoot.
Thu Vu (10:12):
Yeah, exactly.
No, I, I think anyone cando this kind of projects.
Um, yeah, given that you putin the time and put a little
bit effort and some patience.
Um, and I've seen also areally, really cool project on
your, on your channel as well.
And I, I thought like you really put a lotof attention to all these details on the
(10:34):
videos or visualizations that you've made.
I thought like, yeah, it's really.
Really cool.
Yeah.
Maybe something sometimes I just thought,okay, I can create this visualization
of like, um, you know, animatingsomething just, just for the fun of it.
Uh, and you did it sometimes.
And I thought, Oh, like you have somereally, uh, really great, uh, insights
(10:54):
on how you can show things differently.
And so I think, yeah, like, yeah, withmaking YouTube, it's, it's really fun
to look at other people's, um, workand see how you can learn from them.
And yeah.
And I guess for many people as well,uh, in the audience, yeah, you can
definitely just like sometimes comeacross something on YouTube and then you
(11:17):
thought, huh, I can maybe do this as well.
Um, yeah, so that's a great wayto learn from each other as well.
And it's definitely, I'm not a,yeah, know it all kind of person,
definitely in data science or
Avery (11:29):
machine learning.
Well, you definitely know a lot and peoplecan learn from you, uh, a lot and, and
yeah, I've made some project videos.
In the past.
I haven't made anyproject videos recently.
I find them that it appearsthat the YouTube audience
doesn't like them as much.
I know I've, I've spoken toLuke Bruce in the past as well.
And he has like this awesomevideo on his channel where he was
(11:50):
analyzing his mountain bike data.
And I was like, that video rocks.
And he's like, I know, right.
It should have way more views.
And I don't know.
It's, it's, it, I think you dida great job of, of making it
digestible for the YouTube audience.
Cause I think these personal projectswhere Like, for example, I, I looked
at like what states Google the mostevery single hour for like a quarter
of a year, you know, you've done thisanalyzing my financial data with an LLM.
(12:13):
Luke's done the mountain biking one.
I think these projects are fun.
And I think, I think it's as datascientists or data analysts, like we
want to use data in our real normal life,not just in our work or our business.
So these types of like personalprojects, I think can be really fun.
Thu Vu (12:27):
Yeah, yeah, absolutely.
Um, no, I think, I think you're rightthat, uh, not all the videos that
you make or all the work that youmake would, uh, get the recognition
that you think it deserves.
Yeah, it's, um, it's, it's hardand, uh, I'm sure some people
also post things on LinkedIn.
I also have some friends who, uh,post, um, try to post on LinkedIn more
(12:48):
often, but really like doesn't getmuch views or, uh, like interaction.
And then.
Yeah.
You feel discouraged.
Um, and I'm sure a lot ofpeople also relate to that.
I also don't know how some videosgot, uh, seen and some people, some
videos just got completely tankedand no one really look at it ever.
(13:10):
It's, it's really hard to, to, to kindof like predict that even though, yeah, I
really want to predict it, but yeah, yeah.
I think there's some kind of secrets.
I tried to make the first, um,like the opening of the video.
really engaging.
Like the third, the first 30 seconds orso, uh, that's what I learned from all
the YouTube gurus and, uh, try to kind oflike make the best edits out of it, uh,
(13:34):
and see if people keep watching longer.
And usually they do, but overall thequality of the whole project is the
video is, is, is more important, I guess.
And I hope that is,
Avery (13:45):
that's true.
It's, it's definitely hard.
Um, let's talk about, so like withthese cool projects that we, that we've
been talking about, if people wantto build their own, obviously they
can kind of look at the stuff, youknow, you and I have done on YouTube.
Um, I know that you give you your GitHub,uh, in the description a lot of time,
uh, for most of my projects, you canget all of the GitHub stuff for free.
(14:06):
It's kind of like open source like that.
But you also just recentlycreated something called Python
for AI projects, which basically.
is an opportunity, a platform wherepeople can, you know, build some
pretty cool projects for an AI withPython, kind of with your guidance.
Is that right?
Thu Vu (14:21):
Yeah.
Yeah, that's right.
Um, it's kind of like my, uh, reallymy, I put my heart into this project
because I believe that many peoplestruggle to learn the basics of Python.
Python and AI, because they don'thave the really like the most
beginner friendly kind of, uh,guidance or kind of like a road map.
Um, so that's why I decided to createthis, uh, this giant kind of like, uh,
(14:47):
curriculum that teach people Python fromscratch, all the fundamentals, and also
hands on stuff on how to learn, how to,how to use Visual Studio Code, how to
use AI assistance for your work, and alsolearn the basics of machine learning,
deep learning, and AI, and with someproject walkthroughs as well for people
(15:11):
to really follow and create their ownprojects with kind of like the idea.
Yeah, using the, uh, large languagemodels and kind of like, uh, yeah,
just like some projects I did also onmy YouTube channel, um, one projects
about extracting information, uh, fromPDFs using, uh, large language models
(15:31):
and how you structure it in a niceformat in a nice, uh, table format.
And also, yeah, I'm also planning toadd a few more advanced projects like
fine tuning and LLM and all these thingsthat, yeah, I also kind of like, I really
always wanted to do it also myself.
(15:53):
Now it's also an opportunity to kindof like explore it further and help
other people to learn them as well.
Avery (15:59):
Very cool.
I, I might have to check that out becauseyeah, I definitely, I don't know how to
do anything with like an LLM from scratch.
So there's some, there's probably somethings in there, uh, that I could learn.
So that would be a lot of fun.
We'll have the, uh, the linkin the show notes down below.
Thu Vu (16:14):
Yeah, definitely.
Yeah.
No, I, I think, uh, well, you, you, youknow, Python and you, yeah, probably
you can pick it up very quickly and,uh, all the machine learning AI stuff.
Uh, I teach someone as really likesomeone who has never worked, never
built a machine learning model before.
Try to teach the, like the fundamentalsand the building blocks for you
(16:37):
probably is, is much less relevant.
Um, but yeah.
That's, uh, indeed.
Yeah.
That's kind of my project
Avery (16:45):
at the moment.
Very cool.
I can tell that you're, you'rereally excited and passionate about
it, uh, which I think is very cool.
Um, we've talked a lot about projectsand, and your, your great YouTube
channel, and I've kind of given alittle bit of your background, but,
uh, I'm guessing a lot of peoplelistening don't a hundred percent know.
Uh, your, your background.
So could you just tell us like whatyou studied in school and then maybe
what your first job was out of school?
Thu Vu (17:06):
Uh, yeah, yeah.
Thanks for, for asking about this.
I think, uh, yeah, I, I alsohaven't really shared about
it a lot on my channel.
Um, my, about my background.
So if
Avery (17:19):
you don't want to talk about it, we don't have to just so you know.
Thu Vu (17:21):
Oh no, no, no.
Of course.
Uh, no, of course I can talk about it.
Yeah,
Avery (17:25):
so don't need
Thu Vu (17:26):
to add it in an edit.
Um, yeah, so I, uh, yeah, somy first, uh, degree that, um,
at school is, uh, economics.
And, um, back then I was, yeah,I was still living in Vietnam.
Um, and I got my bachelor in economics andthen I go to, I went to the Netherlands
(17:47):
to study a master, uh, in economics.
as well.
So this is, yeah, it was kind oflike a very, uh, theoretical degree.
And, uh, although, yeah, you learnsome basic stuff, econometrics,
um, linear regression, whichwere like basic statistics.
And that was quite useful later on as a,like a, when I would start working as a
(18:08):
data analyst and then learning a bit moredata science y stuff, machine learning.
And that was in 2015.
So I moved to the Netherlands when I wasYeah, around, yeah, 22 ish, um, back then.
I started working in the Netherlandsand stayed, um, decided to stay,
(18:28):
uh, even though it was reallynot the first plan, uh, when I
moved to the Netherlands to study.
Um, but, yeah.
I found out that it was likereally, really a great country.
And I fell in love with the culture,the food, not so much the weather,
not so much, but people were great.
The working environment was really, reallytransparent, really nice, very efficient.
(18:52):
And also people are very direct.
And I really liked the way that,you know, people are honest to each
other and, um, and, uh, Yeah, you,they give you really straightforward
feedback that you can improve on.
When I start my internship, uh, afterright after my master's, I felt really, I
felt really good about, uh, working here.
(19:12):
Um, so that's how Istarted my career actually.
And I started working as adata, kind of like a research
assistant in my internship.
And then later I got a joboffer as a data analyst.
So I got really lucky to, you know,Actually start in this career because at
the beginning when I learned economics,the thing that I would think about was
(19:36):
more like a researcher or maybe workingin policy, working in maybe a little
bit like even started a PhD and thatI even applied for a PhD in Amsterdam.
I still remember.
And thank God I didn't, I didn't got it.
I didn't get it.
, uh, otherwise I would be like, Idon't know where I would be right now.
(19:57):
And yeah.
And a few, yeah, a few years later Istart working at BWC, um, Pricewaterhouse
Coopers and I start working asa, a consultant for six months.
Six years.
There, I also learned a lot ofdifferent, uh, new things and worked
for different, in different projectsand I found it really incredibly,
(20:20):
um, really, uh, I learned so much.
Um, it was really helpful to workwith so many different people and
you pick up new things every time.
Yeah, and that when I was working thereat BWC, I decided to learn a bachelor.
degree in computer science, Idecided to take it because I feel
like I still missed something.
(20:42):
I, my technical skills were still kindof like not so, I was not so confident.
I was, I was still like, yeah, I wasprobably, you know, like an imposter
feeling and also the drive to learn morein a more kind of like structured way.
Um, that's how I decidedto take the degree.
(21:03):
It's an online degree that youcan take via Coursera, actually.
It's very nice.
Yeah, it's a, it was alot of work, actually.
It was a master, um, bachelor degreewith, I don't know, 22 modules.
And yeah, I still have the finalproject that I have to finish.
Um, so it was in the end, it was likesix years now that I haven't finished.
(21:26):
So I still feel ashamed when I talk aboutit, but yeah, uh, in the end, yeah, I
work on, uh, the YouTube channel a lotand, uh, it was all kind of like all
go into each other, uh, kind of, yeah.
So that's kind of like my, my, my,work and my personal history and
(21:46):
like my, uh, yeah, my story so far.
Avery (21:51):
Can you just, can you just submit a URL to, of your YouTube
channel to the degree and justbe like, here's my final project.
Thu Vu (22:00):
Yeah.
Yeah.
Yeah.
Like for like my own project or
Avery (22:05):
just like your whole, your whole YouTube channel.
I feel like that shouldcount as your final project.
I feel like they should, they should,uh, give you, give you credit for
that because you've done some,some pretty cool things on there.
Thu Vu (22:17):
Yeah, that's a great idea.
I will, I will try it out.
Avery (22:20):
That's, that's great.
Yeah, I think it's, I think it's onething that's, uh, I want to just pull from
your, your story there, uh, was you goingback to school once you had a data job.
And one of the things I try to, I,I try to help people who are like
brand new to data and who likewant to become a data analyst.
And obviously going to back backto school is always an option.
(22:40):
Um, but a lot of the times if you getyour foot in the door first with any
sort of data job at the beginning, it'sgoing to be so much easier to go back
to school for a variety of reasons.
And so like a lot of the cool thingsthat you do, like, like the LLM
stuff, uh, use Docker in that video.
A lot of that stuff is, is thingsthat you don't necessarily need
when you first land your data job,but, but they can help you become a
better data analyst down the road.
(23:02):
And so I kind of like how you, you kindof gotten your foot in the data door
with, with the data stuff you had fromyour economics degree, and then you, you
upscaled after you were already there.
So that way you can, you can become a dataanalyst or sorry, become a better data
analyst, you know, have a bigger impactat your company, uh, hopefully get, get
compensated more and better because of it.
Um, but I love that you did that.
After you get you started, basically,
Thu Vu (23:23):
yeah, yeah, definitely.
And, and I think this is,uh, you're totally right.
It's so much easier when you get aninternship or you get a, like a really
beginner, uh, like an entry leveljob in data science or data analysis.
Even like us, just a small, uh, portionof your job is, uh, data related.
You can always like show it a littlebit more that you have some experience.
(23:47):
And this is really a big advantage.
So, yeah, I would always advise anyoneto, when they start, just think, uh, step
by step and, uh, take anything that youmay find, like, you can learn something,
um, regarding the data skills, and thenyou can go, uh, can move on from there.
That's so much easier, indeed.
Avery (24:09):
One of, one of your latest videos, you explored data trends, um, and you
found some pretty interesting things,uh, that was going on with the data
job market, the tech market in general,what was like your favorite trend that
you kind of discovered in this video?
Thu Vu (24:22):
Yeah.
Yeah.
I think the favorite trend for me is like.
The new development, when you thinkabout like technical skills, I find
that like Python is, has been reallyso become so much more ubiquitous.
So, so much more universalcompared to a few years ago.
I think definitely a few years ago, itwas like, uh, for the discord analysis or
(24:47):
some particular software, like SAS, evenif you ever, uh, even ever used to use it.
But right now, also within my work,a lot of, in a lot of projects,
we are migrating all the codebase from SAS, from R to Python.
So it was like a nice.
An interesting observation, and especiallywith the development of AI right now,
(25:11):
Python is supporting a lot of cool tools.
For example, like, um, uh, things likelang chain and all these different
frameworks to create, um, an AIpowered application, an AI agent, all
these frameworks are all in Python.
Yeah, that, that, that's reallylike a Uh, yeah, like a really cool
(25:33):
thing to, to, to, um, to recognize.
Further, I think there's also someinteresting trends that I noticed, um,
in kind of like the freelancing space.
It seems, it seems like, They'remore freelancing jobs than right
now, than, than a few years ago.
(25:53):
And I'm not sure why, but I feel likecompanies are more like, probably they
are experimenting with things a lot.
And that's why you see.
Probably some of them have a littlebit budget, uh, or even individuals
or small business owners, they havea little bit budget and they want to
hire someone to do something for them.
(26:14):
I recently have a friend who worked alot on, um, uh, who knows a lot on RAC,
so, um, uh, retrieval, uh, augmentedgeneration kind of projects using LLMs.
And then, um, yeah, so thatperson connects it to me.
Uh, asking, like, do you have someoneor you can help me with, uh, uh,
(26:34):
building and, uh, kind of like a tool toextract this and that information from
like, uh, a hundred PDFs that he has.
And so, yeah, so I introduced my friendto, uh, to that, um, to that person to,
uh, to help him with, with this task.
And I think this is also kind of like anexample of like how people are recognizing
(26:55):
the role of, uh, AI and automation.
And they want to get some, something done.
And so, yeah, it doesn't need tobe a fixed contract, a fixed job.
It's more like a experiment sometimes.
And so, yeah, it's a, I find italso really interesting and I keep
thinking about how, uh, how peoplecan find these kind of projects.
(27:16):
Uh, they can, yeah, like people whoneed to get things done and people who
has the skill, how can they, uh, meeteach other more often or how they can
more effectively, uh, meet it, uh,like kind of like, um, come across each
other's, uh, and connect to each other.
Yeah, that there are the two trendsthat I, yeah, that I really like.
And I also kind of like got a bitsurprised, but also not so surprised,
(27:40):
uh, how, how that, how, yeah.
Avery (27:43):
It's fascinating.
We live in a really excitingtime where you can start a side
hustle or start your own business.
That's, that's what I did threeyears ago, three and a half years
ago was I started to freelance and Istarted to make more money freelancing
than I did in my regular job.
And I was like, okay, I'mjust going to do that.
Oh, really?
Yeah.
Yeah.
That's why I left Exxon was to start doingfreelance projects and start an agency.
(28:04):
Yeah.
I ended up switching mostlyto teaching because I figured
out I really enjoy teaching.
So that's what I do.
Full time now pretty much.
Um, but yeah, the freelancingstuff is super fascinating and I
think there's a great opportunity,uh, for people to get into that.
Uh, and I also love that you, youbrought up the, the Python trend.
I think it just became, youhad mentioned that video.
It just became the most common ormost frequently used language on
(28:25):
GitHub, uh, which was a big deal.
Um, so Python, definitelya thing of the future.
Another trend, uh, that I reallyliked, especially since I helped
people land their first day ata job is you looked at like.
The number of data jobsover the last few years.
And if like, we've seen a lot of layoffsor if we've seen a decrease in jobs,
because you know, a lot of people arelike, Oh, the economy kind of stinks.
(28:45):
And you know, the jobmarket's really bad right now.
Uh, and your conclusion was, you know,maybe it's not as bad as people might say.
The, the, the graph was kind of a littlebit downward in terms of like number
of jobs, but it was relatively flat.
And that's actually, I did.
Yeah.
Uh, a similar video recently where Ilooked at, um, the growth of, of data
jobs from a different data source and thedata source you used and basically came
(29:08):
to the same conclusion that the, if youcompared it to 2019, uh, data job openings
were up specifically for like dataanalysts were still up around like 20%.
But it was year over year,but it was a flat 20 percent
for like the last year or two.
So I was really comforted tosee, like, you kind of came to a
similar conclusion that I did witha totally different, uh, data set,
(29:30):
completely independent of each other.
Thu Vu (29:32):
Oh, that's really cool.
Um, that that's really cool to see.
Indeed.
Um, when I was using, yeah, Iactually use the, uh, kind of like
the data from, um, from, from, uh,collected by Luke, uh, Luke Burrus.
And, uh, yeah, he's the man behindall this, like, web scraping stuff.
And, uh, I also, I was also a bitdoubting, uh, I didn't want to
(29:52):
make a conclusion that, oh, this islike decreasing that we are seeing.
Indeed, it's more like flattened outand, uh, depending on how you see it.
Um, and as you say, it's more likea, uh, glass half full or empty.
You, yeah, like it's quite, Ithink it's quite normal to see some
fluctuation over the year over year.
(30:13):
And, uh, it's, uh, it's definitely,yeah, I don't think it's something that
I would worry about, but more like,uh, what kind of jobs are being posted?
Like, the job compositions are changingrather than the number of jobs.
I think.
Probably within the same jobtitle, you probably have something
(30:35):
new in the job descriptions.
And I, I didn't, um, really have thechance to really dive into that in,
in that, um, data job trend video.
But I think it would be really cool tosee how the, uh, the job functions or
the job, uh, description is changing.
And how you can maybe learn from that.
What can you prepare to meetthat demand in the future?
(30:59):
I'm sure there will be more like,uh, really things that are more
like, uh, data AI engineering kindof role that are emerging in data
science, in the, like, data science,uh, Uh, machine learning space.
And so, yeah, I, I think, uh, yeah,it's probably like, it's better
to, to, to, um, a little bit, puta little bit like, uh, yeah, yeah.
(31:22):
Take that with a little bit grain of salt.
When you look at the chart, um, probablyit doesn't really tell the full story.
Avery (31:28):
I agree.
It's, it's, it wouldbe really interesting.
That data sets very rich.
Um, but once you get into textanalysis and NLP, you just have
to have more data science skills.
It's like a whole separate.
Part of data science, which justtakes longer to do than things like
counting and line charts and, uh,bar charts and stuff like that.
Um, right, right.
Definitely.
(31:48):
Which, which maybe it's a, it's agreat project, uh, for your Python
for AI projects, uh, group thatyou're doing with, with the course.
So maybe that we'll lookforward to seeing that.
On the curriculum in the future to thankyou so much for being on the podcast.
If you guys haven't checked outher channel, please go do so.
Now we'll have a link to it in theshow notes down below, as well as
(32:10):
her Python for AI projects too.
Thank you so much for being on the show.
Thu Vu (32:13):
Yeah.
Thank you so much for having me here.
I agree.
And yeah, it was a great pleasureto meet you here on this podcast.
Avery (32:20):
Same.
None of the projects that I posted on my channel, I knew
beforehand that it would work.
It was just sometimesit's completely absurd.
And I thought, yeah, like,how could I make it work?
And then several days, like tinkeringwith my code and try to like, look at
other tutorials, look up things on StackOverflow and see if anyone has any.
(00:20):
ever done something like this.
Yeah.
So it's also a lot oflike findings for me.
Sometimes you have to be creativeand solve your own challenge and your
own problems because yeah, you alwaysencounter something in your project.
The good mindset is just, uh,like there's got to be a solution.
So don't give up.
When you first see an erroror see like a problem.
Avery (00:42):
All right.
If you are watching on YouTube oryou've ever looked up data analytics on
YouTube, you've probably seen, uh, TuVu, our guest today, one of her videos,
because they are absolutely amazing.
Uh, in the past, she's been a dataanalytics consultant with companies
like PWC, uh, and she's a prolific.
Content creator in the data analyticsspace to welcome to the podcast.
Thu Vu (01:03):
Thanks, Abby.
It's, it's really, reallya great introduction.
So kind of you.
Of course,
Avery (01:08):
it's
Thu Vu (01:08):
my pleasure
Avery (01:09):
to be here.
Of course.
I'm so glad to have you here.
One thing that I love about your videos,uh, is you do some pretty cool projects.
Uh, you do some pretty cool.
Things with, with ai, things withmachine learning, things with just data
analytics and data science in general.
And I think we need more ofthat on YouTube, more like
actual projects being done.
I think you do a great job of doing that.
Thu Vu (01:29):
Yeah, absolutely.
I think, um, people talk about datascience or machine learning or AI a
lot, but I think what I personallymissed, it was like some kind of
like a hands-on demonstration of howyou're gonna use a new technology,
let's say, uh, like a, a network.
analytics or some AI, some cool AImodels or large language models, how
(01:52):
you can apply it to your own problemand also demonstrate it like end to end
almost, um, how you start with an idea,how you get, um, inspired, how, uh, how
you think about the problem, how youframe it and how you Kind of go step by
step, explore it further and further.
And then in the end you have somethingthat you can show to other people.
(02:13):
And hopefully it's a little bit usefuland um, yeah, hopefully you have fun.
So that is kind of the idea that I kindof like, yeah, it was not, The first
thing that I, um, actually startedwhen I, um, yeah, when I started
making videos on YouTube, um, it justoccurred to me that people really
liked it and people really appreciatethe effort to think something so much,
(02:38):
uh, yeah, think through, uh, some, aparticular topic in such a great detail.
Also kind of hopefully inspire otheruse cases for people to try out.
Yeah, so that, that waskind of like the motivation.
And up until now, that's kindof like, that is the type of
video that I really like making,although it takes a lot of effort.
(03:00):
And of course, like I lostsome hair because of it, but
yeah, it was fun and hopefullyhelpful for other people as well.
Avery (03:08):
I think so.
And I think it's very impressive becauseit's not easy to make technical videos and
make technical videos engaging for likea YouTube audience, which, uh, I think a
lot of people watch a video for like 35seconds and then skip to the next one.
So your, your ability to, to kind ofcapture these technical things in a, you
know, shorter video, but not like tooshort, uh, I think is very impressive.
(03:29):
Uh, have you always enjoyedmaking fun projects like this?
Thu Vu (03:33):
I think at the beginning, it was kind of a struggle.
I think it was like, yeah, I think withouta lot of, like, experience with, uh,
you know, like making tutorials, it wasalso kind of like always like climbing
such a, like a big mountain every time.
Like how you talk through everything,how you explain everything, every
little step that you make, uh, makingthe, like the screen recording and also
(03:55):
kind of like, So kind of like nice Brolls, you know, like in YouTube, you
have like all these kind of like funthing that you film yourself and then
combine it in, in like a good storyline.
I think that that was kindof a struggle for me in data
science or machine learning.
You can always find some kindof like a project in terms of a
blog post or, um, like a Jupyternotebook or a GitHub repository.
(04:18):
And that, um, yeah, that's usuallyhow people think about these projects.
But like, how you present it in a video inan engaging way, I think it was like the,
the biggest, yeah, it was a challenge.
At the beginning I was scared.
I was totally like, I, I didn't really,I could not really enjoy filming myself
(04:38):
and like make such a complex explanation.
I was kind of uncomfortable with it.
At the start, but itgot better and better.
I know how to kind of like preparemy scripts, how to kind of like,
think about it, maybe a few days,come up, come back to the project
and refine what I want to tell.
And, uh, it, the workflow gets better.
(04:59):
Better and better.
Uh, so it's also less scary and I'malso not a native English speaker.
So sometimes there are a lotof concepts I want to explain,
uh, and I like words for it.
I love the way to, to explain it.
And that's also frustrated.
Uh, but yeah, it's, uh, yeah, all ofthese struggles, I think it's kind of
like, it's worth the effort for me and Ithink it's really rewarding to, to create.
(05:24):
That's kind of my, uh, likehow, how it went for me.
Avery (05:27):
It's, it's really impressive, especially, especially like
in a, in a second or, or howmany languages do you speak?
Thu Vu (05:33):
I speak, uh, Vietnamese as a, uh, as a mother tongue and my, uh, yeah,
definitely the next language is English.
And I also speak Dutch becauseI've been living in the
Netherlands for the last 10 years.
That's so
Avery (05:50):
impressive.
That's so impressive to be creating thisgood of content in your second language.
Now let's talk about a littlebit more about projects.
So one of the cool things that you'vedone is you've built a project to analyze
your finances, uh, with like a local LLMs.
Like you basically created your,your own version of like chat GPT to
specifically look at your finances.
(06:12):
Now that's crazy because.
Like I think most people wouldprobably just like do something
easier to do that, but you'relike, no, I want to make it cool.
I want to make it hard.
Have you always been interestedin like creating kind of cool
personal pet projects like this?
Yeah.
Yeah.
Thu Vu (06:25):
Yeah.
Thanks for pointing it out.
I think that's also kind of like oneof the projects that I got some really,
like some people saying on YouTube.
Oh, like you, you just invented somethingthat was completely unnecessary because
like on the, like the bank banking appthat you are using, probably you also have
kind of the insights feature where youcan also have the same, do the same thing.
(06:47):
But yeah, yeah, I know about thatfeature, but I was like, huh, how can
I use, uh, an LLM to help me with this?
And because I download my bank statementsall the time, uh, and I like in.
Yeah, I just like looking atit myself and see in detail
what kind of expenses I make.
Yeah, so that was thestart of the challenge.
(07:07):
And, uh, yeah, that video got a lotof, uh, nice, um, uh, yeah, nice
feedback because I think peoplereally like to have feedback.
Something that is a bit private,like, uh, when you have an LLM
and you, uh, you cannot post yourblank statements on ChatGBT or
Cloud AI to help you analyze it.
(07:28):
So yeah, like using a local LLM isa great way to kind of like test out
this idea and see how well it works.
it might work.
And I also have, yeah, it waslike a, like a trial and error.
I also didn't know if it wouldwork, um, at the beginning.
I, I think, yeah, none of the projectsthat I posted on my channel, I
(07:49):
knew beforehand that it would work.
It was just sometimesit's completely absurd.
And I thought, yeah, like,how could I make it work?
Uh, and then several days, liketinkering with my code and try
to like, look at other tutorials.
tutorials, uh, look up, uh, thingson Stack Overflow and see if anyone
has ever done something like this.
(08:11):
Yeah.
So it's also a lot oflike findings for me.
Sometimes you have to becreative and solve your own
challenge and your own problems.
Um, because yeah, you alwaysencounter something, uh, in your
project and, um, the good mindsetis just, uh, like there's got to be.
And a solution.
So don't give up when you firstsee an error or see like a problem.
(08:33):
And that project definitely didn't workwell at the beginning because I know
like the LLM was was really unreliable.
So I had to like changethe temperature for the.
LLM, and then tweak something in theworkflow and try to validate the output of
the LLM with Pydantic and all these kindof things, uh, just for a toy project.
(08:56):
So I was like, yeah, it was a lot of work.
Um, but it was, it was fun.
Um, yeah.
Yeah, I think nowadays there are somany new frameworks that help you do
these kind of projects, maybe likein an easier way, or like, um, Python
packages that you can use, I think,like, instruct, um, like some new
(09:17):
Python packages that lets you outputthings from LLM in a structured format.
That is something that I onlyknew later, but that was much
later after I posted that project.
Avery (09:30):
Yeah, I think there's so many good things that people can take from,
from what you just said, because I thinkoftentimes people, you know, look, look at
you and maybe look at me and they're like,Oh, these people are experts with data.
They know what they're doing.
And the truth is that no oneactually really knows a hundred
percent what they're doing in dataever because it's ever evolving.
It's ever expanding.
Uh, there's always new things.
(09:51):
And.
Uh, you know, I, I can't, I mean, Iguess I can speak for you cause you
just mentioned this, but like, uh, Iget stuck all the time and it's still
like a process to, to troubleshootand to, like you said, use chat GPT to
try to solve or go to stack overflowand, and get through those problems.
So even though you're creating thesevideos, you've done a lot of them,
you have like, like a decade ofexperience, you're still getting stuck
and you still have to troubleshoot.
Thu Vu (10:12):
Yeah, exactly.
No, I, I think anyone cando this kind of projects.
Um, yeah, given that you putin the time and put a little
bit effort and some patience.
Um, and I've seen also areally, really cool project on
your, on your channel as well.
And I, I thought like you really put a lotof attention to all these details on the
(10:34):
videos or visualizations that you've made.
I thought like, yeah, it's really.
Really cool.
Yeah.
Maybe something sometimes I just thought,okay, I can create this visualization
of like, um, you know, animatingsomething just, just for the fun of it.
Uh, and you did it sometimes.
And I thought, Oh, like you have somereally, uh, really great, uh, insights
(10:54):
on how you can show things differently.
And so I think, yeah, like, yeah, withmaking YouTube, it's, it's really fun
to look at other people's, um, workand see how you can learn from them.
And yeah.
And I guess for many people as well,uh, in the audience, yeah, you can
definitely just like sometimes comeacross something on YouTube and then you
(11:17):
thought, huh, I can maybe do this as well.
Um, yeah, so that's a great wayto learn from each other as well.
And it's definitely, I'm not a,yeah, know it all kind of person,
definitely in data science or
Avery (11:29):
machine learning.
Well, you definitely know a lot and peoplecan learn from you, uh, a lot and, and
yeah, I've made some project videos.
In the past.
I haven't made anyproject videos recently.
I find them that it appearsthat the YouTube audience
doesn't like them as much.
I know I've, I've spoken toLuke Bruce in the past as well.
And he has like this awesomevideo on his channel where he was
(11:50):
analyzing his mountain bike data.
And I was like, that video rocks.
And he's like, I know, right.
It should have way more views.
And I don't know.
It's, it's, it, I think you dida great job of, of making it
digestible for the YouTube audience.
Cause I think these personal projectswhere Like, for example, I, I looked
at like what states Google the mostevery single hour for like a quarter
of a year, you know, you've done thisanalyzing my financial data with an LLM.
(12:13):
Luke's done the mountain biking one.
I think these projects are fun.
And I think, I think it's as datascientists or data analysts, like we
want to use data in our real normal life,not just in our work or our business.
So these types of like personalprojects, I think can be really fun.
Thu Vu (12:27):
Yeah, yeah, absolutely.
Um, no, I think, I think you're rightthat, uh, not all the videos that
you make or all the work that youmake would, uh, get the recognition
that you think it deserves.
Yeah, it's, um, it's, it's hardand, uh, I'm sure some people
also post things on LinkedIn.
I also have some friends who, uh,post, um, try to post on LinkedIn more
(12:48):
often, but really like doesn't getmuch views or, uh, like interaction.
And then.
Yeah.
You feel discouraged.
Um, and I'm sure a lot ofpeople also relate to that.
I also don't know how some videosgot, uh, seen and some people, some
videos just got completely tankedand no one really look at it ever.
(13:10):
It's, it's really hard to, to, to kindof like predict that even though, yeah, I
really want to predict it, but yeah, yeah.
I think there's some kind of secrets.
I tried to make the first, um,like the opening of the video.
really engaging.
Like the third, the first 30 seconds orso, uh, that's what I learned from all
the YouTube gurus and, uh, try to kind oflike make the best edits out of it, uh,
(13:34):
and see if people keep watching longer.
And usually they do, but overall thequality of the whole project is the
video is, is, is more important, I guess.
And I hope that is,
Avery (13:45):
that's true.
It's, it's definitely hard.
Um, let's talk about, so like withthese cool projects that we, that we've
been talking about, if people wantto build their own, obviously they
can kind of look at the stuff, youknow, you and I have done on YouTube.
Um, I know that you give you your GitHub,uh, in the description a lot of time,
uh, for most of my projects, you canget all of the GitHub stuff for free.
(14:06):
It's kind of like open source like that.
But you also just recentlycreated something called Python
for AI projects, which basically.
is an opportunity, a platform wherepeople can, you know, build some
pretty cool projects for an AI withPython, kind of with your guidance.
Is that right?
Thu Vu (14:21):
Yeah.
Yeah, that's right.
Um, it's kind of like my, uh, reallymy, I put my heart into this project
because I believe that many peoplestruggle to learn the basics of Python.
Python and AI, because they don'thave the really like the most
beginner friendly kind of, uh,guidance or kind of like a road map.
Um, so that's why I decided to createthis, uh, this giant kind of like, uh,
(14:47):
curriculum that teach people Python fromscratch, all the fundamentals, and also
hands on stuff on how to learn, how to,how to use Visual Studio Code, how to
use AI assistance for your work, and alsolearn the basics of machine learning,
deep learning, and AI, and with someproject walkthroughs as well for people
(15:11):
to really follow and create their ownprojects with kind of like the idea.
Yeah, using the, uh, large languagemodels and kind of like, uh, yeah,
just like some projects I did also onmy YouTube channel, um, one projects
about extracting information, uh, fromPDFs using, uh, large language models
(15:31):
and how you structure it in a niceformat in a nice, uh, table format.
And also, yeah, I'm also planning toadd a few more advanced projects like
fine tuning and LLM and all these thingsthat, yeah, I also kind of like, I really
always wanted to do it also myself.
(15:53):
Now it's also an opportunity to kindof like explore it further and help
other people to learn them as well.
Avery (15:59):
Very cool.
I, I might have to check that out becauseyeah, I definitely, I don't know how to
do anything with like an LLM from scratch.
So there's some, there's probably somethings in there, uh, that I could learn.
So that would be a lot of fun.
We'll have the, uh, the linkin the show notes down below.
Thu Vu (16:14):
Yeah, definitely.
Yeah.
No, I, I think, uh, well, you, you, youknow, Python and you, yeah, probably
you can pick it up very quickly and,uh, all the machine learning AI stuff.
Uh, I teach someone as really likesomeone who has never worked, never
built a machine learning model before.
Try to teach the, like the fundamentalsand the building blocks for you
(16:37):
probably is, is much less relevant.
Um, but yeah.
That's, uh, indeed.
Yeah.
That's kind of my project
Avery (16:45):
at the moment.
Very cool.
I can tell that you're, you'rereally excited and passionate about
it, uh, which I think is very cool.
Um, we've talked a lot about projectsand, and your, your great YouTube
channel, and I've kind of given alittle bit of your background, but,
uh, I'm guessing a lot of peoplelistening don't a hundred percent know.
Uh, your, your background.
So could you just tell us like whatyou studied in school and then maybe
what your first job was out of school?
Thu Vu (17:06):
Uh, yeah, yeah.
Thanks for, for asking about this.
I think, uh, yeah, I, I alsohaven't really shared about
it a lot on my channel.
Um, my, about my background.
So if
Avery (17:19):
you don't want to talk about it, we don't have to just so you know.
Thu Vu (17:21):
Oh no, no, no.
Of course.
Uh, no, of course I can talk about it.
Yeah,
Avery (17:25):
so don't need
Thu Vu (17:26):
to add it in an edit.
Um, yeah, so I, uh, yeah, somy first, uh, degree that, um,
at school is, uh, economics.
And, um, back then I was, yeah,I was still living in Vietnam.
Um, and I got my bachelor in economics andthen I go to, I went to the Netherlands
(17:47):
to study a master, uh, in economics.
as well.
So this is, yeah, it was kind oflike a very, uh, theoretical degree.
And, uh, although, yeah, you learnsome basic stuff, econometrics,
um, linear regression, whichwere like basic statistics.
And that was quite useful later on as a,like a, when I would start working as a
(18:08):
data analyst and then learning a bit moredata science y stuff, machine learning.
And that was in 2015.
So I moved to the Netherlands when I wasYeah, around, yeah, 22 ish, um, back then.
I started working in the Netherlandsand stayed, um, decided to stay,
(18:28):
uh, even though it was reallynot the first plan, uh, when I
moved to the Netherlands to study.
Um, but, yeah.
I found out that it was likereally, really a great country.
And I fell in love with the culture,the food, not so much the weather,
not so much, but people were great.
The working environment was really, reallytransparent, really nice, very efficient.
(18:52):
And also people are very direct.
And I really liked the way that,you know, people are honest to each
other and, um, and, uh, Yeah, you,they give you really straightforward
feedback that you can improve on.
When I start my internship, uh, afterright after my master's, I felt really, I
felt really good about, uh, working here.
(19:12):
Um, so that's how Istarted my career actually.
And I started working as adata, kind of like a research
assistant in my internship.
And then later I got a joboffer as a data analyst.
So I got really lucky to, you know,Actually start in this career because at
the beginning when I learned economics,the thing that I would think about was
(19:36):
more like a researcher or maybe workingin policy, working in maybe a little
bit like even started a PhD and thatI even applied for a PhD in Amsterdam.
I still remember.
And thank God I didn't, I didn't got it.
I didn't get it.
, uh, otherwise I would be like, Idon't know where I would be right now.
(19:57):
And yeah.
And a few, yeah, a few years later Istart working at BWC, um, Pricewaterhouse
Coopers and I start working asa, a consultant for six months.
Six years.
There, I also learned a lot ofdifferent, uh, new things and worked
for different, in different projectsand I found it really incredibly,
(20:20):
um, really, uh, I learned so much.
Um, it was really helpful to workwith so many different people and
you pick up new things every time.
Yeah, and that when I was working thereat BWC, I decided to learn a bachelor.
degree in computer science, Idecided to take it because I feel
like I still missed something.
(20:42):
I, my technical skills were still kindof like not so, I was not so confident.
I was, I was still like, yeah, I wasprobably, you know, like an imposter
feeling and also the drive to learn morein a more kind of like structured way.
Um, that's how I decidedto take the degree.
(21:03):
It's an online degree that youcan take via Coursera, actually.
It's very nice.
Yeah, it's a, it was alot of work, actually.
It was a master, um, bachelor degreewith, I don't know, 22 modules.
And yeah, I still have the finalproject that I have to finish.
Um, so it was in the end, it was likesix years now that I haven't finished.
(21:26):
So I still feel ashamed when I talk aboutit, but yeah, uh, in the end, yeah, I
work on, uh, the YouTube channel a lotand, uh, it was all kind of like all
go into each other, uh, kind of, yeah.
So that's kind of like my, my, my,work and my personal history and
(21:46):
like my, uh, yeah, my story so far.
Avery (21:51):
Can you just, can you just submit a URL to, of your YouTube
channel to the degree and justbe like, here's my final project.
Thu Vu (22:00):
Yeah.
Yeah.
Yeah.
Like for like my own project or
Avery (22:05):
just like your whole, your whole YouTube channel.
I feel like that shouldcount as your final project.
I feel like they should, they should,uh, give you, give you credit for
that because you've done some,some pretty cool things on there.
Thu Vu (22:17):
Yeah, that's a great idea.
I will, I will try it out.
Avery (22:20):
That's, that's great.
Yeah, I think it's, I think it's onething that's, uh, I want to just pull from
your, your story there, uh, was you goingback to school once you had a data job.
And one of the things I try to, I,I try to help people who are like
brand new to data and who likewant to become a data analyst.
And obviously going to back backto school is always an option.
(22:40):
Um, but a lot of the times if you getyour foot in the door first with any
sort of data job at the beginning, it'sgoing to be so much easier to go back
to school for a variety of reasons.
And so like a lot of the cool thingsthat you do, like, like the LLM
stuff, uh, use Docker in that video.
A lot of that stuff is, is thingsthat you don't necessarily need
when you first land your data job,but, but they can help you become a
better data analyst down the road.
(23:02):
And so I kind of like how you, you kindof gotten your foot in the data door
with, with the data stuff you had fromyour economics degree, and then you, you
upscaled after you were already there.
So that way you can, you can become a dataanalyst or sorry, become a better data
analyst, you know, have a bigger impactat your company, uh, hopefully get, get
compensated more and better because of it.
Um, but I love that you did that.
After you get you started, basically,
Thu Vu (23:23):
yeah, yeah, definitely.
And, and I think this is,uh, you're totally right.
It's so much easier when you get aninternship or you get a, like a really
beginner, uh, like an entry leveljob in data science or data analysis.
Even like us, just a small, uh, portionof your job is, uh, data related.
You can always like show it a littlebit more that you have some experience.
(23:47):
And this is really a big advantage.
So, yeah, I would always advise anyoneto, when they start, just think, uh, step
by step and, uh, take anything that youmay find, like, you can learn something,
um, regarding the data skills, and thenyou can go, uh, can move on from there.
That's so much easier, indeed.
Avery (24:09):
One of, one of your latest videos, you explored data trends, um, and you
found some pretty interesting things,uh, that was going on with the data
job market, the tech market in general,what was like your favorite trend that
you kind of discovered in this video?
Thu Vu (24:22):
Yeah.
Yeah.
I think the favorite trend for me is like.
The new development, when you thinkabout like technical skills, I find
that like Python is, has been reallyso become so much more ubiquitous.
So, so much more universalcompared to a few years ago.
I think definitely a few years ago, itwas like, uh, for the discord analysis or
(24:47):
some particular software, like SAS, evenif you ever, uh, even ever used to use it.
But right now, also within my work,a lot of, in a lot of projects,
we are migrating all the codebase from SAS, from R to Python.
So it was like a nice.
An interesting observation, and especiallywith the development of AI right now,
(25:11):
Python is supporting a lot of cool tools.
For example, like, um, uh, things likelang chain and all these different
frameworks to create, um, an AIpowered application, an AI agent, all
these frameworks are all in Python.
Yeah, that, that, that's reallylike a Uh, yeah, like a really cool
(25:33):
thing to, to, to, um, to recognize.
Further, I think there's also someinteresting trends that I noticed, um,
in kind of like the freelancing space.
It seems, it seems like, They'remore freelancing jobs than right
now, than, than a few years ago.
(25:53):
And I'm not sure why, but I feel likecompanies are more like, probably they
are experimenting with things a lot.
And that's why you see.
Probably some of them have a littlebit budget, uh, or even individuals
or small business owners, they havea little bit budget and they want to
hire someone to do something for them.
(26:14):
I recently have a friend who worked alot on, um, uh, who knows a lot on RAC,
so, um, uh, retrieval, uh, augmentedgeneration kind of projects using LLMs.
And then, um, yeah, so thatperson connects it to me.
Uh, asking, like, do you have someoneor you can help me with, uh, uh,
(26:34):
building and, uh, kind of like a tool toextract this and that information from
like, uh, a hundred PDFs that he has.
And so, yeah, so I introduced my friendto, uh, to that, um, to that person to,
uh, to help him with, with this task.
And I think this is also kind of like anexample of like how people are recognizing
(26:55):
the role of, uh, AI and automation.
And they want to get some, something done.
And so, yeah, it doesn't need tobe a fixed contract, a fixed job.
It's more like a experiment sometimes.
And so, yeah, it's a, I find italso really interesting and I keep
thinking about how, uh, how peoplecan find these kind of projects.
(27:16):
Uh, they can, yeah, like people whoneed to get things done and people who
has the skill, how can they, uh, meeteach other more often or how they can
more effectively, uh, meet it, uh,like kind of like, um, come across each
other's, uh, and connect to each other.
Yeah, that there are the two trendsthat I, yeah, that I really like.
And I also kind of like got a bitsurprised, but also not so surprised,
(27:40):
uh, how, how that, how, yeah.
Avery (27:43):
It's fascinating.
We live in a really excitingtime where you can start a side
hustle or start your own business.
That's, that's what I did threeyears ago, three and a half years
ago was I started to freelance and Istarted to make more money freelancing
than I did in my regular job.
And I was like, okay, I'mjust going to do that.
Oh, really?
Yeah.
Yeah.
That's why I left Exxon was to start doingfreelance projects and start an agency.
(28:04):
Yeah.
I ended up switching mostlyto teaching because I figured
out I really enjoy teaching.
So that's what I do.
Full time now pretty much.
Um, but yeah, the freelancingstuff is super fascinating and I
think there's a great opportunity,uh, for people to get into that.
Uh, and I also love that you, youbrought up the, the Python trend.
I think it just became, youhad mentioned that video.
It just became the most common ormost frequently used language on
(28:25):
GitHub, uh, which was a big deal.
Um, so Python, definitelya thing of the future.
Another trend, uh, that I reallyliked, especially since I helped
people land their first day ata job is you looked at like.
The number of data jobsover the last few years.
And if like, we've seen a lot of layoffsor if we've seen a decrease in jobs,
because you know, a lot of people arelike, Oh, the economy kind of stinks.
(28:45):
And you know, the jobmarket's really bad right now.
Uh, and your conclusion was, you know,maybe it's not as bad as people might say.
The, the, the graph was kind of a littlebit downward in terms of like number
of jobs, but it was relatively flat.
And that's actually, I did.
Yeah.
Uh, a similar video recently where Ilooked at, um, the growth of, of data
jobs from a different data source and thedata source you used and basically came
(29:08):
to the same conclusion that the, if youcompared it to 2019, uh, data job openings
were up specifically for like dataanalysts were still up around like 20%.
But it was year over year,but it was a flat 20 percent
for like the last year or two.
So I was really comforted tosee, like, you kind of came to a
similar conclusion that I did witha totally different, uh, data set,
(29:30):
completely independent of each other.
Thu Vu (29:32):
Oh, that's really cool.
Um, that that's really cool to see.
Indeed.
Um, when I was using, yeah, Iactually use the, uh, kind of like
the data from, um, from, from, uh,collected by Luke, uh, Luke Burrus.
And, uh, yeah, he's the man behindall this, like, web scraping stuff.
And, uh, I also, I was also a bitdoubting, uh, I didn't want to
(29:52):
make a conclusion that, oh, this islike decreasing that we are seeing.
Indeed, it's more like flattened outand, uh, depending on how you see it.
Um, and as you say, it's more likea, uh, glass half full or empty.
You, yeah, like it's quite, Ithink it's quite normal to see some
fluctuation over the year over year.
(30:13):
And, uh, it's, uh, it's definitely,yeah, I don't think it's something that
I would worry about, but more like,uh, what kind of jobs are being posted?
Like, the job compositions are changingrather than the number of jobs.
I think.
Probably within the same jobtitle, you probably have something
(30:35):
new in the job descriptions.
And I, I didn't, um, really have thechance to really dive into that in,
in that, um, data job trend video.
But I think it would be really cool tosee how the, uh, the job functions or
the job, uh, description is changing.
And how you can maybe learn from that.
What can you prepare to meetthat demand in the future?
(30:59):
I'm sure there will be more like,uh, really things that are more
like, uh, data AI engineering kindof role that are emerging in data
science, in the, like, data science,uh, Uh, machine learning space.
And so, yeah, I, I think, uh, yeah,it's probably like, it's better
to, to, to, um, a little bit, puta little bit like, uh, yeah, yeah.
(31:22):
Take that with a little bit grain of salt.
When you look at the chart, um, probablyit doesn't really tell the full story.
Avery (31:28):
I agree.
It's, it's, it wouldbe really interesting.
That data sets very rich.
Um, but once you get into textanalysis and NLP, you just have
to have more data science skills.
It's like a whole separate.
Part of data science, which justtakes longer to do than things like
counting and line charts and, uh,bar charts and stuff like that.
Um, right, right.
Definitely.
(31:48):
Which, which maybe it's a, it's agreat project, uh, for your Python
for AI projects, uh, group thatyou're doing with, with the course.
So maybe that we'll lookforward to seeing that.
On the curriculum in the future to thankyou so much for being on the podcast.
If you guys haven't checked outher channel, please go do so.
Now we'll have a link to it in theshow notes down below, as well as
(32:10):
her Python for AI projects too.
Thank you so much for being on the show.
Thu Vu (32:13):
Yeah.
Thank you so much for having me here.
I agree.
And yeah, it was a great pleasureto meet you here on this podcast.
Avery (32:20):
Same.
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