If you use Excel for something, there is a lot of, you know,
stereotyping that you know, you are so not so good at, you know,
the typical data science profile, that people expect you
to be setting up. Start getting started with
python itself is a big challenge.
I mean, like I'm a python programmer.
I don't want people to, you know, get get angry at you.
But I think the initial friction that you have with python is
really, really hot. But from the beginner to
intermediate stage, you would see very less content on
internet. No, boot camp.
Trains them are no courses. Teachers them.
There is very less content. In the middle space and I think
because of that Gap, a lot of people actually get stuck in the
beginner space. Hello, and welcome to data,
Shadow the podcast on all things data.
This podcast is a series of conversations with experts and
Industry leaders in data. At each week.
We aim to unpack a different compartment of the data
Sudanese. I am your host Catholic Church
that I'm a blogger newspaper, columnist book author, and a
former data and strategy consultant at currently head
analytics and business intelligence for Woody one of
India's largest logistics. Companies.
You can follow me on Twitter at Karthik s that is Kar Phi.
K s and read my blog at. No, Intruder.com.
That is n and p. H, be a.com.
All opinions expressed in his podcast belong to me and my
podcast Pious. And I do not reflect the views
of any organizations. We might be Associated.
Nothing discussing his podcast should be taken as been achieved
for us. My initial plan was to record a
podcast on our versus python and imagined a little fist fight on
it as a guest and I would battle about whether our is more suited
for data science or python overtime, better sense,
prevailed. And I decided to be more
constructive. So, what we have now is a rather
healthy conversation about the merits and demerits of the two
big languages used for doing data sites.
When do you use our? And when do you use Python?
What are the advantages of being proficient in both languages?
How do you integrate both into your workflow?
Our Guest is Abdul Majid, Raja a data scientist at atlassian.
He's a heavy user of both R, and python and a founder of
Bangalore, our users group. He also offers tutorials in
programming, in both languages. So today we will be talking
about our and python probably the two most popularly used
programming languages, statistical packages.
Whatever you call it for analytics.
So before we start, can you sort of take us through your journey
into analytics how you got into it?
And also how you got introduced to both our and to python?
So I come from a digital analytics background.
So I was predominantly into digital analytics and once the
data volume started increasing a lot.
So there was a need for me to use some programming language.
I actually first Learn Python. After I learn python after a
point packages that I wanted to use for digital and index was
easier and are for example, Google analytics Library, making
our mark down reports making our shiny applications for doing
Powers analysis like a Sankey diagram.
So a lot of things became very easy and are so I started slowly
migrating into our Our and then slowly became for me that you
know, using one language was not enough.
So I started keeping a myself with both the languages and
that's how I but formally. I learn python first myself and
then I moved into our to learn on and then I did back and forth
between both the languages and what was your background like
before you got into digital analytics and by 3 and so on
work, in terms of programming where you are programmer
already? If so work, what were you
programming in? I was a trained graduate in
computer science. Ring.
But, you know, as most computer science Engineers.
I was not good at programming when I was at University.
I struggle because there was, there was Java and I didn't
quite like, Java to let the time.
I think I still hate Java. Probably that is why I ended up
in analytics. Otherwise, I would have become,
like a traditional ID programmer.
So, I didn't code. And after my University, I was
trained in Cobol. I didn't like kobali there.
So I left my job as soon as possible.
I can. So this this actually frustrated
me. That's where digital analytics
actually gave me a good impression where It was a time
when you know, analytics was not not like a great thing.
Like people are not, there was no data science at the time and
slowly migrated into the space as the space was growing.
And that's how I moved into our and python.
But initially, if you ask me, I think I ended up in Orange
python only because I didn't like any other programming
language. I think my background is sort of
similar, right? Hi.
Again. I did my undergrad.
In couple decides was asked to do a lot of coding in Java.
And so I decided I don't want to be a programmer.
So it just went to two completely different path later
on. I think when I learn python I
was Okay, they had introduced by thrilled when I was studying,
computer science. Maybe, I might have stayed a
program of because like as a programming language, it's like
he at least in my opinion like far easier to use than than
Java, right? So, so yeah, so that's the that
sort of my background as well. So did you have any other
experience with any other statistical?
The software software's or with Excel or something before you
got into Analytics? Okay.
Axl. I was quite good at Excel all
the time. I think I still think
accelerates quite useful, even if we ran our python programmer,
but I think the problem that I feel in Industry.
I don't know if I'm jumping ahead.
But the problem that I feel is if you use Excel for something,
there is a lot of, you know, stereotyping that you know, you
are so not so good at, you know, the typical data science profile
that people expect you to be, but but I think having Excel
also, as part of your data science, book flow is very
helpful. Not maybe you're not going to
build a dashboard or something. I use a lot of gc8 in my current
profile. This it is quite helpful for me
to, you know, converter small. Like, not every time I'm going
to use ggplot for making charts. So sometimes they should gives
me the flexibility to quickly turn a very powerful
visualization. So I think Excel is still useful
and I used to be using Excel at the time as well.
Okay, I think as we talk about our in Python today, I think
he'll also keep excel in the in the loop because I'm also a big
fan of accelerating. The downside of excel in my
opinion. Is that like you can't take sort
of Automate stuff and you have to do.
I hold that with a win-win with some of the Microsoft tools that
are actually becoming easier now, but but still, I think you
can't automate easily. But in terms of just analyzing
data in terms of this looking at data rate, like the thing with
either our of python, you don't really necessarily need to.
Look at the data. It will forces you to look at
the data. And sometimes that will give you
a lot of insight intact or python doesn't necessarily
provide a Jeremy, Howard. The co-founder of us today.
I was like very well respected in deep Learning Community.
He actually emphasizes a lot in looking at the data.
Even when you are doing deep learning problem.
He says, like, you have to actually look at the raw data
and you know, how the neural network progress is?
So I think your point is very valid in that case.
Yeah, because I mean, one of my first experiences was like
again, it wasn't analytics job. I was asked to do the
forecasting because the company was using Java for all its
coding. Direct like why don't you write
your forecasts programs in Java? I smell a six-month.
Got no. We're in debt.
Certainly someday. I was like, okay, I am going to
try exit. I tried.
Excellent. Within two days to steer by
where I was going wrong and the whole thing was turned upside
down. So now like I mean now that I
mean apart from Excel, I mean I assume you continue to use
Excellence one, but within our and python like coming, do you
have a kind of a preference on your default of what you re what
you use or do you have something?
Like there are certain situations where you use are
certain kind of problem statements where you use Python
like, can you take us through like What do you use in what
context? Yep, so I will just basically
Define the workflow that I follow for data collection from,
you know, the Big Data System, I use equal because it's Park
sequel. So I try to write most of the
things in there, like create like a temporary table for me.
And then for EDM. Mostly, I prefer our and then
for actual modeling if I have to do modeling then machine
learning, I Professor python. The reason why I prefer python
for machine learning is I think pythons APA for machine learning
cycle on jpa. Quite unmatchable.
So there is a very clear, four-step process, Define fit
predict and then evaluate I think tidy model is coming
closer to that in our universe, but still, I think scikit-learn
gives me that ability to if I want to change the model, just
have to change the class that I'm defining at the top.
Then I'll have a new model. So I strongly prefer like there
is a very strong preference of using python specially secured
loan for machine learning. But if you ask me about Eda, I
am one of those people who still a girl with matplotlib for every
simple thing. I have to go online and then
find. So I strongly prefer using our
tidy, which is amazing. So I don't want to, you know,
Miss that opportunity of using tidy was riding pipe pipe Based
on data pipelines, to do some analysis, or Edie.
I prefer the other place where I prefer orders, if you have to
create like a web report, a simple report, not as oversight
report. I'm not talking about, you know,
shiny or I'm not talking about streamlets, but if you want to
repair a like a client-side report like a small panel Such
as a report. Then I think our modern is
again, quite unmanageable there. So, the other option that I have
in Python world is I write something in jupyter Notebook.
I format everything and then I, you know, I export it as HTML.
And then I tried to publish it, but I don't find that hope flow
that interesting or that good. There are a couple of libraries,
like, Fast pages. That is again coming from the
past, not a community. There.
You can write a jupyter notebook and publish it as a blog post,
it's there, but I think the workflow that that you Have an R
is really amazing. So I have a website now.
That's called programming with or.com.
So the entire website is actually created by our maqbool.
I create an arm of the on file. Who should do, the get up, there
is get abortions running and then it publishes it.
I think it's quite seamless and with our connect, if you, if
your organization uses are connecting, our mob down is like
a superpower to share internal internal analysis report.
So that is a place I would strongly prefer on.
Yeah. So why is it the, why do you
think it's got that? I mean, I don't know how much
you like. History of the Dual credit for
two languages or software's, if you were to call it that.
Why do you think like optimized more in terms of like usability
user friendly netting Zone while python if you think about it as
more like sort of in some way developer-friendly, right?
I mean like if you over, because it's easier to integrate into
the into the overall, take workflow and things like that.
So, why do you why do you think this differential thought of?
Yeah, I think it's a very valid point D of Broad and I strongly
think our is developed for people.
Who are not technically strong in mind, I could be wrong.
But if you see people who are using our people from Humanities
people, from social science, people who work in bioscience.
I don't I I don't think they're like, you know, core programmers
like how programmers tend to be but they can get their work done
using their tool to give you an example.
I I actually teach in a local University in Bangalore and I've
tried python before, this is for economic students.
I've tried python before. So these students have never
coded in their life at all. You told them to install a
software as well. Like somebody has to sit next to
them and then tell them it's easy for them to use Facebook,
but it's very hard for them to use any technical because they
have this project in mind. So for them I've seen are they,
you know, learning or much much faster than setting python.
For example, I'll give you example setting up start getting
started with python itself is a big challenge.
I mean like I'm a python programmer.
I don't want people to, you know, get get angry at me but I
think the initial friction that you have with python is really
really hot. In fact, that is why A platforms
like repeal it, you know, has that initial friction reduced
variability is being used in a lot of schools or
internationally, but if you want to set it up on your own machine
as a workflow, if you are, if you're on Mac, do you know which
python to use? Because Mac comes default with
one python that python doesn't help you a lot.
If you install an account or now you have, like, probably three
python on your machine. Do you open an account of prompt
and then try something? Are you open your existing to
Miller and do something? Then when you use Pape, then do
you use paper clip 3? Then you just, you just comment
to this, this range of problems. Then you have like your code
editor, which coded to use, why? Somebody would be very strong
with python by Chomp. Somebody would have a strong
opinion with spider. Somebody would have strong
opinion with visual studio. So at the end of this process,
somebody who tries to learn, let's say like I'm a doctor and
I want to, you know, learn data science for something that I'm
doing at the end of this process.
If I'm a doctor, right? I would probably drop box.
I don't expect anybody to stay at until this point and then
still further. Go ahead and then Learn a
language and our it's quite simple.
Go to our download our go to our studio, download rstudio.
You don't have 10 different versions install it you're up
and running just immediately like next five minutes.
You are up and running. Yeah, actually, provide doctors
in Economist. I think I with some with the
degree in computer science. I finally found it.
Hard to get started by the name of my actions are good.
But I think Amon there was a, I was doing a machine learning
Workshop in collaboration with someone and they did.
I want to do it in Python. And so that Back person actually
taught me bison over to three days, creating bills to pay and
that's how I learnt by. Otherwise are the sort of the,
the initial learning curve, was actually very, very steep for me
that sets and it's like later actually.
I got infected with a few organizations, which were
completely in the in the python world.
Like, so they asked me to use Python there again, what I
remember was like they had virtual machines which I have to
be set up by things like that, which it was a, I had no clue
how to use. And then there was a dozen aw
space where you had Jupiter, but they're like, how do you get
files in and out of that direction, was not very
intuitive and things like that. So even as a as somebody with a
sort of a fairly strong programming do not software,
engineering background, I found it very difficult to kind of
gauge is get started in the started invited.
I very comforted by the lady you were talking about an accord a
spider and things like that. The other thing is that you also
have like within python syntax very linked significantly,
right? Like There is the numpy, some
context, there's a bind us indexed, then they report by 10
p.m. To deck.
And in some places things are, there are some small differences
and you can just end up writing, fairly inefficient, good.
And the thing is even our, as if you think about it, like every
day, you have the tiny worst way of doing things.
And also the base, our way of doing things, which a lot of
people, especially in Academia. I think they still do baits are
in stuff. So while so what happens, I mean
like when you have these were sort of versions literature of,
does it make it? Easier and more user-friendly
for the quarters. Or is it just that like the
communication between coders sort of drops and things like
that? No, I think if you are a, if
you're a border for quite some time, so these kind of changes
are do not trouble you much, but I am but if you're somebody who
is starting new. So like you said that this
there's a DOT notation and python.
There is a function for you you something.
There's a method that you use something else.
There's a an object that you define and even in our old you
have the same problem, right? What is five, operator?
Some people do not understand how they have to buy by
operators. One from, tidy was Universe one
from court. So I think these are these are
the problems that beginners. I dilute phase, but I Only
believe, once you get started, especially if you're a
programmer, who who learns from documentation every time there
is a new release. You you understand.
What is the relief? And I think I diversity team
actually does a great job in communicating that especially if
they're deprecating a function and also any primary for python
for that matter. They also communicate that the
problem arises when most people do not look into it.
Most people when there is a new release for example, the last
week, last week. I think I lost last week.
There was a new cycle own release cycle on one point.
I don't know how many, how many programmers, especially working
in data. Science Community have had a
look at it. I strongly believe data.
Scientists still don't think, like software developers a lot.
I don't know which Universe they live in by the mostly if you
actually see her always, you know, in the race of, you know,
improving accuracy of their model latest algorithm.
In this process. They actually leave out this,
you know, there are certain good practices that software
engineering can actually teach us and we started realizing it.
Very now, like after maturity has increased in this domain.
But still a lot of people actually leave order.
I think that is a problem. Otherwise developer should like
developers who release libraries are doing great job.
It's just that we don't notice it actually just take a little
digression. Oh, so you may think that they
can be, there are things that like data scientists can learn
some software from software doing community.
Right? Like what are the some of those
things? So there are a lot of things
data scientists can learn from software, engineering to start
with one of the problems that I have as I write very bad code
and the way I'm trying to overcome right now is the
Jupiter environment like you can, you can do The dog control
groups has given its a very popular talk about why.
Jupyter notebook is not a great tool for writing a software code
like development. I completely agree with that.
Jupyter. Notebook is is like a repeal
environment. You write something you iterate
on it, you write something in bed, visualization and along
with it and then you can publish a report but using jupyter
notebook only as a final tool, like a lot of people do this,
right? They write, jupyter notebook and
they want when they want a python code.
They wouldn't write a python code from the first.
They'll just go to the Our notebook, download the python
script and then they would try to start, you know, embedding
it. I think that's a very bad
practice. What we can do is, like, what I
started doing is using visual, could use Visual Studio code BS,
code extensively. So we escort has jupyter
notebook. And we have scored.
Let me write a python code as well and the advantages I use vs
codes. Jupyter notebook for any Rebel,
like whatever. I want, iterate small things, I
use it and then I just copy it and then I paste it in the
python script. So I have a decent python script
at the end. So this way I am The term and
not ruining my python script development experience.
At the same time. I'm not losing my ability to
iterate on something. Like if I want to make a chart
and then see how it looks this is this is one thing that I
would I would strongly say. And the second thing that I
think software engineer software developers do a lot is going
through documentation. I think this is what I touched
upon slightly before data science Community seems to be so
obsessed with the new algorithm, you know, like Cutting Edge
tools, but I don't know how many people like if you go online
like you would see lot of people asking questions.
It's like, how do I become data centers?
How do I become data center, but you the initial becoming data
scientist is everybody trying, but from the beginner to
intermediate stage, you would see very less content on
internet. No, boot camp.
Trains them. No courses teaches them.
The there is very less content in the middle space and I think
because of that Gap, a lot of people actually get stuck in the
beginner space. They are only searching for job
and then they never move ahead. And that again leads to a lot of
bad code that we end up writing. Technical debt is a big problem
in order. When we write jupyter notebook,
that nobody can understand. A lot of people still do not lay
out their dependencies properly. Like, if you are using python
for that matter, a lot of people don't have dependencies filed,
right? If you have a library
dependency, what is the library version?
We still do not know if there is a vulnerability, then we are
upgrading. Is there any truck?
We don't know. So a lot of these problems come
in because we just dumped into directly, a jupyter notebook,
and then we don't do anything before.
I think like these are a couple of things.
There are a lot of things actually enjoy gross.
Talk about By Jupiter notebook is bad for software development.
Will actually tell you a lot more.
Okay, I think we're learning to that dock in the in the show
notes. So that liquor lives in can go
and check it out. And you mentioned about online
support in things like that. So but what if the in the two
years with python, what I found is that them, especially in
terms of like your online, help stack Overflow and so on the
support for python seal much harder than the our support for
our it was very difficult to there.
For example, some day. I was like coming from the art
world. I was like, aww, Metadata play
with python. I asked them some of the people
I was working with and they didn't understand what making a
data frame needs. And then like that overflow
again, like it was very difficult to our, it gave either
like some are links or something, but nothing related
to Panda. So is this is my perception of
the online support of the two things, correct?
And if so, why do you think that regarding online support?
I strongly believe python has a better support in terms of, you
know, the because of because of the nature of the Language,
which is very, you know, data scientist.
The support for python is really good.
I would say. But the problem arises, when
some are Developer actually comes to python world and then
try to use the same keywords that they have used to used to
use our. So, for example, melt and
reshape is not melt in Python. So melt melt is like, mint
different likely, voter and pivoting.
I think that is first Gap, and the second thing if you see
again, python does not want to enter python, right?
For example are tiny. Tiny was one of the complaints
that people make about. Videos that are studio is
tightly controlling by diverse, but because they are tightly
controlling tidy, was everything in Thai.
Diverse has you know some unified theme, but that does not
want how it is in Python Universe, right?
Soap, and houses from a different group, numpy is from a
different group. Like you learn is from a
different group, even though if all of these are like in part of
the scientific stack, so it is all from different different
entities or different developers.
So that makes it slightly different from what they have.
They have been using and that is why you Don't see that
uniformity there. When, so what do you do?
I mean, I is in our user. Let's say I do I do who seek for
help in pythons are assuming I use the right keywords.
Like, I use the keywords that are more relevant to python.
What is the, what is, is Googling till the answer and
Googling for and get going to stack?
Overflow links to the underworld?
Is it, that python have a different kind of a support
system on that? Yeah.
So mostly these days I use DuckDuckGo only for one reason.
So I think Google's answers are mostly car.
With a lot of, you know, SEO strong SEO blog post.
So I use DuckDuckGo for that purpose.
Lot of blocks are not optimized for DuckDuckGo.
At least II, think. So I use DuckDuckGo and mostly,
I go to the stack, Overflow answer.
And I would, at least go to couple of answers.
One of the problems that you would probably see in Python is
most of the times. If you go to the top answered
stack, Overflow answer, you would see some answer, that is
not very recent and that is probably because this feature
was not available, you know, When five years back, a few
years back, when somebody question, and somebody had given
an answer to that and somebody Mark the dancer was, right.
So, I would strongly encourage anybody to actually go through a
couple of answers below. That also, to understand if
there is a new feature that has been added to give you an
example, one of my stock or blue answers in our community, how to
fill missing values. So, Phil underscore n, a is a
function that was added in tidy, Versa lighting.
I think a couple like in last within last two years.
If you go to that stack, Overflow question, you would see
a lot of answers that has nothing to do.
This, but this is the easiest way.
And once I added this answer, I think probably this is my most
uploaded answer or something because a lot of people started
then seeing this answer as one of the best answers, but still
this would not be marked as answer.
So anybody who's looking for an answer on stack Overflow?
I think not just the mark dancer.
They should scroll down and he because all these libraries keep
on enhancing things. And when win, this is being
added as new answer, lot of beginners might or even
practitioners might miss out that the most efficient way of
doing this. Just by looking at, you know,
some for your five-year-old. Super and one other thing.
I think you touched upon research some time back.
So again, like as you as you were, everybody else knows like
I come from the art world and have sort of exterior
experimented with python and sort of gone back.
So my big so I'm very visually the way I think.
So if somebody sends me a large Excel sheet unless it's probably
sort of properly formatted with conditional, formatting and
things are, it's very difficult for me to kind of really make
sense of it. So the way I make sense of data
is to sort of plotted graphically.
And if on the knotting support in Python to be like, fairly
weak in some sense. I mean, it's again.
So, how do you kind of navigate this sort of difference in in
visualizations between the two platforms?
Or is it that? Like if you are heavy on
analysis and visualization you should just use our Yeah, I feel
the thing that you just mentioned lost and I do that.
So every platform has an advantage and disadvantages in
that way. I strongly believe, like if you
want to do very extensive video, at least I am very comfortable
in doing that in our I'll give an example.
So I tried plotly. When just plotly, was there like
a couple of years back. So you have to build go object?
You have to do, you know, like the code itself.
Looks like a Json script or something.
So I completely disregarded, plotly, but now, plotly has come
up with plotly Express, which they are calling.
I was inspired by ggplot now, for every single plot.
You have the same syntax, you have data frame.
You have x axis, you have y axis at couple of other attribute,
you don't touch any of us are a couple of other arguments.
You don't touch any of the other arguments.
And just with three data frame, XY you can make a bar chart.
You can convert that bar, chart to the scatter plot.
You can do all of these things and that is how very much, you
know, ggplot used to look right or at least till you have the G
blotnick. So, I think now libraries also
started Realizing that, you know, how world is the best.
And I don't think anybody would disagree with that statement,
that ggplot and tidy was universe is the best for data
analysis. Even I've seen a lot of python
data and data science between success accepting this, this
fact, so if you ask me, I would say if I want to make an
analysis, make an analysis report, make something that is
shareable that I can, you know, share it across internal
organizations Community interactive.
I would still stick to our for that rather than you know, going
to Python and My luck that is a place.
I would strongly use our Gordon Gordon, okay, I'm done.
So that's instead. I mean like well where you
belong apart from legit machine learning models, like for
example tidy models have for me has been like anything, but
tidy, you just seems a very very verbose way of defining a Model.
A. I having used Cyclones three
lines of code. It's very difficult to come to
come to terms with Payette ID models.
One more thing is recently for some work.
I had to write a random forest and then I found that the
natural the sort of the default. Random Forest as going.
Our it has been somebody had it has been adapted from Fortran.
So it is sort of I think it's a 32-bit code or something.
So because of that if you have a large data set to just hands,
right? So please, I guess machine
learning is one thing where like, sort of python is sort of
his. I don't think there's any
comparison again. Well, I hate of our, do you see
are catching up in this? Or do you think that like, I
mean, it's best for people to be sort of bilingual to kind of
address. Steve these two words.
Now, if you're bilingual, you should definitely stick to
scikit-learn. There is no to a avoided, but I
see our studio doing a lot of lot of lot of work.
In terms of Tidy models, are to give you an example, Julia,
silicic. So, the the creator of like, one
of the creators of Tidy text mining.
So, she creates a video every week, and I am seeing a pattern
that every week. She uses tidy models.
So, this is a pattern and I see our studio doing a lot of
webinars related to tidy models and tidy models, if you know,
like, it initially was like they were Initially only that carrot
was there, Max Cohen had developed it, but tidy models is
something that now they've pushed, they have created a
universe like tiny words. They've created a universe
around, you know, the study models.
So if you are in our universe, I think there is a strong value in
getting into tidy, you know, Teddy models than, you know,
using like you said, right? You have like three different
ways to do random person bar. Which one do you pick?
I think that is the problem. We didn't have initially but we
have now I think we should like at least practitioners should
solve it by just getting into one universe.
That would that would Help them, you know, do everything in
standardized manner. But if you are bilingual, there
is no question about, you know, leaving scikit-learn, especially
the fact that, you know, deep learning libraries, right?
There are a couple of deep learning bindings.
In are like, for example, you have carers binding in, are you
have helped in the flow binding an hour, but it's still a
binding. You still have to have python
installed on your machine food reticular, 10 top of it and then
do it rather. Do it in, you know, the this is
the same language python itself, right?
So why to use bindings, so I mean, so again like I mean as
you know, like I mean like be like recruiting fairly heavily
over the last one year and since I personally use our I sort of
have been specifying are for all the people that I recruit.
But what I find is that in India, among all the series that
I get, like a very, very large. Majority of the data scientist.
Seem to only know, python even for Eda, they use Python and in
my opinion, produce a lot of ugly crafts.
And, and so like, I mean, why why?
Why is it that like sister of? I mean, it's, it's only been now
Lego the last one or two months that I found at least some sort
of a critical mass of people who are, who are actually using a
fair amount of our, in their regular work.
It's also, why do you think the data science community in India,
especially as link? So heavily towards the python?
I mean, that is just my empirical experience over.
And why is there a difference in sort of a background of people
who, in your opinion, seemed to prefer a prefer python?
Part of the work. They do your first question.
I think there was a myth and that myth, says, you know,
becoming like a self-fulfilling prophecy.
Know the myth was that python is a better language for data
science. Okay, so which I strongly
disagree, again even today, but this myth was told again and
again and again and everybody who is getting started to become
a data scientist. They believe this myth and now
they actually, you know, learned only Python and Deb.
There was what they were brought into this mess and now it became
like a self-fulfilling prophecy because Everybody is now a
python developer? And now, everybody will say,
okay. See everybody is a python
developer. That's why python is based.
So it is, it has become like that.
I think that myth has been told multiple times in Indians are,
and data science Community. Have I run like, for the record
I run Bangalore or user group. We have, we have done, we have
partnered with the pi data Bangalore before and then we
have run events. But yeah, so have we have people
coming? Like there is a Malik lot.
I have spoken to a lot of people who use our in their daily
workflow, but still people don't want to believe what they do.
I want to believe. So this, this myth has become
like a fact that is, that is one of the primary reasons why you
would see a lot of python-based resumes.
Second thing is, I, I don't know.
Somehow, I feel, you know, people who are technically, good
being always bashing a heart. I don't know, for what reason,
so, I, I am a computer science engineer.
I use Python, but still I love art and I would still like if
you give me a choice for a lot of things, I would still prefer
our but still, I see this. So this is this is a big problem
and that is one of the reasons why a lot of people.
People are not learning or itself.
I mean like you don't lose anything by learning a new
language, honestly. Speaking.
It's it's a new tool in Your Arsenal so you can do a lot of
things. But yeah, that is why a lot of
people again have not learned or but if you will, if you see a
lot of veterans you would see them, you would see them, you
know, liking the other tool as well like appreciating what the
tool is offering whether you use the tool or not.
But at least, you know, you should appreciate what the tool
is offering like in the world that we live in.
So these are the two reasons why I strongly.
Think you would see a lot of Out of python-based resumes at least
because of that myth that that people people started talking
about, does it also have to do some sort of a background like
for example for me like before I got into our python long time, a
very long time back. I used to use mostly.
I was mostly in Excel Greg and I used to write like, VBA code to
kind of do some interesting things on top of Excel, or I'll
even briefly use SAS when one of my employers had a license for
it into one or Or hand you think like the python guys are, mostly
the sort of people who come from more for software, engineering
background rather, from rather than a sort of an analytics
program. Yeah, you're right.
Actually, you're not just speculating.
If you see students coming from engineering colleges.
I wouldn't necessarily say, you know, like only ID background
but still students coming from engineering colleges doing well
with the python again, because you know, their peers are using
python. So they can talk to somebody who
uses Python and then get the answer.
But if you see non engineering background, I see a strong or
at, I strongly encourage them to use our like I so told you right
everywhere. I teach I try to use our because
people who are not from engineering background.
I think it's very, very easy for them to get started with our
initial friction is list. And again, the problem is those
kind of people applying for a, you know, typical data science
role is also not very common in India.
That is another reason why you wouldn't see.
Like, for example, if you see outside, India, right?
Somebody who had done some bioscience course, they might
be, you know, looking into data science.
Not even in Pharmaceuticals, all these things.
But if you see in India, especially like, mostly, you see
Engineers, we are all engineers and that is how we have been.
And that is why you see this strong python here.
Now. I mean, let's talk about that
database access rate. So I think it again like the big
come here, literally discovered like because to my nine years of
Consulting. Before I joined delivery.
I people kept asking me if I know how to use big data and I
was a kid. I know the principle of big
data, but I don't think your org really needs to.
Big data, and then I joined delivery and here, obviously,
our data size is like absolutely humongous tank.
So like thanks. So for the first time I've had
to like kind of continuously have database access in stuff.
And if we have sort of solved that problem for the last 10
months of being here is to get a use with our package called tv
player where I just write code in our it's okay, if the court
is a heat generated with maybe slightly inefficient compared to
writing it natively in SQL, but I have personally become a very
big fan of Because I find it far easier to debug compared to a
skill. I think SQL because of it's sort
of, I don't know if we should call it an in fix for might or
something, but you have to keep looking up and down and up and
down. If you have to sort of debunk
the debug an SQL statement and it gives 100 line segment, which
is very easy with the kind of data that we have in my company.
It's I find it done. I find it sort of mentally
taxing to you. So but I will gather for the
benefit of the rock python guys in my team have been sort of
trying to figure out a DVD player.
Violent deaths so far not been able to find it and so on.
So how easy database activity are in Python.
How my I mean, I assume you have done a done a fair bit of this.
Yeah. So DB blade is amazing again for
a lot of reasons like you mentioned and again sequel
debugging is also very difficult.
Like you said, so what does currently working out for me at
least in my current organization?
The greatest thing that I have found out this data breach.
So we have one, you know Big Data System, where we have
connected it's like one cluster and within that one cluster.
I B equals Park sequel. I can write our, I can write
Python. And so I have the ability to
write all the three languages in the same notebook environment so
I can just barely switch between languages much much easier.
I don't have to you know, make new connections.
I think that is that is really good option.
But I think if you do not offer this option, only your other
solution is. Let's say if you have a spark
when you use Five Spot, make a separate connection and then do
things separately. It is spice Pockets, you know,
closer to closer to what you would see with the Pandas adding
koala is something that people are using call our qualis.
I think it's called people are using task is something people
are using all these have a very similar pandas are related to a
syntax. But but in my job, I don't have
to do all those things because I have this flexibility.
But other than that, you cannot, I don't think you have a lot of
options to write a native, at least from what I know native
Python, and that would be on and the other thing, like you spoke
about, right? Making database connection
itself, using our studio is quite straightforward, very
easy. You see the connection and it's
like how you How you would see it?
If you are using data reports from database, from SQL client.
It has a very similar nature. If you use our studio for making
database connections and you can have multiple connections, that
is again, very easy to make button in Python.
It may not be easy. But the advantage in Python.
If you ask me is most of this database Solutions companies,
you will easily get a starter code in Python, but you may not
easily get a starter code. Do not like any time you see
this thing. So that is another place where I
actually use Python a lot. Any time, I see an API, you go
to the documentation. You would actually get a starter
code in Python, very fairly simply like you can easily get
it and you can get started with python, but it may not be as
easy as python in order to do that.
One other thing. I mean like, this is again,
something, which I've been doing a lot of and like so.
I as part of us as the head of the, I ended up having to build
a lot of dashboards and reports. I have in my life.
Never figured out these sort of bi packages like Tableau or
click on power bi or Any of these have never figured out any
of those. I probably have too much of a
control sleep for that. So I do things using tiny or
like I kind of generate reports using our markdown and things
like that. So often it actually have come
across a few blog posts and videos that you have made on on
streamlet, which I think is a python-based.
I'll start offered dashboard create a so can can you talk a
little bit about about creating dashboards how that world Works
in both are and in python or rather?
If we were to put it this way, Dash.
Boards, without the sort of broadly speaking, that Tableau
Universe. How do you, how do you program a
dashboard? How easy it is in both our and
pythonic where you would pick? They say something like a
something like a stream later and where you would pick
something like a shiny or something.
Yeah. Yeah, I have a strong opinion in
this as well. So maybe I'll start with why you
should use a programming language for a dashboard over
Tableau or power. Bi are any of these proprietary
tools forget about that. This is open source for a
moment. Even, you know, if it's not, why
do you think? Why do I think that you should
use? I think, the first most
important reason, again, I would talk about technical data,
Tableau. If you see you, can you can have
a table and you can make some changes in table.
Now, you can have another column or some changes in the Tableau
when you Import, the data and third you can have calculated
field and for you can have aliases.
And when you do like this is the under industry till the chart.
You could have changed the label.
Okay. Now, if somebody is coming to
debug, they will first see the sheet and they have to go back.
All these places to understand what like, if I have to change
our field name and this is, this is a very difficult task.
And in terms of technical debt, it just gets accumulated and
accumulated, and accumulated. And, again, it's a, it's a very
bad thing for an organization. So, what these Ironpython offers
is simple, right? So if my colleague has built a
dashboard. All I have to do is import the
code in our studio or import the code in Visual Studio code.
I can basically see where all these variables are, you know,
reference and then you can you can you can start using it for.
So it is very straightforward. In terms of technical debt, in
terms of reusability. I have built something, you take
it. You keep it for yourself, change
the data source, change, the column names.
You have a dashboard up and running, it might look easier on
top. Universe, but because of all the
complications that I said, it is also not very straightforward.
I I prefer Tableau at only one place has a lot of cxo
dashboards right? People had, you know, the higher
level, they like things visually appealing.
I strongly still believe lot of Chinese developers.
Lot of stimuli developers do not have enough tools to make their
dashboards. Look as good as Tableau
dashboards. I should agree that I think by
our a shiny actually recently like a year back, at least.
Rider to give you on UI where you can actually select the
theme of The Elements like button should be like this.
They should be like this like Dynamic team created.
That was there in Chinese, but still, I don't see a lot of lot
of shiny developers using it. So, most of the times, if you
see Shiny applications as shiny dashboards, it wouldn't look as
great as a tableau. So only for visually aesthetic
appearance, tab lose fine, but for everything else, I think
people should either shiny or streamlet.
Okay, now coming back to shiny like, let's say, talk about the
our universe. I think our universe has That is
amazing, which is our mob. Don't, especially if you want to
build only a client-side dashboard.
You can use flex dashboard, right?
So you don't need a server to host.
It simply HTML file, that looks like a dashboard.
So, to it, to even demonstrate this, I actually built a cackle
notebook. So, kaggle.
Notebook is actually only a client-side notebook.
But, ultimately it renders a dashboard at the friend.
So Flex dashboard is really, really amazing.
If you do not want server-side interaction.
You should always offer client. Sorry, Flex dashboard, where you
can just Share the HTML file. Anybody can open it, it would
open a browser. And if your organization can
afford our studio, connect our studio.
Can I ask I've spoken to a couple of people who used our
studio connect people have said it's magical, you know, click.
You can just publish your report into our studio konnekt.
And then, you know, somebody can make use of it.
So Flex dashboard is a free solution.
Now, coming to our shiny or shiny.
Watch. This was the sole King for quite
a long time before simulate came in.
So I have made multiple presentations, why are shiny is
a very important tool unmatchable.
I think this was the time when slowly in Python Universe,
people started realizing that we need some alternative.
So to powerful tools came and I would say, one is plotly Dash
plotly. As this plotly visualization
company. They also pushed Dash.
And now they're pushing a lot of Enterprise stuff.
Especially Dash pokers, and then we have, streamlet.
I still prefer streamlet to plotly dash.
The reason I prefer streamlet, is because I find streamlet
quite similar to how I used are shiny.
It's not in the same way to write the code, but how simple
it is to Do you know spin up something?
See the problem in these kind of things is if you are a data
scientist, your focus is not to develop a full stack
application. That is something that we do a
lot of people actually forget when they develop these kind of
applications. And a lot of companies actually
have a software developer or a data engineering team tied up
with. So as a typical data center.
It's not necessarily every time you are going to develop a full
stock application. But if there is a need for you
to develop realistic application, do you have tools
that has very less friction to help you make like an MVP?
I would see this like more like an MVP.
So I think that's where streamlet under shiny.
Actually does a great job, but that doesn't mean you cannot
develop full stock production grade application.
Now, the question is, what is production grid.
Do I want to build the next Facebook using shiny or
streamlet? Know, I would, of course not
right. I would probably recruit
somebody who has react background, more in stock, and
then we'll let them develop it. But if I want to develop a full
stack Facebook, next Facebook, if I want to raise Ting.
If you want to pitch it in my organization, let's say like I
want to give a tool to my internal, let's say some team as
part of an initiative that like data sends have figured out
something. Now.
I don't have to find a resource to do this pilot.
I can myself develop this tool and then give it to them.
So it's not that you cannot develop production grade.
But what production grade is the first question.
The second question is, you can still make these tools work work
much better than how most people are developing.
If you see Shiny two years back or should your conference Focus
solely on lot of scalable. Applications, there is a library
by Colin fake called Golem Geo. Lem Golem is an amazing library
that tries to bring some standardization recently.
I came across another Library brochure.
I think that is also by Colin fade and that is also again
trying to make more optimization in this area.
So, yeah, to, to quickly some of our shiny and stimulator quite
equivalent or from from a developer perspective.
If you ask me very similar, it's just that in different
universities available, but I don't think there is anything
like our mob done in Python Universal and our McDonald's.
Like, Really amazing. Even if you are a software
engineer, I see. Why don't you talk a little bit
more about our March towards? If I don't think give, they
might be a lot of our listeners, who may not be very familiar
with the tool. I so, can you talk about how you
use are? My daughter's is sort of a
client a step back boarding. Yeah.
So this is a very nice blog post written by Minimax are so we can
link that in the show notes as well that compares our notebook
and jupyter notebook. That had that does a very good
comparison. So now our notebook is nothing
but a slight extension of our Bond.
So we'll just talk about our mark down alone.
So our markdown is this is this script type that that is
available in our that lets you write our code and also lets you
light, right markdown. So R plus markdown is what you
are or McDonald's but it's like oversimplification of saying.
So what are mugged on? Lets you do, that.
Does it primary lets you create like a lot of different formats
and it's not just you know, typical standard report that you
can do in your company. For example, let's say, like you
have a dock, you want to build It's a simple analytics report
and you want that report to be customized.
For example, like, let's say, it could be like a stock market
reporter. Something for example, and based
on what the stock market value. Do you want to do some natural
language change within the document and you want it to be
sent out to, you know, bunch of people every day.
Now, this is quite easy to implement it in our universe.
So you you just need an arm of done and that arm of don't under
probably the package like sendmail.
And that arm of Don has our script that gets customized.
Like let's say maybe you're building a linear regression
model or you're building a cheap.
Model that will do some forecasting and all the results
are there. Visualized very nicely and send
that email as PDF to the customer.
Very simple. This is one of the use cases of
our arm Ogden, but leaving out the organization part like
organization Partners like you build a client site dashboard,
you build analytics report, you build HTML Pages.
You build PDFs, but leaving out this part are McDonough still
grown a lot. You can publish blog posts using
our mock. Don't you can write books using
our modern style. I lot of People started writing
books, using our mob done with the library called book, don't
book. Don is an extension of our
Magnum. So, the advantages, when you
like mini books, at least I see a couple of people I respect in
the industry. They are not very famous liquid.
You see them actually riding a mini book.
So that many book is like a reference for me as well
reference for the elderly Community as well.
So once you the greatest thing that I like about harm of known
as want. So this is a very classical
example of once you give the tool to somebody how they
leverage it, something that you cannot even imagine.
Like I wouldn't imagine publishing a blog post using our
mobile, but I'm doing it. So block down is they're booked
on, is there a lot of extensions of arm of them, but to basically
arm of known as like one script, where you can write markdown
where you can write R and you can write anything and all and
that gets rendered into some formats.
And how do you render it is what the different parameters.
Does it render like a book or blog post?
Like a dashboard with, you know, subtle changes in it, and that's
something that python Community. Definitely misses.
Correct, correct. Okay, so I think they're coming.
So I think, what I initially planned this episode, I mean,
before I decided to go ahead of high value on it.
I thought you should have a sort of a are versus python Qaeda for
debate on this. But I think what's happened now,
is like, you provided very nice intro into, like, what is it
like to kind of use? Both are in Python?
Because I see that you use both in your in your regular work.
So can we talk about like, I mean, like, I mean your sister
spoke a little bit about this, but normally how you get the
best of both worlds. I mean like if you if, you know,
both are Bite it. Like how do you sort of like
Oneness? Like I mean, you were talking
about how you do your Eda and I are and then the modeling in
Python, but my thought immediately was like, okay, how
do you transfer the data from one to the other?
Like, I mean, I can't imagine radical CSV or something as a
go-between and so on. So so how do you get a integrate
these two into your workflow to kind of get them become a even
better data scientist? Yeah, so I have actually called
this as a superpower previously, in couple of my talks where you
can combine, you know, it's like, I don't know if you have
seen seen superheroes. Like they usually you have like
combination. Like, if you see Justice League,
they have like multiple Powers together and they formerly the
same way. Our has some superpower python
as a superpower. And if you can combine both of
this, it's like a massive super power.
And that is what reticulate library is 4.
Pie R has a very nice Library called reticulate that lets you
combine both the walls and like I said at the start, right?
So after reticulate came in, you actually started seeing a lot of
python Library coming into our world just by using binding.
So at the start like we said, right?
You know, if you are bilingual, is better to use that that
language itself. So now the question is, you
don't want to use binding, but if you want to use the same
language, then you can write python code inside.
Our studio using reticulate, and you can interchange objects
between both the languages. Let me give you a very simple
example, you know, let's say I want to do an LP, okay natural
language processing and I strongly think natural language
processing, couple of libraries like Spacey hugging phase
Transformers are like super awesome in natural language.
Processing. Even if I have tidied X, I would
probably prefer these languages for me to do natural language
processing. But before I do natural language
processing, I want to import data may be like you said, I
would probably use a DVD player. I want to.
I want to do some Eda like unigram by G or like whatever I
want to do some kind of cleaning.
I would probably use tiny dicks to Green do cleaning because
tidy text as a very English syntax, right?
When you compare it with nld care somewhere.
It's a very English syntax. So, after I do all these things
now, I would take this data. And immediately the same code, I
would pass it on to my python environment and all these other.
Within the same code, if you use reticulate, everything is in the
same code and now within python, I would probably let say do POS
tagging any a named entity recognition and probably let's
say, like, even I'm going to use cyclone and extra boost to build
a text classification model. Now I have the result.
I can interpret it. So in one code, I basically used
our and python basically managed to use best of both the worlds.
And this is simply Possible, only because I'm Oozing
reticulate, reticulate handle. Please, I have a data frame in
or how does it, how should it be in Python world?
I don't have to be worried about it.
Reticulate as a simple map, like a table that would say, okay, if
you have a list in Python, it's a, it's a, it's a vector in R.
So they just give you the mapping to understand for you,
to mentally, understand. What is this object here?
And what is this object here? But other than that, you as a
developer, do not have to do anything.
Just basically have the installation and reticulate also
lets you call. Specific python version.
What does it mean? Let's say the initial problem
that I talked about. Right?
Let's say you have a condom environment and you have a bunch
of libraries there or let's say you have created a virtual
environment. Where I think that is quite
common with python developers to create virtual environment to
keep the library's only within the environment, ready
platelets. You specifically, called only,
that environment, use libraries from that environment, keep your
entire session within the undead environment.
And the greatest thing about reticulate, again, is reticulate
can be combined with our shiny and, and our mock known as well.
Which means I can now finally create a report, send an email
also, which uses both R and python code.
Or it's okay. That's, that's very interesting.
I, I personally have aware of the existence of ridiculous, but
I haven't really used it though of a time finding cases where I
have to use it, because I run a team, which sort of which works
both in our and in Python. So I come stage will have to
integrate our code and things like that.
So we love will have to do that. So by the end of the super, as
we were sort of talking about like reticulated, ironpython
directly, one of the other things is like, what about other
languages? Like, for example, I think is
right in the beginning of the We spoke a little bit about Excel.
So can we talk about a little bit of how do you interface
between? Let's say our and python on one
side and Excel on the other. What do you use each one more.
And also I don't know if you have any experience with with
Julia, which I think again, becoming popular with a lot of
with a lot of data scientist. Yeah, okay.
So starting with Julia, so I tried a little bit.
I, at least if you ask me, personally, I'm more of an
applied person. So, for my, whatever I do is
mostly driven by business question, so I still don't see,
you know, Julia fitting in my work line anytime soon.
Probably. Like, next, 2-3 years.
I think our and python would completely keep me safe.
At least from the job market or whatever.
It is straight, but the next half a decade.
So that way I don't use Julia and Julius pitch if you see
their pictures to solve to language.
Like one language for Prototype own language for protection.
So I think Julia would be more applicable for people who are
facing the to language problem. So let's say like if I'm an iot
developer or something. I mean, it's Computing.
I develop something in Python. Then I have to translate it to
into, you know, like that that devices code.
Maybe Julia will help me. But again, if you actually see
tensorflow ecosystem by torch ecosystem, lot of these tools
are also developing tools to help you stick to python
ecosystem and then export some, you know, the model into the H
come. Booting or that side of the
world. So both are actually growing a
lot. So I don't see, you know, Julia
being a main tool for a typical data scientist who's like me
using it maybe separately for machine learning engineer show.
That is about Julia and I'm sorry.
Sorry. What is the first one?
Yeah, so I think Excel is good. If you, if you accept, if you
are not using dates, so you would probably have seen lot of
names around Excel dates xlsx LS painful.
Many times. I would again put extra light
Tableau. The problem that I Basement
Excel is reusability. Lot of things that you do in
Excel. Like if I have to redo it again,
it's very difficult for me. But again, there are places
where I would strongly prefer Excel first.
The first point is like you said, right?
If I have to do d f dot head in condos or a the dataframe head,
like top ten rows. I want to see.
I think I can make more better use of my same time.
If I do the same thing on Excel, right?
So, if I have to see a part of the data, like you said, right,
conditional? Formatting, I'm known for doing
a lot of fun. Ditional formatting and then,
you know, making colorful colors but it actually gives you a lot
of advances that you might otherwise miss when you simply
look at, you know, the plane screen that you use order or
python for. So there are places where I have
I want to look at data. Simple pivoting is sometimes you
know much much easier and to do in Excel.
So we all have to accept, right? If the data is not very big I
can just use pivot, make a simple chart.
So Excel is so useful in that these days I find making charts
very thematic using Excel Tex. Okay, whenever I say Excel, it's
either, G sheet or Excel. I don't know.
Differentiate between a spreadsheet based tools, so
making charts that that are quite fitting in my organization
scheme or the team that I want to produce Penny, minimalistic
charts, and maybe it's because I didn't put much effort in, you
know, creating a theme myself. But yeah, XL XL.
I started finding it more easier.
And so these are these are certain places.
But again, I would strongly discourage using Excel in data
science for flow. The reason I would say is still
a lot of people. I see them using, you know, like
Excel library in or like pandas read underscore Excel to read
XLS. Five, I think we should try to
keep as much as clean possible. Which means like we should deal
mostly with the CSV. We shouldn't, you know, get into
Excel world because that world is really messy in terms of
putting something inside your pipeline.
If something goes wrong, for example, let's say there is a g
sheet and the output of G sheet is what you are using inside.
You're like Founders workflow. Now.
The problem is that g? It has different authorization.
If somebody leaves the organization, right?
What do you do with the g-h it? So the like the more, you know,
proprietary Solutions you get into your data.
Workflow, I think the Messier, it becomes the technical debt
increases. So keeping it simple as a
school. Like for an automated workflow.
I think we should keep it as simple as possible as, you know,
standardized as possible or python tsp.
But if you want to use Excel, these are the cases I would use
Excel for. The other has been great.
Is we re done is give a fascinating conversation adding
to end. I mean, is the question that I
in most of the most of my conversations with.
So let's say if you are, if you are kind of an aspiring data
scientist and from the programming perspective lately,
how would you? Or let's say you have your seen
some sort of very interesting statistics in yourself or like
some sort of interested in looking at numbers and so on and
you want to be a data scientist. So how do you approach it from
the programming perspective? Like what you learn?
What's how do you sequence a tank as you get into the work
environment? How do you navigate between the
different programming languages that are available?
Yeah. Yeah, the the very first thing
that I tell everybody is don't get into language words, like
people who are part taking part in language words or you know,
like language phonetics. I don't you shouldn't be
language phonetic, you like both the languages you would like one
language, but just don't get into language words.
It doesn't help anybody and people who are doing it.
They are already professionals. Probably they have they have
achieved something. So, if you're a beginner and
when you get into language, or you don't get anything out of
it, you're basically losing out a super power that is available
in some other language. That's the first thing.
The second thing is. There is no point in, you know,
trying to figure out which is better or not.
Just get started with some language, whether it is order
python. So if you want some clue or
guidance, I would say, like if you are not from engineering
background pekar, if you are from engineering background,
pick python, just purely because, you know, you will have
a Community Learning something with the communities, much
better than learning, something individual.
So pick order Python, and if you are not from engineering to God,
if you are from engineering pick Python, and after you pick that,
I would strongly suggest first to go through the language being
It's this is again a mistake that I've seen a lot of data
science Enthusiast. Doing just straight away.
Get started with machine learning, don't do that.
Just get started with the language Basics.
Understand the data types, understand, you know, how to
write for Loop out erisa fails in, in, in whatever language
that you have picked. I think like you said, write
these things come very handy, probably sometime later in your
career. You don't want to be in your
later, in your career at the time thinking.
How do I write is here? How do I write is here?
I mean, that is very basic part of the language, right?
You're not just learning the language for data sense.
You're actually learning the language.
It's not like I'm asking you to do develops with python, but at
least you should be able to read a python code and make some
sense out of it. Like, what does it mean to learn
the basics of language? Once you learn the basics of
language, get started with basic data manipulation in our basic
tidy basic ideas, especially, especially the deployer under
tidy or and in Python pandas numpy.
And after you do that, I would suggest immediately get started
with data visualization, not machine learning.
And the reason I'm saying, is from my teaching experience.
I've seen when people get into t.
Our machine learning. It just goes deep into deep.
It's like a rabbit hole. You can never get out of it
because now you will try to learn.
Linear regression, logistic regression.
Then you start going into decision tree.
So it's like a big rabbit hole. We as human beings.
We need some gratification. So visualizations are very good
way to give you gratification when you are trying to learn the
get started with visualization Library.
Start from, you know, static visualization Library move into
interactive. Visualization library.
Now, switch back to machine learning start from, you know,
classical algorithms go deep, and then learn Full stack
development. Also, I think we are in 2021 and
we, as data scientist. We should be.
We should we should have the ability to build at least an
MVP. If you are in our loan or
markdown and are shiny. If you are in Python, learn
either Dash or streamlet, I have slightly strong preference for
streamlet. I think this would make you.
This would make you really competitive and market.
And this is, let's say, you get the job.
And after you get the job. I think your primary duty is to
go through documentation and understanding how people have
written code. Internet is Like an amazing
place. You have a lot of Open Source
Code. I think at this point, we should
stop watching tutorials when I make noodles, but I still say, I
think, at this point, we should stop watching tutorials and
start reading. Somebody else's code, spend more
time on stack of lighting that will make you an intermediate or
better programmer from me. Thank you for listening to data
set. If you like the show.
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Once again, this is kind of exciting of thank you.