I feel in that sense a PhD is actually, you know, in some
sense. It's leading you to become a
data scientist because that's what we do in day in and day
out. One of the challenges I feel is
that in the industry itself. The expectation to reach a
solution is the time frame is very small.
Hello and welcome to data. Shatter the podcast on all
things data. This podcast is a series of
conversations with experts and Industry leaders in data.
And each week. We aim to unpack a different
compartment of the data suitcase.
I am your host. Karthik chassis that I'm a
blogger newspaper, columnist book author and a former data
and strategy consultant at currently head analytics and
business intelligence for Liberty 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 0e.
N th you be a.com, Halloween is expressing his podcast belong to
me and my podcast guests, and it do not reflect the views of any
organizations. We might be Associated, nothing
disgusting. This podcast will be taken as
Financial or legal advice. When I was graduating college in
the mid-2000s the word in job, descriptions that most commonly
appeared alongside data was analytics.
However, in 2008, the phrase data science, got coin and took
over the world in the next five years.
Nowadays. It seems, everyone wants to be a
data scientist. Where is this science in data
science? It's and why are so many people
with phds in pure Sciences moving to data science to
understand this better. I bring back one of my old
guests or data. Chatham.
Hernia parameswaran is an aerospace engineer, third
neuroscientist turned data scientist.
She's co-founder of messy fractals and cavity, Atta and a
researcher acts. 18 laps, Daniel talks about her journey from
Neuroscience to data science, white PhD, is good training for
data science. And what the science in data
science is all about So to get us started.
Can you just take me through your career?
Evolution? I mean, last pretty much the
last time. Last I know is that you are
doing b.tech in aerospace, engineering.
So what happened after yeah, like you mentioned right?
I did my btech in aerospace engineering at IIT Madras and
that's where the pH debug between.
Where during my third year, at IIT itself.
I was introduced to this. Guitarist at DHS Hospital, which
isn't that our money. And his name is, yes
krishnamurti, and he was doing some fmri work in neuroscience.
And one of my seniors SRI that they were Arjun.
Who is in fact, a professor at, I see, I see today.
He introduced me to dr. Krishnamoorthy.
And there was my first stint with Neuroscience.
So, where I started looking at Neuroscience data, I Getting
some fmri recordings in that sense, right and post this I
continued my PhD immediately after my btech at NCBS in
Bangalore in CB s stands for National Center for biological
sciences, right? And here I did my PhD with dr.
Open the sing Bella. In fact, he himself is a
physicist who moved into neuroscience and those were the
transitions which were happening.
I guess a decade or two earlier. Sure.
And during my PhD, I worked with another collaborator for name is
Tarik, a garage in, and she is a neuroscientist of, and she's
from Stanford. She was a neuroscientist from
Stanford, but due to some personal situation.
She had to come back to India and her father had, in fact,
passed away and she had to take over her father's micro finance
company. So, she became a neuroscientist
and an entrepreneur that time. So, She was working with us at
in CBS and I work very closely with her for three years.
Last, three years of my PhD. And once I finished my PhD, she
said, why don't you kind of helped me solve some questions
at mudra microfinance? Also.
And so, the questions are simple questions, right?
Like, whom do I best lend loans to write?
How do I cluster Villages where I give loans and they were not
Neuroscience questions, but they Very interesting questions.
Nevertheless, and I guess that's where I would have first taken
up. What is what we call as data
science today, right? Try to address these problems.
In fact, the very first question I addressed was this morning,
whom do we lend loans to? And at that time we built a
simple, logistically, regression model and the results are very
interesting because it was a mother's education, which kind
of finally decides whether the person will repay.
A the loan or not, right? And so that's how I got into
data science and I won't say, in my particular case.
I won't say it's 100% shift from Neuroscience to data sense.
Because today, I continue working with Tara And as a part
of this company called Sapien Labs.
So Sapien Labs is essentially, a not-for-profit neuroscience
company where we are trying to collect data EG data.
From thousands of people across the world, you know, and you're
trying to make sense out of that data.
So a part of my life is still stuck to Academia in that sense.
And another part of my life is where I do more of my data
science related things today. Interesting.
So, okay, so, but that Euro
science Academia part is not strictly speaking.
It's not like work for a university or something like
that. It's more like Neuroscience.
I can't hear. Such.
Yeah, exactly. Okay, so it was a sort of soft
move in some sense for you, right?
It was like you moved from Neuroscience to something
adjacent click. So, if I ask it, why is it that
you just sort of stuck on in industry and business for the
large part rather than sort of pursuing a career within its a
core Academia and so on? Because my understanding is that
like or it correct me if I'm wrong, but my understanding is
that if somebody does the PHD this with a strong, it did to
becoming an academic. So what was the then?
We particular reasons why you stuck to the to business?
No, I am sure even when I started off my PhD, I guess the
Eve was probably to become an academy ssion.
Become a professor at some University and, you know, follow
it up from there. But as you do, your PhD, I think
the number 133 to anyone needs to complete their phds
perseverance because it is a grueling sort of affair.
Right? And today, things have changed a
bit when it comes to Getting an academy, adore bread.
Because most of us need to follow up phds with a postdoc
and postdoc is not a degree as such but it is more of the same
stuff that you do do during a PhD.
And essentially, when you finally get your job, you'll be
easily around 32 years old and I would sit, right?
So it's a long, wait before you actually get your tenure track
position, right? Because that would take another
three to four beers. In University, I think that was
one of the deterrence and the second thing.
I mean I would say is monetarily.
Also, a PhD after PhD it, I don't know if it really makes
that much sense, right? Because you're if you look at
your P scales and your growth in terms of career opportunities,
you grow fast. I mean, you grow.
As you do your degree is right. If I do my btech, I get
something. I do my intake.
I get a little more if I to do my PhD images.
Come down. Yes.
Yes.
And I'm a bit old, for many of the jobs also, so that would be
some of the reasons I will say. But in my case, it was also
serendipitous, like my husband arvind.
He was running a startup called office.
Yes. At that time, which is a
e-commerce startup, and they were a lot of operational
optimization which It needed to be done, and he is to obviously
reach out to me, and we used to work quite a bit together at
that particular time point to solve, you know, supply chain
problems. Operation problems for this
particular startup, and that's when both of us, kind of, you
know, thought about it. Why don't we kind of work
together because you bring business value.
I can solve the problems and that seems to make a lot of
sense that when we created messy fractals in 2015.
Three years after I finish my PhD.
And the goal of messy fractals was centered around data, sings.
Okay, I then from there like you guys decided to sort of focus a
bit more on the, on the sports bit, I guess, which is where you
sort of got into comedy at diet, things like that.
I have no idea. Exactly.
Yeah.
They're initially, we started working with startups.
We wanted to solve data, science problems for startups, and
sports was a hobby. So where we started working with
Saina Nehwal prakash Padukone at that time as a hobby, I would
say because it was just an interesting problem to solve.
And but eventually kept getting bigger and bigger and snowballed
into more sport analytics and less of other startup stuff
coming back to your PhD days and so on.
So what about your fears during your PHD programs?
Have have many of them. Also moved into something sort
of similar to data science, or have a lot of them sort of
stayed within Academia. If I have to think correctly,
they were wrong ten of us who are doing a PhD is roughly
around the same time. And I would say, three to four
of them. Three to four of us have moved
out of Academy at self. Yeah, rest of them.
They are still pursuing hoping to get to Academia.
Some of them are still in their postdoc.
So, but I believe we are. If they get into their postdoc
at least they're quite clear that they want to end up in
Academia. Yeah.
Yeah.
To me personally, that's a very strong sign.
I mean because I think the easiest best exit out of
Academia into Industries. The moment you get your PhD, I
guess because once you get a get into a postdoc, you going to
the, you signal to the market in a among other things that you
want to be become an academic rather than be the industry, I
guess. That's exactly how it is.
Yeah, so, okay. So now coming back like that.
So, what's your in your obedient rather?
What's the definition of data sites have been?
Because I asked a lot of my guests this because this is a
very, very highly abuse term, right?
Like, lots of different people use it to mean different things.
So what is your opinion is data center?
Okay. So my opinions, I guess they'll
be biased by my experience. By doing my PhD, right?
So and that's how I address all the problems which I face.
So I go in with the thing that I hype.
I try to design a good question. When I face a data science
problem, right? It's not easy often to design
the right question. And once I designed the right
question, I try to hypothesize on what could be the possible
solutions. And most importantly, I feel
Understand the context within which this question or problem
is. They're right?
For instance, in badminton. The context would be, how
players understand. You know, how our players will
learn the sport of badminton. What are the different
dimensions when it comes to badminton itself, right?
For instance, height is one dimension, which I never thought
of until I spoke to people who play badminton height at which
they get the shuttle. So Creating context around the
question I think, is one of the most important things, and once
we do this, we can design Solutions.
And that's where I think the abuse term of data science comes
in today, right? Where I design Solutions and
write code to address 0 Solutions.
But it's oh, so what you're saying is like, I mean, if I
might be paraphrasing, correct me if I'm wrong that the bigger
problem bigger challenge is in figuring out the right question
to ask rather than just solve solving the thing.
Gam get the problem. I'm guessing.
Yeah, exactly. So they can anything standing
designing the question and understanding the context.
Yes. Yes, because I know for example,
recently, I put this sort of only LinkedIn post where I said
that anybody can The three lines of scikit-learn sport to any
machine learning model. What's what's critical is to
figure out what the context is and structure the problem
correctly rather than so the real skill is in structuring the
problem rather than sort of solving it ready for solving it.
Some extent is sort of democratized nowadays, encasing
correct. Correct.
Exactly. Solving.
It is moral, is democratized with the kind of codes and the
packages that you get, right? It's it's pretty easy to write
it. Finally, if you've got the
structure, correct and well thought through and I guess
that's what will be good data signs.
And that is precisely, the reason I feel a lot of phds end
up doing data science because that is a skill.
We are trained to do in our phds, whether we like it or not
right. There is obviously a specialized
question, we go in trying to address in my case.
It was Sorry, I was trying to address a question that, but
what I ended up doing in my first two years of my PhD was
designed the question, which has not been solved before design,
understand the context in which that question, is there by
reading a lot of research papers.
And, you know, seeing if something which I think about
has already been done or not, and then the next two years, it
was about collecting data designing experiments to collect
data. So I could collect as much data
as I wanted or as much data as I thought make sense to solve this
problem. And the final step is where you
kind of write that piece of code which solves it, build a model,
which addresses that particular problem that you're solving.
So I feel in that sense a PhD is actually, you know, in some
sense. It's leading you to become a
data scientist because that's what we do in day in and day
out. Yeah.
So is there other certain So Phi, G Square, this is more
prominent because what I see is like, especially what I see is
data sets may not be so much in India.
But at least I brought like you have a lot of data scientist who
come from in my limited data Gathering physics or biology
backgrounds rather than other sort of academic background.
So are there certain kinds of phds that are more suited
towards data science? And like I mean, or or rather
like if you want to get into the industry you get into data
science instead of getting into something else and so on.
I I believe, right, the way people think about research and
It is almost any PhD kind of goes through the similar
process, which I just told you which is hypothesized.
Collect data, solve the problem. In fact experiments experiments
as a way of solving problems Krypton into the way science is
happening. I think only after Newton's
times, right? So it's quite the way that has
happened. So so datas, I believe that PhD
in any field will be Equally good at solving data science
problems. The only thing is the figure of
coding, which is stopping many people from taking up that as an
industry alternative. But otherwise, I don't think it
should make a difference. In fact today.
What I see is the reversal of some process in some sense that
is people who are data scientists, right?
At the end of their detect are interested in machine learning
AI. They've done a bunch of courses,
the research. I'm sorry inviting them because
all research papers. Go through this experiment data,
presentation of data. And now there's a new section
which is added in all research papers, which is a model to
address that data and to build this model there, looking out
for data scientists to come in. So they're inviting data
scientist to get into the research lab and you know, solve
problems, which may be of, you know, greater interest in that
sense. Like this could be like a
research in Pretty much any subject not like research in
machine learning or anything like that.
Not reset for the instance, right?
I will give you one example. I'm working on a project where
we are looking at cognitive development of three-year-old
children. And so there is a scale scale in
the sense. There's a question which is
given to three year old children are 80 85 questions and they
have to say which shape is bigger.
Which shape is smaller. Does this fit there in those
sort of things and you'll get a cognitive score of where they
are. It's called the Bailey's test.
Okay. So very popular test.
And now we are doing, EG is to collect data from the brain.
And so now, the obvious thing is to say, okay, what are the EG
signatures, which predict my bailey score.
So I want data scientist or a person who is good at machine
learning to come and build this model for me.
And so, we invited one person from Harvard and he was an
undergrad, and he's the one who actually built out the model.
And it took him three four weeks to build out this model.
But with some iterations and on them, but he was invited to
build the model. So it is a reversal of process
in some sense where and calling in data, scientists to quickly
solve this model for me because I have got all the data.
I've got all the context of got everything sorted for you now
just build the model and give it to me, right?
Which I think kind of brings me to the sort of classic
definition of data scientists as somebody who's like sort of
given a well-defined problem statement and then like writes
the model for it. I'm Exactly.
Exactly. Exactly.
Okay. Is this so changing track a
little bit like. So we were talking about how a
PhD sort of helps you in your data science career, because of
your process of hypothesize. Correct data, then we'll models
its own, which is pretty much what happens in everything that
we do at work. Right?
So, but are there any sort of challenges that I because you
come from an academic background West?
Let's say, for example, I don't know.
Like your output is measured in terms of research papers and The
things like that. So are there any challenges that
you faced in the industry because of your academic
background? Yeah, they were definitely a few
challenges. I faced initially, right.
One of the challenges. I feel is that in the industry
itself. The expectation to reach a
solution is the time frame is very small compared to that
during research in research. You kind of practically unlearn
that skill where speed is a skill which you unlearn during
your research, right? So but the minute we came to an
industry for instance. Instance, we working with our
Telecom product during early days of my see fractals, and
this guy had collected a lot of data about his users.
And he wanted us to Cluster the uses into many groups.
And he said, you have one month and you have to give me a
solution in one month and how to Cluster the data and that's
practically the time. It took me to understand the
context of the problem. How people interact with people,
paid vouchers and things like that, right?
So, speed is one thing I think which the industry demands, but
coming from an academic background.
We take it a bit too lightly. And another thing which I have
faced often is that the data is already ready when you come into
the industry, right? So here is the data now, give me
the solution. That's how it is there, as in
Academia. This is a problem.
I want to solve, so I need to collect this data.
So I'll design an experiment together.
Get this data. So the way that works is a
little different and often data is a limiting step.
I mean there are certain fields which are never available, you
know, there's certain data points which won't be available.
But still, you need to go ahead and take a decision.
So, those are two places where I feel, there is a huge difference
and phds struggle a bit, at least in the initial years in
the industry. Did.
There's another challenge which is that when we do a PhD is, or
when we are doing in Academy of you're not bothered about
building a product out of it here, often interested in
solving a problem coming up with a solution, understanding key
parameters, which Drive the solution, those sort of things,
whereas, in the industry, in the early days.
That's one of the key things I faced when we build a code.
When we write a piece of code, which Works that is never
enough. It has to connect with the
database. It does to sync with the
product. It has to keep getting updated
and it has to just work. Well and so that that is
something I I think I struggled in the early days of my stint in
the industry. But over time.
Yeah, I figured that out over time.
You learned that. So what you mentioned that th
these are also generally good at coding, right?
Because back when 10, 12 years back.
I used to work for Goldman. And they're like used to hire.
I think physics phds because they could write C++ code.
That's what somebody told B, which is like, which sounds like
sort of country do too because you would think that, if you
want somebody to write C++ code, you are a software engineer.
But, but so in some sense, I think these are to ask different
aspects of coding. As I guess in the PHD you become
good at coding, a model. But in the industry, I think you
have a lot of sort of for the lack of a better word like
pipeline coding and stuff you. That you need to do to kind of
kick to the database connected. The product on the other side in
the and things like that. I am correct.
That's exactly what I was also thinking day.
Yeah, for the first seed be, I learnt coding was because I had
a need for it. Even at ITV were trying to solve
some problems for my thesis. It was in combustion at that
time, right of jet engines. And I had to Learn Python to
write that piece of code. And so that's how we learn.
Then we get into research. We don't learn coding because we
had to learn coding. I'm really envious of you that
in IIT Madras. You Learn Python because be in
the computer science department, were asked to do everything in
Java. And by the time I graduated, I
hated everything about computer science.
It took on another six years for me to kind of start coding
again. So, so in that sense, I'm
envious of you for the ring python, but but yeah, I guess
sorry. You were telling About learning
coding because it was required. When you were doing.
Yes. It is.
A need to learn coding when you're doing research that you
don't have a choice because most of us in research, especially
during a PhD, we own one project ourselves.
So it's my responsibility to take it from data collection, to
model to writing paper to publish in it.
So, you end up learning to code? You don't have a choice really.
So, and that, that is a different way of learning to
code rather than I want to become a data scientist.
So let me learn how to code in python or are you just a
different motivation to learn coding?
I think that's the major difference.
Yea though. I mean, I don't have a PhD, but
I personally have a bias for I'm going to learn to code because I
need to code for something rather than I need to learn to
code because I want to become good at something real cents on
every day. I somehow have a bias towards
the first one, but coming back to another thing you were
saying, I mean, you were talking about how like the nature of
data is different between industry and Academia, right?
Because in Academia, I guess you get to design your own
experiments, which means you have control over the data that
you collect. Which I guess the downside of
that is that like sometimes the amount of data that you can get,
can get a little limited because you have to Even the experiments
in so on. But the good thing is that you
get all the fields that you want, if the way that you get
and so on, but in the industry, obviously, like, I mean, pretty
much have have almost. I do run some experience from
time to time. But like I almost never get to
collect my own data, it's some data collected for some other
purpose, that has to be used for this particular problem and so
on it. So, how do you, how do you deal
with this? I mean, what are the things that
you do to sort of like, kind of? Because it's obviously it's a
uncomfortable / unfamiliar situation for you.
So How do you deal with the situations where your space with
sort of data that's been collected for another purpose
and what are the challenges there?
How do you solve them? It's one.
See, it's a very difficult problem.
I would say because in most cases the baby have solved.
It is, we have in addition to whatever data we have.
We do another exercise of data collection of data.
We want, that is how we solved it so far.
And for let me give you an example.
For instance, when I was working with the micro Finance Company.
Right. Mother of microfinance.
We wanted to kind of identify how far the villages are from a
national highway or a state highway because we had a hunch
or an hypothesis that distance to Highway was a key parameter
in defining success of giving loans in that particular
Village. And so we had to painstakingly
collected data from GPS satellite maps and all that and
there was more alternate to it at all, but there are also times
when You can create proxy or derived variables, right which
give you an approximation of the missing data.
And from there. You can probably derive things.
Which can not fully give you the information.
But at least partially paint a picture for you.
So, for instance, the one metric we created was ratio of
industrial workers and that give us a proxy for how How much
migration has happened to a particular Village, or how much
migration has happened from a particular Village?
And to proxy for some variables, we can often use, but when
nothing helps, I think you'll have to go back and collect
data. And this might be caused by a
bias of my prior background in Academia itself.
Right? So, I think I just because I've
never been an academic. I just end up using proxies all
the time. So you can give you some more
examples of the interesting examples of proxy State.
I mean this industrial workers was a was an interesting one.
I mean could be in any of your work.
Like it's always fun to look at what kind of proxies people use
for what in cricket, right? For instance.
We wanted to get boners often get most of their wicked in the
last sad thinking about T20 matches right Bowlers.
Often get their wicked in the death overs and this could often
be because of mistakes made by batsman or by the bowlers
Brilliance itself. So we created a metric, which is
basically how many characters were caught in the field as a
proxy for how good the bowler is, right.
So if more catches are taken in the Outfield, we attribute that
wicked, as wicked witch just happened because it Batman was
taking a risk, whereas if it is due to an lbw or a clean bold,
attribute goes to the bowler. So if feel they're dependent
wickets and feel the independent wickets, and that was kind of a
proxy of how good a boiler is. For instance.
Ebola-like shower dual tracker. He gets a lot of wickets in the
death toll was right and probably, he is equivalent to
boom, right in that sense. Right?
But most of whom are circuits are Fielder independent wickets.
Whereas shardas, Ricketts a Fielder dependent wickets.
So you get a sense and that's a proxy for how good a boner is in
that. Sense, right?
Okay. I'm glad I asked you this
question because I be proxies are always fun than this.
The sensor, it I mean, because it really, it always makes you
think about things that you would normally think of like
aspects that you would sort of, because if you sometimes kind
of, if you have the freedom to collect the data, you would have
probably, I mean, in some cases, you can't really run an
experiment for this like inner life, which obviously their life
cricket match. You can't run an experiment.
But like I feel like you can But yeah, it allows you to think in
the in this manner answer. So coming back.
I mean like so I think you spoke about how in data science, you
in Industry you end up having to build a product and things like
that. So another aspect I guess isn't
just in terms of communication, right?
Because not every data science problem the leads to a product
decision in some sense, right? In some sense.
It's more like it could be a business decision.
For example, in your micro Finance Company.
I'm guessing one of the problems they might face is like, where
do we have? Of our officers, sir branches
and things like that, which is a business decision because you
can't, you can't give them a GPS, XYZ to say that you need to
put your office exactly here in. So on.
So in terms of communicating your results, make I mean, what
are the challenges in terms of communicating the results of the
model that you have built, how what are the challenges in terms
of how is communication of the results of an experiment /,
results of a problem different in Academia?
Compared to the data science Industry.
In fact, I'm glad you brought this up, right?
This particular question, which you gave an example of which is,
how do you create more branches? And that was one of the
questions we addressed where communication became an issue.
Also, right. So what happened was mudra had a
lot of branches in Tamil Nadu. And so we looked at the data of
Tamil Nadu and identify like things.
Like this industrial worker ratios importance of villages
where the industrial worker ratios High.
The repayment is better or distance from national highway
is an important metric. So we took all these metrics and
they wanted to open branches in Karnataka and Maharashtra
looking at this particular data. So I told them who can give me
the tough for villages from Maharashtra and Karnataka.
Let me run the model on Tamil Nadu and give you Villages where
you can open branches in. Modesty and what happened was we
were not getting any Villages which had scores, you know,
which fit the community explanation.
And we could not understand why that was happening.
And then we had to talk to the field team and they also
obviously at that particular Point didn't have any answers.
So we went back to data and what we realized was Tamil, Nadu is
heavily industrialized state that has colonel.
Attica and Maharashtra were highly agree States.
So in the solutions, which are modeled through a portal gonna
do, just doesn't make sense in Karnataka and Maharashtra.
And so, you had to throw out that parameter and rebuild the
model to make sense of things over there.
And this, and also another thing, which we face, which
comes through for the communication point, right?
We gave a list. Of 100 branches, which can be
opened in Maharashtra and Karnataka with the field team.
Often came back saying things like but there's competition is
high, then people are not accepting us when we go there
and things like that, right? And so you need it to work
through in a sort of painstaking manner could filters remove
things, which don't fit. So that the field team is
equally happy deploying the solution which we kind of
created on our computers. He's in our offices.
Right? And the that yeah, that's where,
I guess the communication comes in communication and context,
right. Sometimes while solving data
science problems. We just don't understand what
are the challenges faced on the field by the team, which is
actually executing the problem and they don't understand why we
are saying these are good answers and a lot on a lot of
back-and-forth communication probably helps it a bit.
But what helps most is you traveling over there with them
and you know, spending a few days and understanding the
context yourself. Oh, yeah, either that can that's
super important. I mean, in my purse in my work,
I mean, I work for delivery, right?
So I mean, I had built a model for something away.
I forget what it was and I discussed it with my team and
these viewers sort of Fairly happy with the model and then
one day, we decided like let's go.
Look at the operations of our have been back door.
So three of us went there. We are looking at operations and
then one of my teammates is like Just look at how they're
collecting this data. You do take that into account
while building your model, and I was like, I was like, no.
And that's when I realized my model was like, completely of
the monk. So, it's events like this, which
Make Me Remember that. I'm not solving a math problem.
I'm solving a real life industry problem right.
Now. You just exactly the same for
you, sort of going to your microfinance locations to
spending time with the, with a team, I guess.
Because that's Pretty much the only time you kind of it's
almost like you are G where your entire model is sort of getting
calibrated to the market in some sense, right?
When you when you sort of like when you do that to do.
So the question again, like I think we discussed briefly a
while back is, how does it tie in with speed?
Because I think in the industry, especially when you are having a
it necessarily needs some back and forth, right?
Because you give a model, they will tell you.
These are the issues, so you re work on it and so on.
So I guess in that Since you have to at some level prioritize
speed over accuracy or speed over correctness of the
solution, its own. So, how did you sort of have?
Which I guess, is very different from Academia where, I want six
very different from Academy in Academy.
You learn one thing that you need to be patient and you need
to do things. I treat every hundreds of times
before you actually get it, right.
So in that particular sense, it's okay.
In the cell where You quickly give one solution.
You don't have, you have seventy sixty seventy percent accuracy
you work on it and you edit the solution, but I guess this I
traitor model Works probably in any field and it is it is
something which should work well in data science also and like
you said once we get to the field once you understand the
context better and that takes time, that takes its own time,
we can I treat if we improve the model.
Well, and I think that hydration is only solution forward here in
that sense. How is the communication
different? Because I think again in
Academia, you're used to communicating in papers, I
guess. So what is the transition there
in terms of like because I think your papers I'm guessing don't
work unless you're in a job I guess.
Yeah. So yeah, and I'm glad you
brought this up because yeah, I continue to publish some papers
and they are research papers. They are.
To read the often. I do know how many people read
it. But initially when we started
off messy fractals, I still had this enthusiasm of publishing
research driven paper. So we used to put out a lot of
white papers about things, which we have created, which we have
done, which we have hypothesized.
And but over time, I think I have moved more to communicating
through blogs and articles. Simplifying things to giving one
message at a time and in a research paper, you don't try to
do that. You try to bottle up a lot of
things in a very compact way. Within the given word limit with
few figures and lots of supplementary figures and you
try and put it out. Whereas now, I just change the
way I'm communicating. And then realizing this is only
because you brought this question up because it's been a
few years since I even wrote white papers initially.
We were quite interested in writing out.
Why Papers and you know, publishing at least detail level
of what we are doing now. We put it out in blogs.
We do the same thing, except we put it out doing blogs.
We try to give one or two simple messages at a time.
And there are benefits to both because now I'm optimizing for
more people reading. What I've put earlier, I was
optimizing for putting out my set of results and, you know,
documenting it in a perfect way. That's what I did during
research. Now, I am more about.
Okay, let more people read it. I think it will be more valuable
that. We, there are benefits to both
are good. Thing is also talking about
communication of results within the company and so on.
They like, for example, things like I don't know, like I build
a model and I'm like, I'm like, okay, here's a model to tell you
where to place your next branch and stuff.
Now, they'll ask you for an explanation of the model and
that explanation will be. I guess you will be very
different from what you would have written.
If you had written a Blog about it.
I'm guessing, I think I have learned to communicate better
over time when I try to speak as much data science in English as
possible. So for instance, I tell people
in the microfinance example itself.
If you live closer to Highway then that willage is much
better. Right?
And then they will give us anecdotal information.
It always works better. If the answer also comes from
the field team, right? Because As they have a sense of
this, pollution in their own way, and if it comes from then,
obviously, the understanding is a lot greater than me saying, I
build these eight models and the model has thrown up these 16
solution. And this solution is the most
optimal for, in your case. I think that probably doesn't
work. So I guess your journey has been
such that. Like you've figured out these
things. I figured out how to
communicate, figured out, how to use the data that's available.
It's one another. Question for you, if they can we
generalize a little bit. I mean like I don't know like I
unfortunately, I don't know too many other people with a PhD in
the world working in data science and so on.
So like I don't know about your network, but like if you know
more people is this how or if you have worked with more such
people, is this how everybody approaches things are like or
are there challenges in terms of like how you approach the
problem mind like whether you can make this transition to this
kind of communication. And I think, I guess, Everyone's
situation will be slightly different depending on the
context. They're working and right like a
friend of mine. She was doing Behavioral Science
Neuroscience then at lab and now she's doing an LP with company
and trying to optimize the language in which Things are
Written and try to get information from things which
are written and I do not know when it comes to communication.
I don't know how she has handled the situation and depending on
our particular context, like in my particular case, since we
were running the startup and there were 67 data, scientists
who are working with me, it became mandatory for me to
improve my communication with the data science team and with
the field team and that was a necessity, which crept in and
because of which, I am the way I am today, right?
And so How will the I guess communication is going to be a
challenge? I guess it's often a challenge
even in my case. But communicating in simple
language is one of the key skills.
I believe in data scientist should pick up.
And otherwise the value of a data science team will be
limited because you're as good as the feel team can execute it.
Right? Otherwise, your model is not
good enough. So so that is a mandatory skin.
I would think now with in data science rate, like a, we do you
think there are particular kinds of roles which would academics
better than other kinds of roles.
Like, for example, you have some people who kind of work more or
the product side like I work more on the The state where my
my inputs going to kind of making business decisions rather
than going into the products, or some people are closer to the
tech kind of code and write more deep models.
Some people have more in at the level of what you mentioned,
which is like converting are taking a business problem and
formulating it as a math problem for figuring out the context in
things like a. So do you think?
I mean I will be might be generalizing a bit, but I think
it's okay. I hope you are too but like like
are there. Any certain kinds of roles where
you think, like academics might do better than in other kinds of
foods. Academicians by Design are
trained to solve problems that have not been solved before,
right. So, when there are tough
problems, it's it's better to get an academician on it because
they are often not going to get overwhelmed by this.
So when it comes to problems like this, where we need to, you
know, build an hypothesis and, you know, take guess work going
forward. And then design a solution.
I think academicians will be better, climatized to something
like that. But converting a solution into a
product. I would say, is not necessarily
a skill, which academicians would be good.
Then because there are very few Labs at least in my field which
are designed to do that. In fact, at Sapien Labs, right?
Where I work with Cara that is one of the things we are.
Trying to achieve, which is take, insights, from research,
and try and build a product out of it.
And I'm seeing challenges because it's taking a long time
to actually do that transition from solution to product design.
So probably academicians are not highly optimized to do that
particular thing, but they are better designed at solution
design. Trying out a lot of different
solutions. I treating many time.
Those sort of things. I mean, in the course of that
conversation, so far. I think you've Bob sort of car
given a you've talked about, I think one or two problems that
you faced as part of your work with mathura micropenis.
So, how is the can you give me more examples of work that you
have done, like industry kind of data science work that you have
done and some inputs on how your background in Academia or
Neuroscience has actually helped.
In terms of how you actually went about the problem and so
on. Yeah, so microfinance was one
thing I did. But when it comes to safety and
Labs, right, so we created a lot of metrics, which is basically,
we looked at lot of EEG data and converted them into metrics,
such as complexity. So complexity can be thought of
as a proxy for entropy in the brain.
So how different spatial parts of your brain are communicating
different signal. So if all parts of your brain
are saying different things, your complexity score is higher
or the entropy is higher and we designed these sort of Matrix
and we try to relate these metrics to more real-world
things. Right?
In this case. It was travel, mobile usage, and
things like that. So designing these metrics, I
mean it involved a little bit of Processing little bit of data,
little bit of Neuroscience. So I guess that's where my role
came in here itself. So that is one of the things
which I did and when it comes to my experience in Neuroscience
itself, right? I believe, I bring two
Dimensions when I look at any problem, right?
I'm always looking at it in a data or a mathematical.
Little scientific way that is one second.
It also gives me a Behavioral Science view of things.
Right? Like how can I nudge the user to
do these things when it comes to a data product and these these
are the things which I kind of bring to play it when it comes
to cover Dia de right? It can come to website design.
It can look, we look at a lot of data of users who come to our
website and try to design a website based on.
The preferences and things like that.
And here we, I try to bring in two Dimensions whenever I look
at this problem and these are things, I think I picked up
during the course of my PhD. And we used to not essentially
only look at data but we used to talk about a lot of other things
which made the brain and unique sort of organ in your body,
right? So, yeah actually like me I want
to dig deeper into the seventh. I think a couple of nerds back
you mentioned about the Easy and entropy in the brain.
It's one. I read them have sort of,
especially interested in it and you can't afford it on is
because for me like because I keep talking about having a
traffic jam in my head sometimes because there are too many
thoughts and like it's like they're all clashing at I have.
I just lose track of what's happening.
So so just I mean, I know it's possibly very tangential but can
you talk about the this entropy? And I mean, I'm also very
interested in it would be because of Not because of
thermodynamics but because of information Theory, so can you
just talk about the entropy in the brain?
And, and so on, I mean, just for and I'll talk about one of the
results which we got, which is related to this entropy measure,
right? We call this entropy measure as
complexity. So we took a study of around,
400 people across different socio-economic strata in Tamil
Nadu and we gave all of them shrimp.
Test similar to IQ test rate. It's a pattern completion sort
of test. And we realize it's this
complexity metric was very highly correlated to how much
they scored in those tests. And that's when then we did
another study and looked at how much these guys traveled like
the same guys and we realize this complexity is equally
related to the amount of person travel.
So if a person is able to get out of their comfort zone, try
Well, too many places complexity is often higher and this
compound. Higher complexity is also
correlated with higher score in your pattern test.
Right? And so I'm not necessarily going
to say that higher entropy in the brain where like traffic jam
is correlated with high IQ. I mean, that will be not fair
for me to say that. I also want to ask you about the
negatives of having it a traffic jam in the head have user
noticed. Any other Spelled like this High
entropy or complexity is sort of correlated with sort of some -
info, if performance on, whatever metric mean.
I don't know if you have no. No, I've not seen any that
still. The thing with science is that
one of the time it's a negative result.
Right? What you're actually saying, we
usually use sort of negative result is the positive effect.
It's a negative correlation is not a negative result.
Rate zero correlation is a negative result.
Ain't ya zero correlation is a negative result.
But what I'm saying is I don't think I have seen any this thing
because you go into the hypothesis, right?
And you try to prove it pretty great.
Huh? So in our previous podcast, in
our previous recording, I think we spoke about you are talking
about one particular project that you read about the about
particular kabaddi player, and how his balances.
And I think you were telling about the center of gravity at
the exhibit. Maybe I think this is a To end
today, maybe that's that could be a something you could talk to
us about maybe with the context of your research background,
everything about how we went about, how you went about
solving the problem. Right?
Like I mean, I think we have here it equally spoken right now
in terms of like raving, the problem, getting the context,
then building the model, kind of zinc.
A if you could I hope I'm not throwing you off guard.
But like if you could take us through that Yeah, I can
definitely take you through that.
So, there is this kabaddi player called for deep neural, and he
was creating havoc in all the teams that we were working with
at that particular time point. And he's a reader and he used to
score points at will and win matches at will and basically
end up winning tournaments at will, right.
So with / deep level in the team.
That particular team partner, Pirates won the tournament.
Three years in a row. And so everything ended up in
this one question that, how do you stop for the nerve on,
right? And that's what the data
question, kind of summarized to write.
And we looked at a lot of data. There was no systematic Insight
when we just looked at which Defender was getting in out, or
what technique gets him out. And there was no, there was no
light at the end of that tunnel. Right, so we had to take it to
another step deeper. So we went into the biomechanics
and that time, I also had intern, who was working with me,
and she was a physiotherapist. So she was interested in the
biomechanics of the body, right? So, it was a good opportunity
for us to address that question at that particular point.
And obviously like, in most things in comedy, there was no
data available to solve this problem, right?
And so, we took a few Videos of perimeter walls with braids and
we marked out different points on his body, including his knee,
the angle at which he's bent. And because the skill, which
he's famous for is called the dookie.
So two keys, essentially, a deep neural, going parallel to the
ground, and he'll just be a few say 10 15 centimeters off the
ground. So you can bend that Loop and he
goes between two Defenders and escapes.
So and people think he's going to get out.
So they pile on top of him and he ends up getting six to eight
points. There.
It is. Got a record-breaking heat
points in a single rate, right? Because of this particular
skill, right? So the idea was what is it that
he's bringing to the table. And when is it that he gets out.
So we separated all the reads into two kinds of raised
successful raids by / deep where the defender Dash.
Is that him or hold him. Whatever, he does and
unsuccessful rates by per week. And we looked at these two data
sets and we marked out different parts of his body, where his
center of gravity was, you know, what?
He was doing during the raid across time, right?
And that's when this result kind of grew up that every time, a
beeps balance is kind of off that is his center of gravity is
outside his body and at at that particular point when the dash
happens from the defender, he gets thrown off the court and
the defender wins the point. And now this is incredibly tough
for us to communicate with the cupboard deep layers or
Defenders of the team. We were working with, right?
And so we try to simplify it and told them that every time he's
on one foot. That is when the dash has to
land, but I don't know whether we were able to communicate it.
Anyway, we like I told you he wrote a white paper about it and
we documented this result. But and we simplified this
insight to the team saying that make sure that / deep is the
third player out so that he never comes back before you get
the team out. Okay?
Okay. Okay.
It is about the communication.
There would have been like real challenge.
I'm guessing because it these are like comedy players.
I don't think. Anybody would have spoken
English, you would have to kind of like we have in sport itself.
That is the case, right? Karthik, in sport itself.
I felt communication of data Concepts was incredibly tough.
So we try to break it down into saying that puts people
understand videos better. So we try to play a lot of
videos and they try to, you know, they have to gather the
inside themselves. So we try to show a systematic
set of videos from which they can pick.
That sort of insight and that worked in the case of badminton
as well as a birdie in badminton.
They were some other things, right?
The coach really helped us. So we wanted like a player like
Saina Nehwal to wait before she puts her so bright, so the coach
does wrote on a piece of paper saying tie your shoelace every
Five Points. So that's the way he
communicated that message to him resting interesting.
Yeah, right. And so yeah, communication is
stuff. So we've created tools where the
players can, you know, see one set of videos and we can bias
them to see some particular kinds of videos so that they can
in green that insult was very interesting because I will, I
mean, I'm sort of how many General begun data,
visualization. So I keep talking about how you
need to make. It make the visualizations
subject. You control the narrative to the
guy who see. So in some sense.
I mean, so in sport, let's say like, your you You can show a
bar graph that gives just tear the piece of paper instead.
You just have to show up a bunch of videos.
So in that sense, I think your skill is in choosing the right
set of videos such that he can learn for himself from those
videos, what your data kind of showed you in.
Exactly. Exactly.
You hit the nail on the head, but it is not always easy.
But yeah, it will take time. There are players even in
kabaddi. They were players who actually
used to start following the videos try to.
Prove themselves, but it has to come from within in the end.
Right? Finally, then, yes, over to
close the conversation if I were doing a PhD now and I've figured
out that my academic career is going over.
I don't want to do a postdoc. I don't want to play the
academic game, its own, and I want to get into the industry
and getting to data science. What would your advice be to me?
Like, how would you, how would you kind of advised me to go
about my career? What kind of jobs to look out
for what? In terms of work and so on.
So I would say one of the things which is essential if you need
to come into a data science, role is to understand how
products are built and if a little more of that can go into
research while you're doing your research, I feel even your
research will become more compact, more usable down the
line, and you are going to learn a sort of skill, which is going
to take keep you afloat. Right?
You're going to be able to build out a product.
Duct and not just a small data solution.
For the whole thing. That would be one skin.
I I wish I had taken a when I was doing my research.
Thank you for listening to data shatter.
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exciting work. Thank you.