SDS PODCAST EPISODE 115 WITH NEELABH PANT · 2018-08-21 · got so many crazy ideas of how to apply that in business, he actually applies it business and he applies that in real life

Show Notes: http://www.superdatascience.com/115 1

SDS PODCAST

EPISODE 115

WITH

NEELABH PANT


Kirill: This is episode number 115 with the super-energised data

scientist Neelabh Pant.

Welcome to the SuperDataScience podcast. My name is Kirill

Eremenko, data science coach and lifestyle entrepreneur, and

each week we bring inspiring people and ideas to help you

build your successful career in data science. Thanks for being

here today and now let’s make the complex simple.

Hello ladies and gentlemen and welcome to the

SuperDataScience podcast. I just literally got off the phone

with Neelabh Pant and this episode blew my mind completely.

I really hope it’s going to blow yours too because Neelabh has

so many stories to tell about data science. We had so many

laughs and as you probably noticed this is a much longer than

usual episode just because I didn’t want to stop talking. I

didn’t want to stop talking to Neelabh, it was so great. Neelabh

has worked in geospatial analytics, he’s combined that with

machine learning and deep learning experience, he’s worked

with genetic algorithms. And the most interesting thing is he’s

got so many crazy ideas of how to apply that in business, he

actually applies it business and he applies that in real life.

Well, I’m not going to ruin it for you, you have to check out

this episode. Whatever level of data science you’re in, this will

get you pumped about data science. Let’s get this thing rolling

without further ado, I bring to you Neelabh Pant.

[Background music plays]


Welcome ladies and gentlemen to the SuperDataScience

podcast. Today I’ve got a special guest, Neelabh Pant on the

show, calling in from India. Neelabh, how are you going?

Neelabh: Pretty good, how are you?

Kirill: I’m going well, thanks man. And right off the bat, how did we

meet? Tell us the story; we were just chatting about it just

now, it’s such a fun story.

Neelabh: It’s exciting. It’s just an amazing story. As a matter of fact, I

really got into data science so much so that I just learning

from online resources wherever I could find. For the first time,

I saw Kirill was on YouTube for one of his promotional videos

for SuperDataScience.com and then I started following him

there, started reading his courses like data science, machine

learning, Tableau and I was like, man I just want to see this

guy because this guy is amazing. I want to see him in person.

I’ve been told, when I was a kid, that dream about anything

and it comes true in your life because universe is there to

grant your wishes. And I was like, I want to see this person in

person, I don’t know how and suddenly I get this huge

advertisement saying that, hey, Kirill’s coming to San Diego,

SuperDataScience GO, and I’m like, I am getting the ticket

right now. First night I landed in San Diego, went to the hotel

poolside, Kirill was there and the moment I saw Kirill, Kirill

saw me and we recognized each other and we hugged, and it

was just an amazing moment.

Kirill: Yeah, it was love from first sight.


Neelabh: That’s what I told my colleague and he was kind of jealous,

but I was like, I’m enjoying this moment. It was amazing.

Kirill: Yeah, it’s funny because as you say this, in my hand I’m

holding a pen from the Data Science GO event, it was such a

blast. I don’t want to blow my own trumpet or paint a picture,

but I personally thought we were doing it as more of an

experiment, can we pull it off or not. I thought it was really

great. What did you think of the event?

Neelabh: It was. Indeed, it was such an amazing event. You call it

experiment, I call it the super hit event. That’s what I call it.

Kirill: Thanks. It’s all the dancing. Because you definitely weren’t

expecting the dancing and the motivational speakers there,

were you?

Neelabh: Oh man. My favourite part of all the motivational speakers

was Laser Sharp Commitment from Ben Taylor and Being

Compassionate by Urie Suhr. The entire ambiance was so

positive, everybody just wanted to learn more, grow more and

just be amazing. That’s what the entire ambience of that hotel

was. The dancing really got my blood flowing and it was just

amazing. I cannot stop saying it because it was just amazing.

I’m waiting for 2018 SuperDataScience GO.

Kirill: Yeah, me too. Can’t wait, and that’s going to be epic. Anyway,

getting side-tracked. We’re here to talk about you, about your


career and the amazing turns and twists that you’ve had in

the path that has brought you to data science. Are you ready

for this?

Neelabh: Oh, absolutely. Yes.

Kirill: Awesome. I read about your bio a little bit and also checked

out your LinkedIn profile- very, very impressive. You started

off in a Bachelor of Computer Science in India, and then you

moved to Texas. Let’s go from there.

Neelabh: All right. Back then, I was just another teenager who loved

technology but at the same time, I was struggling so hard with

keeping up with all the technology, programming languages,

algorithms and data structure. Somehow, I made good grades

but at the same time I wasn’t, for some reason, enjoying it,

probably because of the fact that back then I didn’t have

much of resources. Whatever resources we had in our school,

it was probably more than enough because then I realized that

I really want to do some great thing in the field of computer

science and technology. By the time I completed my

bachelor’s, I wasn’t very much happy with whatever I learned

in my bachelor’s. To be honest, it wasn’t enough. It was,

according to my standards, it was nothing at all. I really

wanted to do something extra and then I planned to move to

United States, I wrote GRE. As a matter of fact, I was the first

person in my college to write GRE, so now you can imagine.

Kirill: What is GRE?


Neelabh: It’s Graduate Record Examination. It’s a standardized test for

international students or even students in United States who

want to pursue a graduate programme in computer science or

STEM, which is science, technology, engineering and

mathematics. I was the first guy from my university to write

GRE. I scored well, I was happy about that, and then I started

applying to different universities and I was fortunate enough

for UTA to accept me; University of Texas in Arlington is where

I did my master’s from. Then in 2014, January, I flew to Texas

and then I started master’s from there.

Kirill: Nice, very nice. Bachelor of Computer Science, Master of

Computer Science and what was the difference? Did you feel

that in your master’s now you were getting what you were

after?

Neelabh: Absolutely. It was just a 180-degrees change from what I

studied back in India and what I was experiencing then in my

master’s. It was just amazing, especially the resources that

you have in United States for any STEM student, it’s just

mind-blowing. You’ve got so much of resources that you

cannot … Don’t take me wrong but sometimes you don’t even

have to go to classes if you really want to study on your own

because there are so many online resources, digital libraries

and people like you providing all those courses and stuff.

Going to the classes is just cherry on top because then you

actually get something really professional. I started my career

with spatial databases. I was a thesis student in my master’s

and I really got a lot of interest in spatial databases, dealing

with maps and geolocation services and stuff like that. I got a

really huge interest in this field and especially in the indexing

part of the spatial database. I was like, this sounds really cool.


I really studied hard for that; days and nights and finally did

my thesis on comparison of different spatial indexes and

which one is the most efficient in certain conditions. In

databases, especially in spatial databases, you’ve got different

query types, so for a specific query, this index is better, for

another query, this index is better. Or maybe the indexing

system, which is really amazing, has its own trade-off where

it takes a lot of time when it’s getting entire data and making

the index on the entire data set. That was a very extensive

research that also got published as a research paper, and

that’s what my master’s thesis was all about.

Kirill: Before you go continue, can you get us up to speed because I

think maybe some people are lost, including in me. What do

you mean when you say, “indexing of spatial databases”? Can

you give us an example or something to just understand that

concept there?

Neelabh: Sure. If you talk about a database, we solve all these

databases like B-trees, B+ trees and the regular indexes that

hold some values. The regular data types, the integers or

strings, or stuff like that. Spatial indexes, I will give you two

examples. One is the R-tree it’s known as Rectangle Tree.

Another one is the newer version of R-tree which is R*tree

which is Rectangle Star Tree. All these indexes do not just

store the characters or the strings on integer objects, but they

also store objects like spatial objects, like points, lines, and

polygons. Whenever you’re trying to index something like how

far is New Jersey from Manhattan. Such kind of indexes have

polygons of New Jersey, polygons of New York State, stored in

their own indexes, and then they do the distance algorithm

like Dijkstra's algorithm or stuff like that. But the access time


by using all these indexes, gets so much faster that the

computation of such higher spatial objects becomes really

faster, optimized and efficient. Just to access much faster all

these spatial objects, we use spatial indexing system instead

of using the regular B-trees or B+ trees which are highly

inefficient when you’re talking about points, lines and objects,

there we have to use spatial indexes like the R-trees, R*trees,

Quadtrees, and there are many more others.

Kirill: Tell me if I’m getting this right. Let’s say I have a map, it has

all these polygons, all these lines and points. One way of

indexing is like go through every single object and just give it

a number, first object, second, but that’s going to be

inefficient. You guys come up with spatial indexing systems

that say the big polygons get numbers 1-10, the smaller

polygons inside the big polygons get numbers 10-100, then

these lines all get these… That’s just like a very basic example,

but it’s a smart way of numerating the objects so that you can

navigate through them better and run your algorithms and

queries much faster.

Neelabh: Exactly. You’re absolutely right. Let’s say you’ve got this two-

dimensional plane over which you’ve got the entire geography,

what R-trees or R*trees do is they put the rectangle on top of

it. By going with the algorithm and by going with the

hierarchy, they start putting more rectangles over a specific

geography so that we know how much granular a certain

rectangle can access the data from this specific geography.

That rectangle will only have, say, north-west of geography of

the entire map, another rectangle will have the south-west of

the entire map and now we actually know which rectangle is


holding which geography over that two-dimensional plane, so

that becomes more efficient to access all those geographies.

Kirill: Very, very, interesting. I didn’t know this actually was the

thing until now and I will pose a question to the audience.

Ladies and gentlemen listening to this, just ask yourself, have

you ever used Google Maps? Probably everybody has, and we

just take it for granted all the time, but I’m pretty sure they

have something similar going on in there. What would you

say?

Neelabh: Absolutely. I wouldn’t say exactly which indexing system

because I’m sure Google would have come up with a more

scalable approach because you already know there are

billions of users who are using Google Maps every second of

the day. But I’m sure these types of technology, other ones

that all these routing companies are geospatial companies. As

a matter of fact, the biggest one which is Esri, Environmental

Science Research Institute, which is the creator of GIS, that

is the biggest GIS company in the entire world, their

headquarters is in Redland, California. These guys use

indexing like R-trees, R*trees. There’s another one, GiST, it’s

known as Generalized Indexing Search Tree, which uses a

combination of R-trees and R*trees. All these huge

geographical information system companies are using these

indexings and as a matter of fact these indexings are also not

new. Some of them came out in late ‘80s or early ‘90s so

they’re fairly old and they have been extensively used since a

long time.


Kirill: Gotcha. I’ve actually used Esri myself on an engagement once.

When I was working at Deloitte, they preferred a different one.

Do you know Pitney Bowes? We used them for geocoding but

then then the program that we were using was called

Pathfinder, I think, I’m not sure. I found it very interesting to

work with georized data because you get very cool insights.

For example one of the projects I was working on is when you

need to estimate drive time from one location to another,

instead of just doing a circle around your location … Or what

is your customer’s catchment? Instead of doing a circle

around your store, you actually use the roads because

sometimes roads help you go faster, sometimes roads make

you go slower, and based on that catchment, drive time

catchment is different to just a circle around your location.

That helps build your customer profiles better.

Neelabh: Absolutely.

Kirill: Awesome. Good to hear some insights into how these indexing

systems work. Is that what you’re doing your PhD on at the

moment?

Neelabh: No. Right after master’s, I was accepted as a PhD student

under Dr Ramez Elmasri. If somebody loves to read about

databases, Dr Elmasri is one of the big guys in the field of

databases. I was fortunate enough to be a student under him

and then shortly after that, I started doing my PhD back in

’15, again in indexing systems but a funny story, that

semester, I wasn’t getting any courses at the university. As a

PhD student, you still have to take a couple of courses and

the only open ones were machine learning and neural


networks. As a matter of fact, not neural networks but then

machine learning was the one which was open. I enrolled

myself in that course and ever since then, I did not look

behind. I started machine learning days and nights and I told

myself that I’m probably going to get married to machine

learning. In 2015, I was also introduced with Dr Andrew Ng

through his course on machine learning, I think it was on

Coursera. I was introduced with Dr Andrew Ng and that

course really opened my horizons, it really broadened my

mindset regarding data science and how can machine think

and what kind of cool stuff that machine can do in today’s

time. The biggest takeaway from that course was I really got

good in Octave and Matlab. I actually started coding. I started

coding in C before, but ever since I got introduced with Octave

and Matlab through Dr Ng’s course, I was like, wow, machine

learning is just so cool, it’s just probably the best that could

ever happen to a computer science student in today’s time.

Kirill: Nice. But why did you think that? There’s lots of people out

there who haven’t tried machine learning, what would you say

to them? Why did you think it was the best thing that could

happen?

Neelabh: To be honest, Kirill, I think this was probably the only thing

in my life in computer science that was tangible to me. This

is something that I could feel from my heart and everything

made sense in machine learning. Whatever I gave to the

machine, whatever I was expecting it to do, it was coming out

of the machine and in such a logical sense. Everything made

sense. Besides that, I was able to see the future and that’s

what I have always dreamt ever since I was a kid. I was able

to see what’s going to happen next before even that thing


happened. That was the thing that really got my attention. I

was like, wow, I really can predict something even though I

don’t know anything about it? That’s what machine learning

really taught me. I would definitely say that that’s probably

the best thing that could have ever happened to me.

Kirill: That’s so cool and it’s great to hear the passion in your voice.

You’re speaking, and I can feel how passionate you are about

this. It’s even more exciting that this random chain of events

happened that you signed up to this machine learning course

and you discovered it. How crazy is that? Like you say, you

ask the universe for things, it will give them to you. And here

it’s just like: by the way, you’ve got to be passionate about

this, how about you sign up for this course?

Neelabh: Like I said, this was the only open course. But at the same

time, I did not have any other option, I had to go in the course.

It was like universe is calling me to be a part of this data

science community. It was there for me, it was just waiting for

me to get in there and realize what potential I have in this

field. There you go, and now we’re talking to each other.

Kirill: Exactly. That’s crazy, that’s crazy, man. Tell us a bit more

about your PhD. Without going into too much technical

details, like at a high level.

Neelabh: What my PhD is all about, is about seeing the future. I’m

trying to see where a user is going to be next in his future

time. I’m trying to predict the future locations of a user based

on his historical patterns which is coming out of his GPS data.


I’m trying to see … until now this user has been here, here,

here, and if you know humans on a typical day follow the

same routine, no matter what. He might take days off and go

for vacation or whatever, that can be considered as outliers,

but on a regular day, he will still perform a regular behaviour

or pattern, what he has, or she has been doing since a past

couple of years. But at the same time, again, it changes over

a period of time, so I’ll give you an example. As a student at a

university, you are enrolled in some specific number of

classes for a semester, so you are going to different buildings

or building A, B, and C in this semester. Now the semester is

over, and you enrol to different classes, now the buildings that

you go are D, E, and F, so suddenly, within six months your

entire pattern has been changed. I’m trying to make a

machine or a model that can intelligently understand the

pattern of this user such that it can predict where this user

can be in the next given position or the next given time. The

applications of this model are just out of my mind, there are

so many applications. One of my favourite applications could

be recommender systems. All these different companies can

advertise their products based on where the user is going to

be next. Let’s say a company like Walmart, you’ve got a

Walmart app on your phone and then the Walmart has been

recording your data, of course they have to take your

permission because that’s a really sensitive data. But if you

happen to give your locations to Walmart, Walmart can make

use of this model and can predict, looks like tomorrow at 12

pm, this user is going to cross Walmart at this street, let’s just

start giving all these recommendations or advertisements or

maybe coupons for this user just to come in while he’s going

to some place. Because he’s going to cross Walmart, that’s

what our model tells and it’s a win-win situation. The user

gets coupons, Walmart makes more revenue. That’s one of the

applications that comes to my mind right now.


Kirill: That’s mind-blowing. When you just said it, predicting where

a person will be, that was kind of cool. But when you put it

into context of how you can apply it to a business situation,

that’s just crazy. It’s on the verge of sci-fi type or even … you

know what I mean. It’s kind of very invasive even, like you’re

living your life and all of a sudden you have this coupon for a

store that you’re just passing by, that’s crazy, man.

Neelabh: Yeah. There are so many applications that me and my

professor were talking about.

Kirill: Can you give us another one as an example, in addition to

this Walmart one?

Neelabh: Another one that we came up with was about the medical

insurance companies. Insurance company is kind of a

business where they just want money but at the same time

they try to not pay as much as it is required.

Kirill: Yeah that’s their business, right. They’ve got to balance out

the risk and the revenue, it makes sense.

Neelabh: Exactly. So, let’s say all these insurance companies

whosoever it is and if they have their customers, they ask

their customers to give their GPS data to them. It’s going to

be totally disclosed from public appearances or whatever kind

of contract they’re making with their customers, just to make

sure that their security and safety is not breached. Once they


have the GPS data, now they know where the user is going to

be next at every second of the day or every hour of the day, so

on so forth. Let’s say they recognise, let’s say there was this

hurricane in Texas. Hurricane Harvey and then Hurricane

Irma which really hit the coastal side of Texas. They already

have predicted that the hurricane is going to hit that place

but at the same time a user or their model has also predicted

that their customer is going to be somewhere around that

area. Now look at this. Be it medical insurance or be it car

insurance, if both of the companies have similar data of the

user, the car company can tell the user not to go there

because it’s kind of possibly risky. Health insurance can tell

the customer not to go there because it’s possibly risky.

They’re telling this information to the user a month or two

months prior to the event, because they have already

predicted where the user is going to be. Even if the user is not

going to be at the similar location, they can also predict the

entire trajectory that the user is going to be traveling so they

can probably ask to take another route because this trajectory

is probably broken or there has been an accident taking place.

And that is happening way before the event actually took

place. So, when you can start predicting the future, you can

actually make a lot of amazing business movements that can

actually harness the power of predictions, make more

revenues, save lives, give coupons, make recommendations,

whatever comes to your mind. This is just one model out of

which you can think about millions of possible applications.

Kirill: That’s really cool, man. I love that description and like saving

lives, that’s amazing. I wish this would be implemented very

soon so that we can start getting the benefits of it. Also, you

mentioned on your LinkedIn that you’re studying deep


learning methods. Are they also integrated into all of this that

you’re doing?

Neelabh: We started with traditional machine learning by making use

of Markov models and hidden Markov models because those

are essentially the time series modelling. Then we realized

that hidden Markov models are really good when it comes to

speech recognition or text recognition because those elements

like speech or text, they are bounded. What I mean by

bounded is if you’re talking about English, there is a certain

boundary around English language that can be utilized in

your daily conversation. But talk about human behaviour,

human is an open system and the function or the movements,

the style of travel behaviour the human possesses, that

function is so complex that you cannot ever estimate. Once

you estimate that function, it gets so easy for you to predict

the future values. We then realized that hidden Markov

models wouldn’t be the best model, we should also start

looking into advanced predictive models and then that was a

time when I enrolled myself in neural networks class at my

university. Man, the moment I got into that class from the first

day onwards, I was in love with neural networks. I was like,

this is the game changer, and this is something that can really

be implemented in my research and I can do so much cool

stuff. Not just predicting locations, but so many stuff, so

many function estimations that can only be dreamt before.

Ever since then, my life totally changed. I even started talking

functions, I started talking to numbers, I started talking to

matrices. It was more like all these things, all these statistical

models and algorithms, are just around and I’m playing with

all of those. And so much so that I could use up all these

statistical models in my research.


Kirill: When we were preparing for the podcast, you mentioned an

interest fact about yourself. Do you mind sharing?

Neelabh: That’s a very funny fact. Unfortunately, or fortunately, this

did not happen with me once, but it keeps on happening to

me in recurring fashion. I’ll tell you my experience when I first

experienced it. I went to bed after my day of research and

studies. I slept and somewhere about four in the morning or

five in the morning I woke up and what I dreamt was I am a

cell inside a matrix and I hold a value, and my function, my

job within that matrix is to get the value threshold computed

and forwarded to the next following matrix. That day I cannot

ever forget. That morning I told myself I have given myself to

data science because I know that there is a reason that I’m in

this world and the reason is to fulfil the purpose of data

science. And I’m glad to be a part of it. That was crazy.

Kirill: That’s awesome. It’s definitely a whole next level of data

science when you do it in your dreams, that’s really cool.

Okay, so you’re doing your PhD, when are you scheduled to

complete it?

Neelabh: I completed my proposal this semester. A proposal is the third

stage out of four stages in PhD, and I am planning to graduate

next semester. As a matter of fact, I also got a full-time offer

as a data scientist from Walmart so I’m talking on as a ….

Kirill: Not surprised, not surprised at all. [Laughs] With your coupon

idea.


[Laughter]

Neelabh: During my interview, this guy was like, this was my final stage

of the interview. The interview went for almost a month and a

half. At my final stage of the interview, the director of data

science asked me, how can you leverage the power of your

research in our community. And I never even thought about

it, it was just then and there that I came up with this idea and

he was like, okay, whatever, even if you don’t get to work with

us, you are connected to me. So just be in touch because we

can do stuff together. I was like, man, this sounds really cool,

why didn’t I think about it before? But yeah, probably next

semester, possibly from the month of May I will start working

with Walmart.

Kirill: Nice. Congratulations, that’s awesome.

Neelabh: Thank you. Thank you so much.

Kirill: Very great to have an offer, a full-time offer lined up while

you’re still studying. It’s definitely an accomplishment. What

advice would you have for students listening to this podcast

who are soon to graduate? What’s the best way to go about

lining up a job for themselves even before they finish

university?

Neelabh: I would definitely recommend to plan way, well before time.

You cannot just leave things lined up for the last moment.


Start applying as much as you can but do make a list of your

10 most favourite companies that you really want to work for.

Make your resumes, tailor your resume according to those 10

and forward it to them, and just hope that you’ll probably hear

something back from them. But even if you don’t and if you’re

getting rejections, let me help you here. I got 150 rejections,

Kirill, before even I got my interviews from a couple of

companies. In your life, don’t be surprised if you’re getting

rejections because that’s part of the game; but make sure

whatever you are doing, give your 120% laser-sharp

commitment. Get married to whatever you’re studying

because then and there you will know that you can win it.

Don’t ever lose hope. Believe in yourself and you are going to

get it because you are worth it. Why else are you working so

hard on yourself? And I’m sure, I believe, if one is 2000%

committed to his job, nobody can stop him, and the universe

is there to help you out. So, yeah, rejections, that’s a part of

life. Learn from it and be better every day. You’ve got to beat

yourself every day. That’s how I try to live my life. To be

honest, Kirill, wherever I am, I know I haven’t achieved as

much, but at the same time everyday I’m trying to grow

because I know if I am trying, I’m going to make it. That’s what

I want to tell all these people, whoever is listening to this

podcast.

Kirill: Love it, man, love it. You should be a motivational speaker. I

felt you’re honest. That was so good. That was really touching

and I’m sure you’ve helped a lot of people just by that message

and I totally agree with that. Rejection is part of it. At the same

time, I’m sure there’s already a ton of companies who didn’t

invite you for interviews that would have they known what

you’re doing for your PhD and would have they known what

exactly, how you can help them with these great ideas, right


now they’re probably kicking themselves in the butt. Saying

that, damn it, we should have gotten Neelabh on our teams.

At the same time, you have a part-time job right now. Is that

correct?

Neelabh: That’s right. Currently I’m working as an intern. This

company is known as Metal Roofs of Texas. This is a house

improvement company, they help people to install roofs,

floors, glassworks and stuff like that but at the same time

within this company, the owner, Mr Josey Parks, he is

amazing. He is a 29-year old entrepreneur, very successful

person but at the same time learning every day. He thought

of changing this contractor business altogether and opened a

new company within the parent company, that is Metal Roofs

of Texas. This company is known as Cognitive Contractors.

What we do is, we are more like consultants. We try to get

other companies’ data and we try to help them in their

analysis because in the field of contractors, Kirill I’m telling

you, today people are so old-school and they’re still depending

on zip codes. And they are so much convinced by the fact that

okay, this specific zip code is the one where I can make most

revenue. They are not thinking about anything else, they’re

not getting demographics, they’re not getting house data,

they’re not getting even people, incomes data, or whatever,

you know. They are just considering that over the past five

years I’ve made so many million dollars from this zip code and

I’m not moving anywhere else. They’re just in this box and

they’re not ready to think outside the box. But this guy, Josey,

he has a vision saying that, I need to change the way this

entire- it’s known as blue collar- business is working. He’s

trying to get all these data which has been collected over the

past 20-25 years, trying to analyse them and we are trying to

target customers based on the revenue that they have


generated for this company. We are prioritizing our work

based on the customers who’re going to make us more

revenues and we’re going to look for the customers later who

are not making so much of revenue. Let’s say we’ve got these

… on a typical day we think that according to our model and

analysis, we think these five customers are going to make

more than average revenue for us, but these guys are really

spread across the city, so the biggest challenge for our

salesman is to contact all these customers so that they can

advertise the product and make more revenue. Again, another

problem that we face is the traveling salesman problem where

we have to make the most efficient route to contact all these

customers, but again in the prioritized manner. Again, we go

through with the entire analysis and we see which of the

customers can be more prioritized because those are the ones

which we really need to contact. And then again, now we know

that the past behaviour of this customer is something … this

customer is not available at this current time or at this

specific day, so we really have to take care of different

dimensions altogether such that we are making the most

intelligent decision by also saving more time and by using less

resources.

My sole job there is right now ETL which is probably 70- 75%

of my entire time in data science. Thanks to you, I really

learned a lot from your data science course with the SSIS and

SQL-server that is really playing. That is probably the NVP in

my entire ETL process. Once I’m through with the ETL, then

visualization and my favourite is Tableau, again thanks to

you. You’ve been an amazing person, Kirill, you’ve taught me

so much. Tableau has been such an amazing tool. Initially I

used to use – don’t laugh at me- I used to use Seaborn,

[Inaudible: 39:06] and Matplotlib.


Kirill: That’s not a bad combination. That isn’t excel for sure.

Neelabh: Yeah but again, drag and drop and making your dashboards

within like two minutes, that is a game changer. With the

usage of Tableau and [Inaudible: 00:39:24] and Python, my

work it’s really easy making all this analysis. And, interesting

fact, sometimes I don’t even have to use machine learning and

neural networks to make any sort of predictions because

Tableau shows me some sort of visualization through which I

can see that, okay, this visualization is following this specific

function, and based on that, we can simply make the

predictions. We don’t even have to jump into the complexities

of machine learning. As a matter of fact, I saw your video

where you were presenting, and I still remember you had …

exactly. I was like, wow, this makes a lot of sense and that’s

what I personally experienced in my job. And I was like, man.

If you’re into data science, even though you don’t want to get

into visualization, I understand but again have some basic

knowledge of Tableau, it’s going to really help you no matter

what.

Kirill: It helps with that data mining part. It helps you see your data

and get those insights quicker. Like you say, you might not

even need to apply machine learning if you can see that this

looks like a logarithmic distribution or this looks like a normal

distribution. It just makes things easier. And congrats on

getting … Because I know exactly in which part of that data

science A-Z course that [inaudible: 40:51] distribution

presentation is, of me presenting. And the fact that you’ve

seen that video means you’ve got to that part which is at the


very end. I watch these statistics, I monitor the statistics on

how people are going through the courses. Unfortunately, a

lot of people drop off but like that is like a surprise lecture at

the end of the Data Science A-Z course and the fact that you

got to it, that’s really inspiring. Good stuff, man.

Neelabh: I cannot even believe that. Why would some people drop it

especially when you are teaching? That’s like, what are you

doing with your life? Come on, it’s Kirill, you’ve got to listen to

him.

Kirill: Thanks. Appreciated. All right so that’s really cool. It sounds

like this owner of the company you say is very young, 29, and

he’s really changing the way this industry looks. That’s a very

cool way to disrupt the industry. Have you noticed any

positive effects of this? Has the company noticed how the

sales have increased or the customers are happier and things

like that?

Neelabh: Yeah. This guy is just amazing. This guy has such a zeal and

passion towards data and he’s the one who appreciates the

data more than anybody I’ve seen around my community. He

knows how to tackle it. The biggest problem right now with

me is I come from a computer science background and

computer science has taught me things like dealing with

different tools, ETL, cleaning, stuff like that. So, everything

that I’m talking about in computer science is always

technical. I am not so good when it comes to finances and

money, but this guy knows how to deal with finances and

money and even to convince a person to give his money to the

company such that we can offer him our products. With our


analysis, we have seen so much of difference in our own home

company. I actually started analysing Metal Roofs of Texas

data. We saw a difference in our sales and how sales people

started behaving in a way that the analysis was asking them

to do. Before, the sales people were just going randomly to

different people without knowing or without analysing all

these customers available and what time of the day they are

available. That is just one small example. After doing an

extensive research, I cannot go much into further details but

after the analysis, the sales people actually changed their

mindset. They know how data mining and analysis can

actually change the behaviour they have been contacting

customers. So much so that how can they even talk to them.

Because the way they were talking before, we analysed the

text data by making use of stuff like Wordcloud and stuff. We

also analysed which were the words which were most

occurring in the conversation with the customers.

Unfortunately, those words were not playing a good role

because some words are good in some context but at the same

time you shouldn’t use them. We realized that text data

analysis was the biggest game changer in the analysis in our

home company, and how sales people should communicate

with the customers. We actually saw a growth of almost 1.4

times. That was really a game changer and I am really pleased

that while I was there, working … I’m still working with them,

sure, but while I was physically there, I was able to see the

change in the mindset of people who are not data driven

before but now, again, they cannot step outside without

seeing the data first. That was really amazing to see while I

was there.

Kirill: That’s really cool. That’s a great example for the

entrepreneurs and directors and executives listening out


there. A great example of disrupting your own business. I

think Richard Branson does this in his companies that he

says … or I’m not sure who exactly, but let’s say Richard

Branson. He says like, okay, how can you come and create a

business that will put me out of business. He tells his own

employees that. How can you come up with an idea that will

disrupt my own business? And then, once they do come up,

you just adopt that idea. That’s exactly what you’re doing

here. You’re like, okay, how can we approach this in a

different way that if we were competing with ourselves, we

would put ourselves out of business. And then you just go

like, 1.4 times the operations revenue, customer satisfaction,

whatever, is a great competition with yourself and so you just

put it into action and that’s a really cool approach and hats

off to you guys for that. That was awesome.

Neelabh: Yeah. We’re just trying right now. I’m sure we’re going to be

good with the kind of business that we’re trying to get into

with the contractors’ universe.

Kirill: Yeah. For sure. I wanted to ask you. You mentioned that after

you did all these implementations, the sales people … Before

they didn’t think about using data and now they can’t even

imagine stepping outside or starting their work without

getting the insights from the data. Did you experience any

road blocks, or did you experience any pushback at the start?

Was it hard to integrate this culture into the company?

Neelabh: Yes. To a certain extent, yes. Like I said, you cannot just show

a new technology to a person and just convince him. The

person yet hasn’t seen what this technology can do because


he cannot just believe whatever you’re showing them, so you

have to give some examples before you convince another

person that, okay, this technology is meant for you. Now, like

I told you in this business, people are still not so modern

because they are really convinced by the fact that the way this

business has been going since years is probably the best way.

We had to try because that was an experiment phase. We had

to try first, do some analysis and let’s just do an experiment

on a typical day. The sales people saw a difference and after

that they started taking more interest in their sales. We were

talking about how this can change the pattern or the flow of

the information throughout the entire sales community and

the way they were approaching customers. Initially it was a

little challenging, but I knew that once they start seeing the

difference … See, again, human nature. Humans are such an

open system that it’s not hard to convince them, but it takes

some effort to convince them first. That part was the time

when I was trying to experiment with different analysis and

fortunately it worked. The initial phase is always challenging.

Kirill: Yeah, I totally agree. I think a lot of people … Why I asked this

question is I think a lot of people have or will in the future

come across this problem that when you have people,

employees or staff who are a bit more old-fashioned, a bit set

in their ways, have never seen this new approach, you will

have some kind of challenges. Because people usually are very

resisting to change, they prefer to keep things the way they’re

used to and the way that they know how everything works.

Neelabh: Yeah. It just takes some effort. The initial phase even in any

predictive modelling it says you still have to show some data,

that’s why supervised learning is a little better than


unsupervised learning. Because you try to show the data first

and let your machine learn and make some sort of

predictions. And that’s what is with the humans because, I

know, I mean, humans are reinforced but at the same time if

we can show them something that can be helpful to them, it

actually makes more sense to them after they have

experienced it before.

Kirill: All these humans, they make me laugh every time.

[Laughter]

Sometimes on this podcast we get carried away talking about

humans, it feels like we’re not humans, it’s funny.

[Laughter]

And I love your … this is a great quote. Humans, of course

they’re reinforced, but sometimes they need something to

show. It’s just crazy, right. Humans are reinforced.

Okay, I got an interesting question for you. You’re very

passionate and very well-versed in lots of topics. Very diverse

topics as well from GIS spatial indexing to machine learning,

neural network, also I saw in your LinkedIn, genetic

algorithm which we haven’t talked about. That’s a whole

different can of worms, right?

[Laughter]

But before we jump into that, if we even do, I wanted to ask

you, is there anything else on the horizon for you that you’re


passionate about learning, that you can’t wait to get your

hands on?

Neelabh: Right now, I’m still sticking myself and making myself better

in whatever I am doing right now because next semester I

have to graduate with a PhD so there are a couple of tasks

which were told to me in my proposal that I have to present

before I defend for the finals. So, right now I am really getting

down into Tableau. I have already taken the advanced course

of yours in Tableau and I am brushing my skills more in

Tableau. But when you talk about neural networks, I’m really

intrigued with the time series modelling in neural networks,

especially the recurring neural networks. I recently started

learning more and more about the long short term memory

neural networks which are again time series neural networks

and I’ve seen the way it can be implemented to predict the

time series functions. We all are surrounded with time series

functions like stocks, price exchange, even human locations.

The next move is to study more about the advances in the

LSTM. And there is one extra thing before I’m going to take a

break and that is the Capsule network if you’ve heard about

that.

Kirill: Yeah, I’ve heard about that. That’s a next step, yeah?

Neelabh: That just blew mind my mind. That is just amazing. To be

honest, I never thought about it. I sometimes think about the

advances and how can things be changed but this thing

never came to my mind that can we show a three-

dimensional image to a neural network so that it can

understand it better? One of the examples that the creator,


Dr Geoffrey Hinton, one of the examples that he gives is if

you show a coffee mug to a neural network kept on a table

but if you invert the image of the coffee mug, probably the

neural network won’t be able to recognize the coffee mug

because now it has been inverted upside down. I was like,

that’s amazing. Once you start showing the images in three

dimensions, now the entire number of times the dimension

has been increased is making more sense to the network to

understand the function. That is something I really want to

jump in and to understand it.

Kirill: Yeah. Awesome. You know I asked you that question, it was

a tricky question, but I felt there had to be something. A

person as passionate as you, has to have things that they’re

always waiting to learn. I don’t think you’ll ever have, like you

say, a break. But you’ll always have something in your mind

that you’re looking forward to. That’s really cool. RNNs and

Capsule networks. I just learned about Capsule networks,

Hadelin told me about them a few weeks ago, or a week ago

or so. Yeah, it’s just mind-blowing with the 3D images, that’s

something for people to look into. And then once you master

them, which I don’t think is going to be long, you should come

back on the podcast and we can talk about Capsule networks

for an hour.

Neelabh: Sure.

[Laughter]


Kirill: Sure, no problem. Everything under control. I just have…

We’ve got about 10 minutes. Okay, I’ve got couple of

questions. Let’s do some rapid-fire questions, are you ready?

Neelabh: Yeah, absolutely.

Kirill: What’s the biggest challenge you ever had as a data scientist?

Neelabh: The biggest challenge was the data collection for my own

personal research. GPS data.

Kirill: Did you get it off your phone?

Neelabh: Yes.

Kirill: What were you researching?

Neelabh: I wasn’t getting enough data for the predictions, to

experiment on my models. This is a data set which is publicly

available. It’s created by Microsoft Research Asia, it’s known

as GeoLife. So this data set contains about 172 users from

Beijing China. It’s an amazing data set. To be honest, it’s

amazing, it’s mind-blowing. Some people have their historical

locations saved for five years so that’s probably millions of

rows of data. But at the same time, I already used that data

set in my previous research but this time I really wanted

something challenging, something I could touch, something I


could feel. Then I was like, well, I’ve got a smart phone and

I’ve got Google Maps in there. There should be some way for

Apple and Google to collect my data. I looked into Apple and

Apple does not let you have your own data. I’m not sure, but

that’s what I read. And I was like, that’s not cool. Then I

encountered somewhere that Google does it and all you need

to do is go here, go here, go here … It’s a long process but

eventually you get your entire historical data set. And I was

like, cool. Let’s just work on this. But that actually took me

more than a month to collect it, to clean it, to massage it, to

make it perfectly ready. Because time stamps and stuff, you

have to convert it in the format that you want it. The latitudes

and longitudes are like probably up to eight digits, so I didn’t

want that much, I just wanted to six digits. Basically, the ETL

stuff. Then again, I was like just latitude, longitude and date,

time and time stamp is not going to help you. I was like, what

else is really important in a human’s life to make a decision

of his travel. I was like weather is an important role. Just add

weather to that. I had latitude, longitude, and I had date time

stamps. I scraped the entire internet and I added the weather

feature and believe you me, Kirill, I increased my accuracy by

1.6 or 1.7 times, just by adding the weather. That was

amazing and that’s what my professor said. That that looks

pretty cool. Yeah, that was the biggest challenge.

Kirill: That’s so cool. Just to clarify, how long was the … It wasn’t

that for a month you were running around with your phone

collecting the data. You already had the data in your phone,

it just took you a month to get it out and massage it, right?

Neelabh: Right.


Kirill: And how long was the period of the data that you were

working with in the end?

Neelabh: I had data starting February of 2014 until I did this early this

year, so probably January or February this year. I had almost

three years of data.

Kirill: That’s so cool. And so, did you find any surprising insights

like, you know, how often you go to the bar or something like

that?

[Laughter]

Neelabh: That was probably the most visible place, yeah.

Kirill: Gotcha. Okay. All right, next question. What is a recent win

you can share with us? Something really cool that you did in

your role or research or studies.

Neelabh: Recent win. Like I was talking about the time series

modelling, I was able to make a model that was generalized

not just to predict the future locations but also was giving me

so far accurate results when I was trying to predict the future

currency exchange rates. It’s a short story. I was in the US a

couple of months ago and I was like I sometimes have to send

money to India and my parents sometimes send money to me.

There’s an exchange of money from US to India and it all


depends what the exchange rate is. If somehow, I can know

when the rate is going to increase or decrease, I can make

better decisions. I collected this probably past 30 years of

exchange rate data in Indian Rupee and US dollar, and I

made this model and was able to make a model that could

predict whether the price is going to go high or low in

tomorrow’s time. I also experimented with it and it was Friday

evening when I was checking this model and it told me that

the price was going to go high, which is a good thing because

I was sending money from US to India and it actually went

high by probably some couple of cents. It wasn’t a lot but

again, the model did something good which helped me to

make some more money. I also published this article through

Stats and Bots on Medium. It actually went pretty good, I’ve

already gotten almost 1,500 claps and people actually started

noticing my work from there, started connecting to me. As a

matter of fact, I also got an offer as a freelancer for this guy

in Brazil and he wants me to make a similar kind of model

for him. That’s again some extra cash that I can make from

that freelancing job. That is probably my recent win.

Kirill: That’s so cool. That’ such a cool … Like you share a ton of

wins and now all of a sudden, here’s another huge one. And

I love how you took it to the next level, you wrote an article

about it. That’s the way to go, that’s the way to get recognized.

I’m just checking, is it called A Guide For Time Series

Prediction Using Recurrent Neural Networks?

Neelabh: Yeah. There you go.


Kirill: Cool. We’ll share it on the show notes. Guys, check it out. It’s

like very in detail. And also, especially people taking the deep

learning course, definitely check it out, it might be something

that can be very useful.

Neelabh: That’s the article where I’ve explained LST and the Long Short

Term Memory pretty much in detail and shown the examples

how by making use of graphs and using the time series model

as the training said and the testing said. It’s pretty intensive

and in depth. It will be a good read, I can promise you that.

Kirill: Awesome. Okay, and moving on to our, what’s supposed

meant to be a rapid-fire of questions, what is your one most

favourite thing about being a data scientist?

Neelabh: One most favourite thing. Just one? I’ve got so many.

[Laughter]

Okay. Being a detective. That’s what I would have been if I

wouldn’t have been a data scientist. I love being a detective

and with the help of data science, I can investigate data. I can

see something which nobody has seen yet, just by looking at

data as in a database or in an excel sheet. I can actually see

what’s going inside there and I can actually gain some

insightful knowledge just by looking at the data. This is one

of my favourite things, but there are so many other things

and I can talk to you for days and days about it. But, yes,

this is one of the things that I love about it.


Kirill: Awesome. Love that answer and I love how you phrased the

question. Remember at DSGO? Were you there for the panel

discussion? Remember we had that question at the end, what

would you do if you weren’t a data scientist? Remember what

Ben Taylor said?

Neelabh: Ben Taylor said he would catch snakes, something like that.

Kirill: He said he would be a python breeder. Everybody is like,

what just happened? Python breeder?

[Laughter]

And this is like 150 data scientists sitting in the room.

Everybody is like, hold on. Is that Python the programming

language or like an actual python, python? He’s like, no, no.

A python. I would breed pythons. Those things go like

$20,000 on the black market. Like, what is this guy? You’ve

got to give it to Ben Taylor.

Neelabh: I still remember when he answered that, and that was the

same thing I was thinking to myself. And I was like is that

Python, Python, or python snake? That’s how I remember

that he would breed snakes or something.

Kirill: He’s got some crazy ideas. He’s always great fun to talk to.

Neelabh: I saw his podcast, it was probably the video podcast that you

had with him and he’s fasting and sleeping trends. That’s just


crazy. Who does that? I mean, this guy is on another level,

you know. He’s really serious in whatever he’s doing.

Kirill: His life has been crazy. He’s like slept in a tent outside on the

campus in the winter for a whole semester and he does crazy

snow skiing. One time I should get him on the podcast just

to talk about the stories that he’s had, it’s crazy.

Neelabh: I know, yeah. That would be really cool.

Kirill: Anyway, moving on. Almost final question. From all the crazy

things you’ve done in data science and machine learning,

deep learning and so on, where do you think this field is

going, and what should our listeners prepare for to be ready

for the future that’s coming?

Neelabh: Before that, I just want to share something which will lead to

my answer. There are so many crazy things that I’ve done

with machine learning and the internet world, some things I

cannot even remember but two things that I can remember.

One is I used to be a GTA, being a TA for the class. Sometimes

there used to be times when I wouldn’t be able to attend

classes because I was busy on something which was really

important related to my research. But then again, I really had

to be in the class because I had to grade students based on

their presentations.

Kirill: What is a GTA?


Neelabh: It’s a graduate teaching assistant.

Kirill: Ah! I was thinking grand theft auto. The computer …

[Laughter]

I was like, what? Because Hadelin keeps talking about GTA

and we were about to create like a deep learning neural

network which plays GTA. And I’m like, what is he talking

about? Sorry, don’t mind me. Keep going. So, you were a

tutor, right?

Neelabh: Yeah. I was a GTA who used to grade but not play grand theft

auto. I had to be in the class along with my professor so that

we both can grade. There was a time when I wasn’t in the

class and for a student there was no grade from my side but

there were grades from my professor, so we were discussing

that; how can we solve this? Within like five minutes, I had

collected all the data because I had been GTA for this

professor since past two years now. We had two years of data

within like 10 minutes, I made a model that can take my

professor’s value and can predict my values. Whatever

professor graded these students, I also graded these

students. It was more like a linear regression. I gave the

training data as my professor’s data and output as my

scoring. For the testing, I gave the professor’s data on a

specific given day, the student, all the information about the

student and predicted my score. So, I gave this model to my

professor and be like, hey, whenever I’m not in class you can

predict what score I’m going to give.

[Laughter]


Kirill: I love it. That’s so cool.

[Laughter]

Neelabh: Yeah. I loved that one. And another one that I had in my

mind, but my friend implemented this was … In India, you

know how we have arranged marriage system where parents

look for a girl, you know they try…

Kirill: I love where this is going already.

Neelabh: So, this guy was really fed up with the fact that his parents

are like trying to hook him up with girls and stuff. So they

already arranged all these tens of numbers of girls for him

but he already knew these girls from before so he slightly had

an idea. And that idea was converted to a score, 0-10. We

took the entire data set, rather he took the entire data set of

all these girls and trained the model and was looking for a

model to predict the best match for this guy with a girl whom

he hasn’t spoken with yet. Based on her different attributes.

As a matter of fact, he got a score of 85 for this specific girl

and he came back to India, met her, and I think he’s already

engaged with her.

Kirill: Whoa!


Neelabh: Yeah, that was something like mind-blowing. I was convinced

that neural networks and machine learning is the future. It’s

definitely going to be the future. And these examples that I

just gave you are simple machine learning, you don’t even

have to go to neural networks. But think about it. How much

more complex functions you can estimate by using neural

networks? That’s what the future is going towards. There will

be a time. I remember you saying that the amount of data

which is increasing, and which will be in the year 2020 or

2030, it’s going to be humongous, it’s going to be a kind of

data where we need everybody to understand data and the

science behind the data. So just like everybody knows how to

use an iPhone or an iPad, everyone should know what is data

science and how can it help you. I’m sure, in tomorrow’s time

… Like Sophia, the artificial intelligence robot, she’s already

there. United Emirates has already given her citizenship, so

we already have robots like Sophia, so what do you think?

Tomorrow, we’re going to be living with robots. They’re going

to be sleeping with us, who knows? This world is going crazy.

I don’t know. I’m sure this world is going to be an amazing

place and what people are thinking that AI is going to damage

everything, no. it’s going to be there because we created

them. They’re going to be our friends and it’s just going to be

an amazing world to live in. Just be ready, everybody start

reading, start learning maths, start getting more interested

in statistics because that’s going to be the future. That’s

going to be the future.

Kirill: Fantastic, man. Love the answer, love the examples, both of

them. Crazy with the tutoring. If I’m not here, just use this

model, you’ll get my score. And of course, you know, your

friend. Oh, man, I really hope there’s going to be a happy


marriage. You can use that as a case study somewhere. Like

when you write a book one day.

Neelabh: Yeah, maybe. Who knows?

Kirill: All right. Well, let’s start wrapping up. First of all, I want to

thank you so much for coming and sharing all this wisdom

with us and all these examples. Where can our listeners

contact you, follow you, or find you, if they want to find out

where your career goes or what crazy thing you’re going to be

up to next?

Neelabh: One answer. LinkedIn. I’m there on LinkedIn, I want to say

probably 18 hours a day. There, I’m following people, I may

not be posting as much as you would expect because I’m

there for 18 hours, but I’m there mostly to learn from other

people. LinkedIn is a place where I can be found, it’s another

place where I live basically. Besides that, you’ve got my email

from the Medium post. I also have my Google account, if you

just go to Google Scholar, type in my name, you’ll probably

find a couple of my papers which are already published. You

will probably find some more coming up. Google Scholar,

LinkedIn and my email on the Medium.

Kirill: Awesome. And I saw you also share some stuff on GitHub,

maybe people can …

Neelabh: Oh, yeah. Absolutely, yeah. My deep learning repository,

machine learning repository, my spatial data repository, feel


free. Data sets are there, models are there, explanations are

there. Feel free to play with them. Correct me if I have done

something wrong because I am going to learn every day. So

tell me where I’m wrong, we all can make it work together.

Please, I’m always off on new projects as a matter of fact, so

hit me up whenever you think it’s going to be amazing to work

with me. Let me know, we’ll work out something together.

And yeah, I’m always there. I’m always there for the entire

data science community, for this entire data science universe

basically. Hit me up.

Kirill: Awesome. That sounds pretty cool. Actually, really cool. And

I’m guessing probably you’re going to get a few … I think it

will be really cool if people who are listening to this podcast,

who are doing a research paper and need some expertise in

some of the things that you said, they’d actually contact you.

I think like you can definitely provide some really cool

insights or assistance with that.

Neelabh: Sure

Kirill: One final question for you. Do you have a book that you could

recommend to our listeners to help them better themselves

sand become better at data science, or whatever in life?

Neelabh: I have a lot of books in my mind but the one that I would

share is probably … Everybody should have if they don’t have

it, just get it right now, it’s on Amazon. It’s known as Data

Science From Scratch, it’s by Joel Grus, the publisher is

O'Reilly. Amazing book. You’re going to see data science the


way data science essentially is. No packages, nothing, you’ve

got to make everything even back propagation from scratch.

The other one, another favourite of mine, is Machine

Learning in Python, it’s by Sebastian Raschka, again on

Amazon. That book is just amazing. It uses the most

important packages in Python to teach machine learning and

different algorithms inside machine learning. He even

touches a part of neural networks so that you can have a

preliminary, the most basic examples of neural networks and

how they work. The most important, if you don’t have it, get

it right now, Data Science by Kirill Eremenko on Udemy or

SuperDataScience. Trust me on this, that course changed my

life. This is coming directly from my heart. Trust me, that

course is probably the best for data scientists. Get it because

he’s going to tell you, he’s going to cover the most important

four stages of data science. So you better get it. The last one

which is my all-time favourite, it’s Linear Algebra and Its

Application, it’s just a mathematics book, it’s just all about

mathematics. Matrices and linear algebra. It’s by David C.

Lay. Get it. Whenever you are bored, tired, get a paper and a

pen and open that book and start solving all those questions.

Not because it’s going to … It’s going to improve, definitely,

it’s going to make you amazing in linear algebra but at the

same time, I think if you’re practicing mathematics on a daily

basis, it actually improves your logical reasoning and the way

you think. It actually makes you faster in your thought

process. Those are four or five resources that I can provide

you with right now.

Kirill: Thanks, man. Really appreciate it and thanks for the plugin

as well.


Neelabh: Thank you. Thank you, Kirill.

Kirill: I’m just going to recap those. Data Science From Scratch by

Joel Grus, Machine Learning in Python by Sebastian

Raschka, of course the Data Science A-Z course by yours

truly, and Linear Algebra and Its Applications by David C.

Lay. It sounds like you’ve got a whole library just off the top

of your head. That’s a lot of resources. Thanks, man, I think

people will really appreciate.

Neelabh: Sure

Kirill: Okay, thanks again so much for coming on the show and

sharing all these insights and knowledge. It was a fun chat.

This has been like one of the longest podcasts, but I really

don’t want to end it. We’ve had so much laughs here, I really

don’t want to end it but time has come. Thanks, man so

much for coming on this show. This has been amazing.

Neelabh: Thank you so much Kirill. One last thing. You are awesome,

your entire group is awesome. Be awesome, keep people

teaching, making them amazing. You are the one, to be

honest, you told me that I am passionate in whatever I’m

doing. Yes, I am, I try to be, but you actually told me how

much important it is to be passionate. I remember from your

data science A-Z, I remember from Tableau, you said, even if

you’re going out to present, your passion should be reflected

with your smile and your words, and I cannot forget that. You

know you said that so thank you. I have really taken you

seriously and I will request everybody to take you, your


group, your courses seriously, because those are an amazing

asset. I will really say an asset because that’s an amazing

asset in today’s time when it comes to data science. So thank

you. Thank you for making all of us awesome. Thank you so

much.

Kirill: Thanks. Really appreciate it. All right. Well thanks again and

have a good one. Can’t wait to see you at the next

DataScience GO event.

Neelabh: Absolutely, Kirill. Thank you.

Kirill: All right, so there you have it. I’m still so full of energy after

this episode. Thanks, so much guys for checking it out and

sticking through to the end. I really hope you got value. I

really hope that if anything, you got the passion. Like, if you

could feel Neelabh’s passion translate through the speakers

or through your earphones, and it’s crazy. I love episodes like

this. We had Nick Cepeda on the podcast who gave us so

much passion, and now we have Neelabh and we’ve had a

couple more. And it’s just so contagious when you hear

people talk about data science or machine learning or deep

learning like that or just the things that they are able to do

with what they’re studying or what they’re passionate about.

Of course, my… there are so many cool things that happened

on this episode like for example when Neelabh used machine

learning, linear regression to predict what his scores would

be. That was such a good… there should be a meme about

that I feel. It’s such a good example of how to use data science

to optimize your life, to make it more efficient. My personal

favourite takeaways were of course his passion about the


topic of data science, and the crazy applications. The

applications that he even mentioned, that Neelabh even

mentioned at the very start with when you’re crossing a street

and you get a coupon because the company that for instance

if it’s Walmart, knows your normal patterns, your habitual

patterns of location or moving around the city and they can

predict where you’re going to be so when we were talking

about it and there was no example, it was just like geospatial

to predict a person’s location. That was pretty cool. But when

you put into context and you give an example like that, or

with the insurance companies that can save people’s lives, or

car insurance companies they can help people not get into

accidents because of the storms and things like that they

know of or other road conditions, that is really cool. That is

data science in action.

So there you go, that was Neelabh Pant. Make sure to follow

Neelabh on his LinkedIn, you can find the URL to his

LinkedIn. Alongside is the Medium article which he

mentioned which looks fantastic and alongside all of the

other resources that he talked about. You can find all of that

at www.superdatscience.com/115. There you’ll also find the

transcript for this episode and that’s it for today. Make sure

to share this episode around. Any data scientist you know at

any level, share it with them and get them pumped, get them

excited about data science, about machine learning, about

where the world is going. Give to them some of this energy

that you got from this podcast. And I can’t wait to see you

back here next time. Until then, happy analysing.

[Background music plays]

http://www.superdatscience.com/115


Documents

SDS PODCAST EPISODE 115 WITH NEELABH PANT · 2018-08-21 · got so many crazy ideas of how to apply that in business, he actually applies it business and he applies that in real life