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Welcome to Episode 19 of Data Science & AI Weekly!

Have you been wondering how to successfully switch to Data Science jobs?

Listen to the professional journey of one of INSAID’s Top Faculty, Deepesh Wadhwani as Manav, INSAID’s Chief Data Science Mentor, interviews him about his career journey in this Part 1 of Episode 19.


[00:09] Topic of Discussion: How to switch career to Data Science?
[00:42] Series Overview
[01:18] Welcome Deepesh! Senior Faculty at INSAID
[02:38] Deepesh’s Data Science journey
[04:27] Wrap up!
[05:00] Learn more about Data Science at

You can follow the podcast here.

Manav: Are you looking to start your Data Science journey and don’t know where to start?

Today we have with us one of our star faculty, Deepesh Wadhwani with us in Episode 19 of Data Science and AI Weekly.

Welcome, everyone. My name is Manav. I’m the Chief Data Science Mentor at INSAID and what I’m going to do is since we have in this exclusive episode of Data Science and AI Weekly, we have Deepesh with us. What I want to do is ask him questions about how he got started in Data Science, what are some of the tips that he has? And what are some of the things that we expect from our students in terms of learning and succeeding in Data Science so if you have not subscribed to our channel, just subscribe immediately.

You will never miss an update when we release the next podcast and if you have not tuned in to the other episodes of this podcast, the other 18 episodes that we have done, just go to the description you will be able to see the playlist in the description itself. So, with this, let me just welcome Deepesh. Welcome Deepesh to this podcast and very, very glad to have you!

Deepesh: Thank you so much, Manav. I’m very happy to be here!

Manav: So Deepesh, why don’t you, first of all, tell us a little bit about what tracks do you teach at INSAID? And then I would want you to talk about your journey. But first of all, a brief intro about you would certainly help.

Deepesh: So let me start by answering one question, there is only one thing a Data Scientist ever teaches, logic. So there are three basic tracks Machine Learning, one, two, and three, all three of them I meant to take. But the common thread that holds Data Science or Machine Learning or Artificial Intelligence, or whatever name we call the subject from is logic. So we start constructing logic with Machine Learning one we start with basic regression, the basic scatterplot that tells us a lot about what data is and then we eventually.

We move on to the most complicated structure neural networks. So this is the journey that we take from basic to something which is like regression, logistic regression, decision trees to something which is more intermediate support vector machines, time series analysis, and ultimately something which is advanced like neural networks. So, this is the journey that we should expect when we start our journey in Machine Learning.

Manav: Right, so, okay, so now you’re, you’re one of the most loved faculty at INSAID. And that’s what the students are telling me that they always want to sit in your tracks in your classes. Let me ask you, how did you get started in Data Science? Which area did you get started in? And how did you decide what were you doing before that and how did you decide that you want to get into Data Science?

Deepesh: So for me, I started Data Science when I didn’t even know the word Data Science. For me, it was more intuitive. Let me just I’m a mechanical engineer and trust me a mechanical engineer. is a person who will not care if he’s wearing a white shirt. If a vehicle is down, he’ll bend down look what’s under the hood. So, I am that person and I joined Tata Motors back in 2013. During my time there, I was given a single line instruction, I have to save some money on tires. So, like all mechanical engineers will know that one of the most costly part of a vehicle at least in terms of total amount of replacements needed throughout the life of the vehicle is tires. So I was instructed to save cost, and nothing else know how know what no when No, no, no questions answered. So I started analyzing data. I thought I’ll try to figure something out and maybe able to save some money.

And by the end of it, we were able to construct method in which we can do somewhat we can now take a keyword here called preventive maintenance and save 10 Crore rupees during the time of next six years. So, a good project, but that is what introduced me to the concept that data can help us make decisions.

Manav: So this is a start. I, at that point even didn’t know what is Data Science or Machine Learning or there is somebody else who does what I do. So, basically, you were working on data and you built some constructs or analysis, you did some analysis, but it’s just that you did not know that, you know, this is called Data Science. And in fact, during that time, since you’re mentioning 2013, 14, 15, a lot of us had not even heard about the term data. Right? And it’s not like 2019. Now all the companies talk about Machine Learning and AI. It’s every second report that talks about AI all governments talk about AI, back in 2013, at least back in 2009-10.

When we were still studying, we had to go out to get an auto. There was no app, there was no smartphone, the way we know it today. So the amount of data that we have today is going growing exponentially.

Manav: Fantastic. So we will continue quizzing Deepesh more about Data Science, about how to successfully ensure that you become a Data Scientist, etc. In the next episode, which is going to be Episode 20 of Data Science and AI Weekly.

So what we’ll do is we’ll wrap up this episode here and we will see you in the next episode of Data Science and AI Weekly. Thanks for tuning in. And if you have any questions that you want the page to answer or if you want us to answer just leave your comment in the comment section. We will surely make a podcast on that as well. Thank you very much, everyone, for tuning in and this is Manav signing off.


Content Writer @ INSAID. A machine learning buff who loves to read, write and explain everything AI!

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