Global Certificate in Data Science and Artificial Intelligence (GCDAI) is one of INSAID’s new courses. GCDAI focuses on building Data Leaders who will command the field of artificial intelligence in the years to come. The course spans over 11 months, is divided into 11 terms and is conducted by some of the Top Data Science Mentors in India.
Before we commence with this new course, we sat down with Suchit Majumdar, the Chief Data Scientist and Architect of the GCDAI Curriculum, to address some common concerns of students and understand the vision of the program. An ISB Hyderabad Alumni, Suchit is one of the top 20 Data Science Academicians in India!
This is Part 2 of the Introducing GCDAI with INSAID’s Chief Data Scientist. To read Part 1, click here!
Malvika: What are some things you wish students knew before they got started with the GCDAI course?
Suchit: Before students get into the GCDAI course, I always recommend that understand the profile before you start any course.
Always do your own research. People always want easy answers but even after getting easy answers you have to ask “is it right for you?’’ That is the bigger question that you should ask yourself, a lot of people miss out on asking the right questions.
So what I suggest is whenever you decide to enroll for a new course, whichever course it might be, the question is do you feel attached to it? Do you feel attracted?
Now how do I know if I feel attracted to a particular course. Let’s discuss something like cryptocurrency, the idea underlying here is blockchain.
I’m not very sure about blockchain. I’m not really sure about cryptocurrency either. I figure that it deals a lot with data but I don’t find it very appealing myself. So if somebody asks me why don’t you start learning blockchain, then my response is how can I if I don’t feel attached to it?
How do I know I don’t feel any attachment is because I researched around to see what this technology is all about and how is it solving real world problems.
So the thing is that I need to put in some effort from my side to understand how it will impact my profile and I think I’m more clear that I won’t pursue this right now, maybe sometime down the line.
So on similar lines for anybody who wants to pursue any course, even if it is the GCDAI course, I always recommend that do attend a lot of webinars. There are a lot of webinars that people often conduct. At INSAID, we always conduct a lot of webinars on a regular basis.
That can help you understand what a particular course is all about. Now, after attending the webinar, and understanding what the webinar has explained to you in terms of how these things are changing our life, you would want to go one step deeper and find out some videos on how the subject is solving real life problems.
If you feel attached to it, then you’re in the right spot to take up the course and benefit from it. Otherwise, you’re just pushing yourself to complete a course, which you’re not even sure you should’ve taken up in the first place.
Malvika: As a faculty, how do you ensure everyone in your class has the same level of understanding of your lectures and is open to discussion?
Suchit: To make sure that everybody understands equally well, we never try to orient our way of teaching to theory. So we don’t try to get too theoretical about things. Our idea is to first explain a topic without telling them the jargon of it. So if I’m explaining what linear regression is I won’t start directly with talking about linear regression, but I’ll talk about a problem, I’ll talk about how we can solve the problem, and then explain that the way you solved the previous problem is exactly what linear regression is all about.
That’s a more generalized way of explaining an idea rather than talking too much jargon, too much theory, too much of equations. A lot of people don’t appreciate math because they have been off math for more than a decade.
Students here with 25-30 years of experience can’t be expected to remember all the math they learned in their school days. If you explain the same logic in simple English, I think that’s how people really understand things much better. Once you understand the logic after that applying it is not not a very big deal.
That is how we try to ensure that everybody is on the same platform, irrespective of their background and that has worked very well for us.
Malvika: How do you define relevance when it comes to which case-studies, use cases and projects to include in the GCDAI curriculum?
Suchit: When it when it comes to relevance, I always say that when you’re learning addition in school, you learn by adding two stones.
Back then you didn’t understand the relevance of addition by adding stones. Once you understand the concept, then when you apply at your workplace, you drive relevance. I always use this example, when you use addition to calculate your salary then it becomes relevant for you.
So what might seem to be not relevant initially actually ends up being relevant. We have designed a curriculum in a simple way; we start off with simple ideas and simple examples where you can understand the concepts better first, and then eventually when we proceed, you will end up doing Capstone Projects, where you will look at real world use cases and understand in a much better way; in a much more channelized way that how the concepts that you learned back in your class relate to the real world.
I always encourage people to first understand the concepts instead of focusing on other things such as What kind of data should I learn it on? Or What kind of topic should I learn it on? Or Which domain should I learn it on? because concepts are irrespective of domains; they need to understand the content well enough and then you will be able to apply it wherever you want to.
Malvika: What are some of the biggest misconceptions, myths and pitfalls that students get involved in?
Suchit: Never think anything is hard because it looks hard on the outside, get your hands dirty, try it out and then you can take a call.
The second bit misconception that goes hand in hand is the myth around data science that people would need to be high-end coders to try data science or AI.
Nowadays the world is changing, if you understand the logic behind data science and idea behind AI, the concepts that underlie these two topics, I think eventually that would be a time then people wouldn’t think they need to do all core coding by themselves.
Today maybe you are using a little code but that’s only for helping you see how things work in data science and AI. Over a period, you can be assured that things will change drastically and be more abstract in nature. Anybody and everybody can start applying AI.
So do not hold on to the myth that you have to be a solid coder. You can be a new coder, see how the concepts are applied and how they play out and over a period you’ll see how you can use other things to solve problems in a faster and easier way.
Practicing is something that is very important. If you are taking the classes for granted such that you sit in the class and then you let it go, that would be a big pitfall for anybody. So regular practice of 1-1.5 hours everyday is really important.
Also when you are studying the concepts, apply it. You have assignments to apply and you have projects to apply yourself to and you have Capstone Projects majorly, so you have many ways to apply. After all that you also have one more important area of application and that is your office!
At work you should start applying these concepts. So if you miss out on the small ideas of practicing and applying, I think these are the biggest pitfalls that other people fall into.
Malvika: Thank You Suchit! This has been very insightful, indeed!
This ends our GCDAI Q&A series with Chief Data Scientist Suchit Majumdar. Let us know about your queries in the comments section!