Global Certificate in Data Science (GCD) is one of INSAID’s flagship courses. GCD aims at creating Data Leaders from aspiring data scientists. The course spans over 6 months, is divided into 6 terms and is conducted by some of the Top Data Science Mentors in India.
Today, we’re in conversation with one such brilliant data science mentors, Suchit Majumdar, the Chief Data Scientist and Architect of the GCD Curriculum! An ISB Hyderabad Alumni, Suchit is one of the top 20 Data Science Academicians in India!
Malvika: What do you have to say about the current landscape of data science in India and the level of awareness about data science among the workforce?
Suchit: I am very optimistic about the current landscape of data science in India. A lot of opportunities have opened up in the field, a lot of research is happening and a lot of colleges have started teaching data science during regular college terms themselves.
There are certain challenges that I would like to point out; the level of awareness about data science in the workplace is not that ideal. Currently, a lot of people have not gotten the right amount of exposure because a lot of companies are taking a long time to get into the data science space.
Companies are apprehensive as to whether data science is the right choice for them or not. This has led to a slight problem that companies are not taking data science seriously. If people are not taking data science seriously, companies are not taking it seriously.
So over a period this has to be broken and certainly data science is only going to grow in the future. In India, it is taking an awkward direction but the pace has to be much faster. That’s only possible when the people and the level of awareness also increases.
Malvika: Great! Let’s now discuss the courses at INSAID. Can you discuss some banner highlights of our GCD program?
Suchit: If we look at GCD the course, it firstly helps us get into a spot where we’re adept in understanding the data, using Python and statistics, which actually forms the basis of good understanding of data. So if you really want a personal shot at making sense out of data, you will need Python and statistics by your side.
Beyond this, you might also want to understand how to use data to solve business problems, and eventually, a lot of this will be automated using machine learning. When you’re working on machine learning, you’d really want to have a fair exposure to different kinds of algorithms. GCD was curated with that idea in mind.
We have all the important machine learning algorithms that everyone needs to know to start solving problems for the company. Once you start solving problems for the companies, they’ll be more interested in data science. Once you get interested in data science, you start digging deeper, and that’s also when companies start accepting data science.
Malvika: What were your objectives behind designing the GCD curriculum?
Suchit: The idea was getting people to a place where they can start solving business problems. They can actually come up with ideas that can help them optimize and better whatever solutions they’re providing to the clients.
The end objective was to ensure that people knew how to match the data science knowledge to their actual business outcomes. We wanted people to be able to use the knowledge they’re gathering to solve bigger business problems; to figure out more optimized ways of solving problems.
Eventually, in the process of solving these problems, you can actually dig deeper and come up with better solutions for your company. In turn, your company will start investing more into data science.
Malvika: How has the GCD curriculum evolved in the past one year?
Suchit: It has evolved a lot because we started off with the idea that we need to get market ready pretty soon. Over time, we realized that the challenge with IT and a lot of industries is the lack of involvement of actual coding so a lot of people are not really geared up for coding right away.
Now it was like a mental block for a lot of people and they feel that it’s a make or break criteria for learning data science. That’s why we had to re-design the entire curriculum and over a period of time, we landed to a place where everybody, irrespective of whether they know programming or not can work towards it.
They can get started with coding and data science and can implement it just like any other person would have done. So I don’t see why someone would feel hampered due to their lack of coding knowledge, because recently I’ve re-curated the entire course so that people can start learning and start applying themselves.
Malvika: Why does our curriculum prefer Python over R?
Suchit: There’s always a debate around Python or R but I feel that both are equally very valuable languages. If I were to take a call on why Python and not R, it would be because R is more academically oriented and more research-oriented.
So companies that are working heavily on statistical information which are very limited in number; so people and companies that work only on research they will be needing R.
If you see big corporations like Google, YouTube, Dropbox and all these big companies, all the solutions they are building are built on Python. Python is very easy to use and learn.
The second part is that it works well with building products. So if you want to build products with Python that can be accomplished in no time. So the output becomes much faster with Python as opposed to R.
All in all, if you want to create products, Python is the way to go and if you want to do research R should be your preferred tool. That said since most of the companies are into building and running products, so Python is with the choice in INSAID’s curriculum.
Malvika: Which type of use cases and case studies are preferred in the GCD curriculum?
Suchit: Usually this is again a point of contention for a lot of people where they feel that should I learn data science from my domain’s perspective?
To make things a little easier, what we always tell people is, when you learned addition in your school days, nobody taught Addition through a calculation of salaries. Nobody limited you to the perspective that you use and learn addition only when you calculate salaries. You were not taught about basic pay and allowances and HRA as a kid, nobody does that!
So the trick is that you learn the concept in a simple way and then as you grow, you start learning multiple different domains, you start applying in different ways. On similar lines, our project data has been created in an order such that irrespective of your background you can always make sense of the data.
The data points are very easy to understand and we don’t want to only focus on specific concepts or domains.
So our target is to create data sets that make sense to a lot of people. Something like IPL. A lot of people watch cricket matches, a lot of people watch football matches, a lot of people own cars, so they’d be more interested in related data-sets.
So these are the data-sets that help other people put things in context, understand the concept once the concepts are solid, I don’t think there will be any major problem of applying them to any number of industries.
Malvika: As part of the data science faculty at INSAID, how do you ensure the classes are engaging for the students?
Suchit: To ensure that the classes are engaging for the students, first things first, we are always on the lookout for answers from the students instead of questions.
We always try to ask questions to students in the class to gauge whether students can answer those queries. Now this is one way to interact because if students understand a particular topic, they’ll always be excited to answer. If they’re not able to answer them, the instructor understands that he needs to change his style of delivery.
These are some little tricks we do during the classes where we follow up while teaching a particular topic, we ensure they’re understanding the whys of the topic, and making students think about this topic. That’s how you gain the maximum value out of classes. So this is one of the ways that we try to ensure that all the classes are very interactive in nature.
Also, at the end of the sessions, we enable the microphones of the students so they can speak up about their own doubts and queries. We also play small games where we ask people to think about data science applications that they can think of.
For example, many of us use Uber daily. We ask our students to come up with ideas around how Uber might use data science to their advantage. Through these small games towards the end of the session, we want students to think out of the box and come up with ideas. So we have constant interaction so that the student is just not glued to the instructor during the entire learning experience.
This end Part 1 of Exploring GCD with INSAID’s Chief Data Scientist! with our Data Science maverick, Suchit Majumdar. Watch out for our next part to the series. If you have any questions regarding the program, feel free to write to us at [email protected]!
1 comment