As data science, analytics and related technologies are evolving at a fast pace, it calls for a constant upgrade in the technical skillset to be relevant to the industry. This is where analytics education has found prominence. Our theme this month is around data science MOOCs, learning resources and analytics education scenario in India. We will try to cover different aspects of data science education in India, its challenges, how efficient they are and the learning material, among other aspects.
Our first interaction in the series is with Rahul Dé who is an alumnus of IIT Delhi and currently serves as the faculty of decision sciences and information systems at IIM Bangalore. Since the 1990, he has taught Information Systems and Management Science courses in various universities in the US, India, Spain, France, Sweden and Norway. Before joining IIM-B, Professor Dé was an Associate Professor at Rider University in New Jersey. With a keen research interest in open source and e-government systems, he has two books and over 50 articles published in international journals.
AIM: Do you think there is a dearth of talent owing to the fact that there are fewer courses catering to new age technologies such as analytics and AI in India? What can be the steps taken to overcome this?
Rahul De: There is certainly a dearth of talent. Given that in general engineering studies itself the talent is short (“unemployability” of graduating engineers is very high), the talent pool available for cutting-edge disciplines like AI or analytics is very low.
The way to overcome this is through wide-scale awareness building in colleges and universities, and training of teachers and professors on these topics. Nowadays, software and even hardware are not a bottleneck. Almost the entire set of tools for AI or analytics is available through open source software and hardware is cheaply available through cloud models. What is lacking is the talent to teach these tools.
AIM: In your opinion, can popular MOOCs from EduTech startups fulfil the current learning needs?
RD: MOOCs help with highly motivated students who are able to follow the entire course online, do all the assignments, and take up challenges on their own without oversight from teachers, tutors and guides. They have not proved to be useful in the college and undergraduate education context. A blended approach of in-class and MOOCs is better.
AIM: What are the various courses offered by IIMB to fill up analytics talent shortage in the industry?
RD: IIM-B runs internal courses on Analytics and AI for all its programs. Plus IIMB runs very successful executive education programs in Analytics. IIMB also has a faculty development program in Analytics. IIMB has several MOOCs on Analytics, Quantitative Analysis and on IT Management that serves the purpose. Lakhs of students from around the world have taken these MOOCs.
AIM: Do the learning materials available today cover the full breadth of learning for analytics and data science aspirants?
RD: The learning materials available today cover the breadth for analytics and data science. There is a lot that is freely available. However, depth is another matter. There are few available materials that go into depth on certain topics that are challenging and difficult to master.
AIM: What are the challenges that analytics education space faces?
RD: Lack of teachers, lack of adequate textbooks and learning materials, widespread lack of awareness about these topics. Some commercial offerings are of poor quality and are not able to teach the cutting edge knowledge required by industry.
AIM: How has the analytics and data science education evolved in India over the past few years?
RD: Many universities and management schools have started offering courses and degrees in analytics and data science. Some are of very high quality and are training students in cutting-edge techniques.
AIM: Do Masters or PhD’s in the areas such as analytics and data science have more weight than self-trained data scientists when it comes to fitting into industry requirements?
RD: Certainly, Masters or PhDs who are rigorously trained have a better grasp of these subjects. Self-trained data scientists will be able to use some tools, but will not be able to grasp the fundamental concepts. For instance, modern data science requires a deep grounding in mathematics and computing, and particularly in statistics and mathematical programming. This requires rigorous coursework for students to understand the concepts well. Self-learning on MOOCs and other resources can go only so far in grounding the concepts, not deeper.