We thought it’s a good time to see how this domain has evolved over the past few years – from the point of view of an early player in India’s Analytics training landscape. Praxis Business School was one of the pioneer in recognizing the need to create a pipeline of trained resources for this science.
We spoke to Charanpreet Singh, Co-founder and Director, Praxis Business School Foundation, and Dr. Prithwis Mukerjee, Director, Business Analytics Program, Praxis Business School to examine how different does the analytics landscape looks today compared to 2011, when Praxis started its full-time program in this field.
Analytics Vidhya (AV): What triggered your starting a full-time post graduate program in business analytics in November 2011, and what was the response to this move? How has the program progressed in the last 6 years?
Charanpreet: We were in Bangalore, meeting companies for recruitment for our business management students. In two of our meetings, we were told by two completely unconnected people to start a course in analytics – they believed that there would be a great demand for trained professionals and Praxis had the capability to deliver a program in this area. We did our own research and decided to go ahead with the idea – as it would help us differentiate ourselves among the several thousand B-schools!
The response we got was overwhelmingly meager – we started with a batch of 8 students. As the awareness of this domain increased, not the least due to the efforts of communities like yours, and as successive batches saw real jobs at the end of the program, the demand picked up. Today we have 2 intakes a year – January and July batches running out of Kolkata and Bangalore – and also run the analytics program at the International Institute of Digital Technologies.
AV: How has the analytics landscape changed over the last 6 years from a Praxis point of view?
Prithwis: There are changes across all stakeholders: the most obvious change of course is the number of people seeking to skill themselves in analytics. There are two important reasons for this:
- The drive toward automation that will make several hundred thousands of IT professionals redundant unless they re-skill themselves in new areas;
- The galloping demand for analytics professionals in the industry.
On the program front – technology has the nasty habit of changing just when you thought that you have mastered it. This is no surprise to anyone who has been at the cutting edge – as is the case for many of us at Praxis. But instead of being resigned to this ‘bleeding” edge we thrive on it by keeping the Praxis curriculum in “perpetual beta”.
When we began in 2011, SAS was central to our curriculum and this was supplemented with domain knowledge from partners like ICICI Bank. Within a year or two we realised that the world was moving into the open source tools like R for analysis and Hadoop for managing “Big Data”, and altered our curriculum accordingly. In another two years with the advent of Spark and TensorFlow, Python has become the dominant platform because of its native affinity for these tools and so our curriculum was modified again – in fact in the middle of the 2016-17 session!
There has been a continual change in the way the subjects are taught as well – there are more used cases available, more hackathons and problems in the public domain that we can get our students to work on. So, both from content and pedagogy perspectives, the analytics program at Praxis has undergone huge changes to remain contemporary and effective.
AV: What about the demand side of things – the industry?
Charanpreet: Three trends are clearly visible:
- Organizations across industry sectors are becoming serious about building effective analytics teams leading to an enhanced demand for data scientists;
- Companies are more willing to hire from specialized analytics training programs like ours – in the past HR struggled to ‘slot’ our program in their hiring template – as the one-year analytics program was neither an M.Tech nor an executive MBA – but today the industry recognizes the relevance of a program like this. We are therefore able to run very successful campus placement programs for both our January and July batches across both campuses;
- There is an enhancement in both the level of expertise expected and the diversity of roles offered. Interestingly, while technology is evolving with speed, and sometimes the industry lags in adoption. As talent builders and suppliers, Praxis has to work continually to meet expectations across a wide array of recruiters.
At present, our placements are witnessing the dual advantage of two factors – repeat purchase by ‘loyal’ companies and a significant number of new companies joining the fray. I am sure this must be the case industry-wide.
KJ: Now that we have a hold on the journey so far, what do you think lies ahead?
Prithwis: There is no doubt that the future belongs to artificial intelligence – of the kind demonstrated in self driving cars and automatic face and voice recognition. All these are extensions of what the data scientist refers to as data mining or machine learning and there are two aspects to this change. Obviously, subjects like artificial neural networks, deep learning and cognitive learning will become increasingly important and we are introducing them into our curriculum.
The other subtle change is that this same AI will be used to make data science simpler and easier to use. Managers will be able to use GUI driven tools to carry out data science tasks without having to learn about the algorithms and data-structures that support machine learning models. The challenge lies in being able to harmonise these two widely different scenarios in a manner that caters to the aspirations and expectations of all our future students.
KJ: Finally, what would you say to an aspiring data scientist?
Charanpreet: An aspiring data scientist has already made the right decision – so we encourage him whole-heartedly to join this exciting world. As the subject is sufficiently complex, my advice would be to invest adequate time in a process that strengthens your knowledge and skill levels and gives you a sound conceptual base from which you can chart your growth. Too many aspirants think they can juggle several things in life along with learning – our experience has been that even as a full-time pursuit fathoming this science is a challenge.
My appeal to everyone who believes he/ she is a good problem solver, analytical and number friendly – analytics and data science is pretty much the best option you have for an interesting, remunerative and sustainable career – because data generation is poised to grow at unprecedented rates, and the requirement for people who can make sense out of this data is similarly poised for an explosion! With the fear of repeatable tasks in the IT workplace getting automated, you would likely be taking a big risk if you do not re-skill yourself.
Thanks Charanpreet & Prithwis for spending your valuable time with us and sharing these thought with us. We wish you all the best for coming batches and hope to see you at the DataHack Summit 2017.