Building a better India with data science and machine learning

In the digital age we all live in, most of us instinctively understand the value of data because it is everywhere. We are drowning in data generated by businesses in high-streets, Wall Street, hospitals, academia and automobiles, power plants and many more. However, several smart organisations run by some very smart people are using Data Science (DS) and Machine Learning (ML) not just as lifeboats to stay afloat, but as a powerful engine for growth and change.

The trillion-dollar club comprising Apple, Microsoft, Google’s owner Alphabet, Amazon and Tesla that have acombined market capitalisationvalued at nearly half of the United States’ economy (GDP), have one thing in common. They are all driven by data. In other words, they have data scientists and ML experts at the wheels, making them some of the most valued and sought after professionals, not just in the field of technology but in other spheres of life as well.

DS and ML may have entered the common lexicon in the last decade or so, but their genesis goes back several decades. A pantheon of great mathematicians, statisticians, economists, and technologists from around the world have contributed to the well-deserved and elevated status of DS and ML today. DS combines our understanding of mathematics, statistics and programming skills to gain insights into ordinary transactional data. ML as a subfield of artificial intelligence, helps machines learn (without being ‘taught’ through programming) from vast volumes of historical transactional data and insights delivered by DS. When we combine these two, what we get is an advanced system that starts to resemble human intelligence. When a video streaming service recommends movies, we are likely to watch, it is doing what a neighbourhood video store owner used to do a couple of decades ago. Every time we use such services, we as consumers are actually making the system behind the services a little more ‘intelligent’ and come up with more helpful recommendations. This is the same principle that is driving the success of online grocery stores, cab services, banking, etc.

Today, DS and ML are doing much more than helping us with these mundane decisions. For example, satellite data that provides advanced knowledge of rains and constantly changing soil quality are helping farmers improve their productivity like never before. Governments around the world are able to design more effective education policies based on a deeper understanding of the country’s demography. Similarly, healthcare services delivered on the basis of advanced knowledge of seasonal diseases are saving lives by the millions. The application of DS and ML is making road signals more adaptive, thus, not only making mobility safer for citizens, but also helping reduce vehicular pollution caused by unnecessary idling due to traffic jams.

In a country like India, which has one of the most complex socio-economic structures in the world, DS and ML can bring about transformational changes that were not possible over the last several decades. In fact, this is already happening in many critical areas like banking, healthcare, education, etc. In the last seven years, more than44 million Indianshave benefited from the government’s universal banking scheme launched on the strength of data generated by Aadhaar. Two out of every three of these live in rural parts of the country. The DS-powered ‘Sutra model’ developed by a handful of professors in IIT-Kanpur is helping us stay ahead of the Covid-19 curve and be better prepared for potential new waves. Vaccination drives and other healthcare delivery initiatives based on such advanced knowledge is saving lives by the millions.

So where do we go from here? The recent surge in digitalisation of everything is delivering massive volumes of data to us. What we make of them and how we act on them will depend on the quality of DS and ML experts we have. Today, the demand for these skills far outstrips the supply, both in terms of quality and quantity.

On the brighter side, we have a fast-evolving and well-funded edtech space in India that in many ways runs parallel to traditional academic institutions. A handful of these are super-specialising in DS and ML. This number must grow first. The pedagogy, curriculum, and quality of trainers, ideally with industry experience, must also evolve simultaneously. Today, industry bodies while doing a commendable job in creating a massive skill pool in the IT sector must also sharpen their focus towards creating an army of DS and ML specialists and this must be done on a mission mode. One recent study has projected the demand for data scientists in India to grow to11 million by 2026. That looks like a target we can chase and achieve over the next four years. The downside of failing to do this will be inexcusable.

The author isHead of Product & Strategy at InterviewBit & Scaler

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