This past November, Avi Patchava and Paul Meinshausen participated in panel discussions at the Data Hack Summit in Bangalore and were encouraged to see Kunal Jain's keynote on developing the data science ecosystem in India. They have been a part of the community for several years and have worked in data science roles across start-ups, management consulting, industry, and venture capital. They share the strong conviction that an important area for development in the ecosystem is in Data Science's intellectual infrastructure. In the early stages of development, data science is often mistaken for a thin layer of popular statistical tools and packaged algorithms that are applied bluntly to a problem of choice. As data science evolves as a discipline in India, it will take more robust shape as an intellectual approach to discovering, as well as building solutions, for tough problems across business and society.

Before it makes sense to answer this question, one needs to think a bit about the relationship between data science and machine learning. To me personally, data science includes machine learning. Machine learning by definition is the ability of a machine to generalize knowledge from data - call it learning or induction if you like. Without data, there is little machines can learn. So if anything, the increase in machine learning usage more broadly in many different industries will be a catalyst to push data science to increasing relevance.