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A few years ago, there really wasn\\u2019t much of a difference between data science in theory and in practice: a jupyter notebook and a couple of imports were all you really needed to do meaningful data science work. Today, as the classroom overlaps less and less with the realities of industry, it\\u2019s becoming more and more important for data scientists to develop the ability to learn independently and go off the beaten path.

Few people have done so as effectively as Sanyam Bhutani, who among other things is an incoming ML engineer at H2O.ai, a top-1% Kaggler, popular blogger and host of the Chai Time Data Science Podcast. Sanyam has a unique perspective on the mismatch between what\\u2019s taught in the classroom and what\\u2019s required in industry: he started doing ML contract work while still in undergrad, and has interviewed some of the world\\u2019s top-ranked Kagglers to better understand where the rubber meets the data science road.