It’s no secret that there’s been considerable hype building around the field of Data Science ever since the term entered the popular vernacular circa 2012. (According to Wikipedia, it was first used as a synonym for computer science all the way back in 1960 — and in a context more closely resembling its current meaning in 1997.) And with the hype have sprung many different points of view. You’ve no doubt already seen the hyperbolic articles, the high-profile jabs, the clever tweets. Some have mused about the lexical validity of the term, suggesting that calling oneself a data scientist is akin to identifying as a “hammer carpenter”.
Investors aren’t involved enough in the space to know that the first Reinforcement Learning textbook was written in 1998, or that what separates a successful applied machine learning company is often the novelty, quality and/or quantity of the data they have access to. Clever teams are exploiting the obscurity and caché to raise more money, knowing that investors and the press have little understanding of how machine learning works in practice.