I met up with an old stats grad school friend the other day. When last we got together a few years ago, he went on a rant about “data science”, suggesting the term's nothing more than a pretentious new moniker for the same statistical work he's been doing for 35 years. I disagreed, noting a substantial evolution from our early statistics days in the breadth of problems, especially involving computation, we address today. I guess his thinking about the statistics-data science divide was akin to FiveThirtyEight's Nate Silver; mine was more like statistician Andrew Gelman.
I was a bit surprised to note my friend had mellowed little in his statistical thinking. He did acknowledge that predictive modeling from traditional statistics serves a different purpose than the machine learning prominent in business today – and, more importantly, that both types must now be part of the modeler's arsenal.
