Carnegie Mellon statistics professor Cosma Shalizi considers the differences and similarities between statistics and data science.
If people want to call those who do such jobs “data scientists” rather than “statisticians” because it sounds more dignified, or gets them more money, or makes them easier to hire, then more power to them. If they want to avoid the suggestion that you need a statistics degree to do this work, they have a point but it seems a clumsy way to make it. If, however, the name “statistician” is avoided because that connotes not a powerful discipline which transforms profound ideas about learning from experience into practical tools, but rather, a meaningless conglomeration of rituals better conducted with twenty-sided dice, then we as a profession have failed ourselves and, more importantly, the public, and the blame lies with us. Since what we have to offer is really quite wonderful, we should not let that happen.
Some time during the past couple of years, statistics became data science’s older, more boring sibling that always plays by the rules. There are a lot of statisticians who now call themselves data scientists. I still call myself a statistician.
But I think we’re getting closer to that part in the movie when the older, more stuffy character learns from the young whipper snapper that loosening up could be a good thing, and when the young one realizes that some elbow grease and tradition can go a long way.
