A couple of cool things happened at this years JSM.
- Twitter adoption went way up and it was much easier for people (like me) who weren't there to keep track of all the action by monitoring the #JSM2013 hashtag.
- Nate Silver gave the keynote and about a million statisticians showed up.
Nate Silver is hands down the rockstar of our field. I mean, no other statistician changing jobs would make the news at the Times, at ESPN, and on pretty much every other major news source.
Silver's talk at JSM focused on 11 principles of statistical journalism, which are covered really nicely here by Joseph Rickert from Revolution. After his talk, he answered questions Tweeted from the audience. He brought the house down (I'm sure in person, but definitely on Twitter) with his response to a question about data scientists versus statisticians with the perfectly weighted response for the audience:
Data scientist is just a sexed up word for statistician
Of course statisticians love to hear this but data scientists didn't necessarily agree.
Not at #JSM2013, but intersect of self-ID’ed statisticians w/ self-ID’ed data scis is ~ null. Not sure who’s losing in the “sexed up” dept.
— Drew Conway (@drewconway) August 5, 2013
@hspter not sure that describes what I do.
— josh attenberg (@jattenberg) August 6, 2013
— Hilary Mason (@hmason) August 6, 2013
I've talked about the statistician/data scientist divide before and how I think that we need better marketing as statisticians. I think it is telling that some of the very accomplished, very successful people tweeting about Nate's quote are uncomfortable being labeled statistician. The reason, I think, is that statisticians have a reputation for focusing primarily on theory and not being willing to do the schlep.
I do think there is some cachet to having the "hot job title" but eventually solving real problems matters more. Which leads me to my favorite part of Nate's quote, the part that isn't getting nearly as much play as it should:
Just do good work and call yourself whatever you want.
I think that as statisticians we should embrace a "big tent" approach to labeling. But rather than making it competitive by saying data scientists aren't that great they are just "sexed up" statisticians, we should make it inclusive, "data scientists are statisticians because being a statistician is awesome and anyone who does cool things with data is a statistician". People who build websites, or design graphics, or make reproducible documents, or build pipelines, or hack low-level data are all statisticians and we should respect them all for their unique skills.