In a previous post The Data Scientist, I talked about the term and where it fits into the current paradigm. The topic arose again this week in a post from Kaiser Fung, with an amusing twist — rebranding.
“You have to give it to the computer scientists. They are like the branding agencies of the engineering world. Everything they touch turns to PR gold. Steve Jobs, of course, was their standard bearer, with his infamous “reality distortion field”. Now, they are invading the statistician’s turf, and have already re-branded us as “data scientists”. MIT Technology Review noted this event recently”
I am amused, as Kaiser is, on how rebranding can hype ideas/terms/jobs/technology… Here is my take:
I agree with the MIT article that it is not so much that ‘data scientists’ do anything differently than statisticians, in terms of their techniques.
However, there is a clear gap from the ‘stats folks’ to the ‘business folks’. One group speaks math, the other speaks English. This is the void which I think needs to be filled. My own personal (and COMPLETELY biased) mental model of ‘data scientist’ is the cross between statistics, data management, and system engineering. The system engineering (a systemic viewpoint of SE, not a systematic viewpoint of SE) is the key to bridge the void.
This relates to the Susan Holmes statement (that >80% of a statisticians time is spent preparing the data ). I would contend that it _should_ be more like 40% prepping and applying stats, and 60% describing/conveying what it means.