By Thilo Stadelmann (ZHAW)
Reposted from https://dublin.zhaw.ch/~stdm/?p=350#more-350
I recently came about the notion of “type A” and “type B” data scientists. While the “type A” is basically a trained statistician that has broadened his field towards modern use cases (“data science for people”), the same is true for “type B” (B for “build”, “data science for software”) that has his roots in programming and contributes stronger to code and systems in the backend.
Frankly, I haven’t come about a practically more useless distinction since the inception of the term “data science”. Data science is the name for a new discipline that is in itself interdisciplinary [see e.g. here – but beware of German text]. The whole point of interdisciplinarity, and by extension of data science, is for proponent to think outside the box of his or her original discipline (which might be be statistics, computer science, physics, economics or something completely different), and acquire skills in the neighboring disciplines in order to tackle problems outside of intellectual silos. Encouraging practitioners to stay in their silos, as this A/B typology suggests, is counterproductive at best, fatal at worst. Continue reading