I don’t believe I’m mischaracterizing statistics. My original point was an observation that, in my experience, good mathematicians and good statisticians are different. Their brains work differently. To use an imperfect analogy, good C programmers and good Lisp programmers are also quite different. You just need to think in a very different manner in Lisp compared to C (and vice versa). That, of course, doesn’t mean that a C programmer can’t be passably good in Lisp.
I understand that in the academia statistics departments usually focus on theoretical statistics. That’s fine—I don’t in particular care about “official” discipline boundaries. For my purposes I would like to draw a divide between theoretical statistics and, let’s call it practical statistics. I find it useful to classify theoretical statistics as applied math, and practical statistics as something different from that.
Data science is somewhat different from traditional statistics, but I’m not sure its distinction lies on the theoretical-practical divide. As a crude approximation, I’d say that traditional statistics is mostly concerned with extracting precise and “provable” information out of small data sets, and data science tends to drown in data and so loves non-parametric models and ML in particular.
I don’t believe I’m mischaracterizing statistics. My original point was an observation that, in my experience, good mathematicians and good statisticians are different. Their brains work differently. To use an imperfect analogy, good C programmers and good Lisp programmers are also quite different. You just need to think in a very different manner in Lisp compared to C (and vice versa). That, of course, doesn’t mean that a C programmer can’t be passably good in Lisp.
I understand that in the academia statistics departments usually focus on theoretical statistics. That’s fine—I don’t in particular care about “official” discipline boundaries. For my purposes I would like to draw a divide between theoretical statistics and, let’s call it practical statistics. I find it useful to classify theoretical statistics as applied math, and practical statistics as something different from that.
Data science is somewhat different from traditional statistics, but I’m not sure its distinction lies on the theoretical-practical divide. As a crude approximation, I’d say that traditional statistics is mostly concerned with extracting precise and “provable” information out of small data sets, and data science tends to drown in data and so loves non-parametric models and ML in particular.
Ok, I am not interested in wasting more time on this, all I am saying is:
This is misleading. Theoretical statistics is not applied math, either. I think you don’t know what you are talking about, re: this subject.
So we disagree :-)