The problem with not having a software and "grungy coding skills" background is that you aren't able to efficiently reproduce/check/verify claims being made which are often wrong or misleading. A recent example I had of this is someone studying how an algorithm behaved on a subset of interest of a particular population, and they didn't even bother to check that the subset of their data was statistically large enough to draw any conclusions (only 17 records out of thousands). Needless to say this example also failed on the reproducibility and reuse fronts.
The problem with both in academia (even at polytechnic non research schools) is that they don't know enough about each other.
Also, I'm glad the author added the twitter endorsement to the article --I've been considering creating a twitter account, and the fact that the author found it so useful helps with my decision to devote time to creating an account.
Computer scientists mostly don't know about sampling and make easy problems hard. Statisticians mostly fail at simply getting the data in a format they need.
I mean, surely, you need a good background in mathematics to model and extract meaningful information from the data, so I don't see how it is crazy to do that with a PhD in mathematics ...
Back then it was virtulay all Pure maths with the computing side being shall we say Antique - most of my course mates where working at Tier 1 RnD workng at the bleeding edge, my employer hired the first non academic knowledge engineer in the UK
They have a lot more experience with real-world data and a lot more knowledge about what statistical techniques and assumptions correctly apply in real situations - quantitatively and qualitatively. You simply don't get this knowledge and experience from a maths-led statistics course.
I would be happy to recommend you a history major.
One of the best in my class was a philosophy major.
I've completed the very first his course in December 2011.