Sweet, according to his list I'm over-qualified. Interesting to think it would be so easy to make the transition to data science. Except I can't imagine wanting to work on less important problems than the ones I work on now.<p>Global food security vs. social network analytics. Yeah, fuck the money.<p>edit: calling all data scientists - why not consider becoming a computational biologist? We have hard problems, real outcomes that affect people's lives, and not much money.
Technical skills aside, the best piece of advice in the article is "show them that you want it."<p>I've conducted countless interviews / hires where it basically went: candidates P & Q are the best on paper and in person, but candidate P said x, y, z or did a, b, c, and seems to really want this job and work in our company<p>x, y, z was sometimes as simple as enthusiasm, and other times was in describing what he/she did in their spare time. a, b, c was usually a project for work, school or fun that was highly relevant.<p>Intellectually, I think I know that "enthusiasm" is a poor / weak predictor of success. But, emotionally, it's a go-to tie-breaker.
I'm currently finishing a PhD in economics and have spent a lot of time learning the exact technologies he suggests (Python, SQL, a bit of R). Working as a data scientist would be an awesome opportunity. But are most companies _really_ in need of so many data scientists, or is it just a trend?
"Recursive programming"... as in, programming using recursion? Why would this be important to "data science"? Surely loops are just as effective.
For those looking to make the transition to data science, another option is Zipfian Academy (<a href="http://www.zipfianacademy.com/" rel="nofollow">http://www.zipfianacademy.com/</a>). No PhD required.