As an academic statistician running the largest data science program in the world (10,000+ have completed a class) I think that CS/Data Science only pose a threat to statistics if we don't adapt. I wrote about it here. http://simplystatistics.org/2013/04/15/data-science-only-pos...
Jeff here - one of the creators of the classes. The great thing we like about this is that you can take the classes for free if you want. You only have to pay if you want the specialization. More info here:
Jeff, I'm signed up for the entire set at the moment. It's just a matter of deciding if I want to pay for it and focus on it or take other classes (MIT is doing a data course as well this spring over with edx). If I can get my employer to pay that'd be better, not because it's expensive but because I perceive that it would be better material to add to my resume if I can say it was work sponsored.
I'd like to know your thoughts on the value of the final capstone project respective to career development?
I can vouch for the value of the material. I took yours last year and it definitely helped with both work as well as my personal projects. In particular the components on functional knowledge are things that other courses tend to ignore.
As the author of the guide in question, I feel I should speak up here. Neither frequentist or Bayesian statistics is "just right". It is 100% dependent on the user and whether they use/interpret the quantities correctly. There are both really good and horrible Frequentist and Bayesian statisticians. To imply one or the other is "better" is incorrect and disingenuous. Just my 2 cents.
> There are both really good and horrible Frequentist and Bayesian statisticians.
Yeah. If I had to choose between Fisher and Anonymous Bayesian, I may chose Fisher.
However, unless both kind of statistics yield the same results (I don't think they do), then at least one of them is bogus, by application of the non-contradiction principle. So, while I can imagine there are good Frequentists Statisticians out there, I insist that frequentism itself is bogus.
