jtleek

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...

If you find this interesting you might also like:

http://biostatistics.oxfordjournals.org/content/early/2013/0...

the paper was discussed by several leading statisticians including Andrew Gelman, D.R. Cox and Yoav Benjamini:

http://simplystatistics.org/2013/09/25/is-most-science-false...

This article immediately made me think of this: http://www.tylervigen.com/

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:

http://jhudatascience.org/

If you liked my guide to sharing data, you may also like my guides:

For writing R packages: https://github.com/jtleek/rpackages

For writing scientific reviews: https://github.com/jtleek/reviews

Or on the future of stats: https://github.com/jtleek/futureofstats

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.

Title of the post: "Why the current over-pessimism about science is the perfect confirmation bias vehicle and we should proceed rationally"