Computing in the Statistics Curricula

The diagram below has been kicking around in twitter lately.  Some think its like the 3Vs of big data description, but it is more about how computing is being used in statistics curricula. From a lengthy detailed article: Computing in the Statistics Curricula by Deborah Nolan and Duncan Temple Lang.  in The American Statistician, May 2010, Vol. 64, No. 2

I like the fact that it covers a number of areas of importance in analytic computing.   But many of these are not new, some have been renamed for marketing purposes, and it is not complete.  You can argue a number of the overlapping aspects of the Venn diagram form.   Yet it is a reminder of how complex this world has become and the technologies that exist.  Few data scientists could fully explain what each of these is in useful detail.

I would like to see further a prioritization of which of these techniques should be included in a statistics curriculum.  You won't be able to cover them all.  Also, how should these be prioritized by industry area?  Also, no mention of 'cognitive systems', AI or Neural nets.   All hot topics today that should be included.  This may be in part from the publication date of the article.

 pic.twitter.com/CpubPMS5Ax