Google Statistician uses R and other programming tools

A great interview on the Simply Statistics blog with Google's Nick Chamandy, Phd in Statistics.  Explains that he mainly uses R among other tools to perform his work at Google.  Also of note is the active data science community within Google that uses R as well as some other interesting tools.  Note that they use a lot of data at Google, understandably, and that R usually can not handle the size.  They do a lot of ad hoc reduction of the data with tools like map reduce, Go, and even an R API.  I would love to see how they use the R API to assimilate data.

An interesting insight from the interview is the amount of programming done by the Statisticians.  It seems the culture at Google is to foster autonomy and let the modelers develop their own data manipulation from the raw data.  This requires a broader skillset beyond the statistical analysis tools.

I've found in my work that having knowledge in many tools like R,  CPLEX, and GLPK allows me to be a more effective in my work.  Recently I've been learning a lot of SQL using the PostgreSQL platform.  The tools of SQL combined with statistical tools like R make for a very strong combination.  I'm very agile in my work and can do a varied number of decision analysis.

Popularity of R continues

No doubt those that read my blog know that the tools I use to do my Industrial Engineering and Operations Research work heavily rely on the open source side of software.  That is why I try to support as many open source projects such as COIN-OR, GLPK, and OpenOpt.  One tool that I love to perform Applied Math and Statistics is the statistical computing platform R.  So it comes as no surprise that I like to see how R is growing and its popularity among programmers.

A recent blog from RedMonk produced results of a programming language popularity study.  The study involved ranking popularity using common social media online sites such as Stack Overflow and GitHub.  These sites draw in a lot of programmers for their popularity around Q&A and code review.  I was surprised to see that R ranks highly compared to some very prominant programming languages.

Also interesting to note that the only other "Data Science" type of programming language I could find was Matlab.  As far as I could tell SAS, S, SPSS, Stata are still rather popular but apparently not among the programming community.