PS: This is continuation of Part I.
At heart, a good data scientist is a passionate coder-slash-statistician –and there’s no better programming language for a statistician to learn than R. THE standard among statistical programming languages, R is sometimes called the ‘golden child’ of data science. It’s a popular skill among big data analysts, and data scientists skilled in R are lapped up by some of the biggest names in business, including Google, Facebook, Bank of America, and the New York Times.
R is freely available – Unlike SAS or Matlab, you can freely “install, use, update, clone, modify, redistribute, and even resell” R.
So not only is it a major cost-saver on projects, but it also allows for easy and constant upgradation of versions, which are useful features for a statistical programming language.
R is cross-platform compatible – R can be run on Windows, Mac OS X, or Linux (go here to learn more).
R is a heavy-duty language – As a powerful scripting language, R will help you handle large, complex data sets. It is a great programming language to compute Big Data. R is also the best language to use for heavy, resource intensive simulations. Furthermore, R can be used on high performance computer clusters which manage the processing capacity of huge numbers of processors.
Please click here to read Part III.