A feature all programming communities have in common is the numerous debates about why their programming language of choice is better, more advanced, faster, holier etc. In today’s data science community, it seems like these discussions are omnipresent with advocates of SAS, SPSS, R, Python, Julia, etc. battling and challenging each other on every online medium. (side note: These ‘data driven’ debates are often a good example of how you can prove anything with statistics.)
While these debates are a good thing for the community and the programming language as a whole, they unfortunately also have a negative effect on those individuals just in the beginning of their data analytics career. Biased opinions on all sides of the table, make it difficult for new data analysts to see the forest for the trees.
Especially for this new group of data analysts (and future debaters), as well as for everyone else interested in learning data science or an additional statistical language, we created the infograph ‘Statistical Language Wars’ that gives a basic comparison between SAS, R and SPSS to see how they stack up. This to provide a more clear starting point.