I do computational sociology/social science so here is my $0.02.
Learn R. It is free, well-supported, extensively-documented, and pretty much the industry standard in computational social science research. Just browse through r-bloggers.com to see how widely-used it is. Sure, it has its problems, eats up your memory and takes its sweet time analyzing more complex models, but with cloud programming becoming the standard soon that won't be an issue anymore.
On top of that, I'd say learn Python, Stata, and SAS too. In my experience, different fields have different preferences. Social science people typically use R; public health like SAS. It just depends on what kind of research you want to do.
Try to learn as many statistical packages as you can. You'll find that knowing different languages/programs will help you avoid certain problems and glitches associated with other programs. R is notoriously bad at recoding variables, so I recode in Stata and analyze in R. Also, regardless of your field, having programming skills AND a phd degree will almost guarantee you a data analysis job almost anywhere. So, it's a great way to build a secondary skill-set in case academia isn't your thing.
I'm happy to discuss further anything related to code/computational methods.