The two most common programs used for empirical data analysis are Stata and SAS. If you do theoretical work, you might also use MatLab or you might just use a soap pencil on your dormitory window a la John Nash in “A Beautiful Mind.” Other common programs/languages include SPSS, R, Python, and even Excel VBA.
Most of the younger finance faculty at my MBA school say to forget about SAS and just use Stata. Stata is supposed to be better at the econometrics, and is more friendly to use since there are a ton of very thorough, official help files for Stata on the internet.
One of our younger faculty members uses Stata for the econometrics, but SAS for preparing the data. The advantage to SAS is that it doesn’t load your entire data set into memory, like Stata, and just reads it from the hard drive. This makes SAS better for getting very large data sets ready for the tests/regressions/whatever.
I’ve learned Stata fairly well through all my economics classes, and I also put together a self-study course on Stata, with the guidance of a professor, for my final MBA semester. I plan to spend a lot of time this summer learning SAS, so I’ll probably have more to say on this subject later.