Depending on what you are trying to accomplish, most of the tools are going to be the same as any other field. The differences are going to be in the type of data being evaluated and potentially in the type of model being used (logit, probit, regression, etc.). I would say R and Python are probably the most common "tools".

From a different angle of view, various methods and tools researchers used. Tools depend on desired methods, you can use STATA, R, PYTHON, and similarly flexible.

It depends on the data you want to analyse. R is a very usefull environment commonly used prior for statistical purposes. There are lots of packages in R you can use for big data, such as MARS, gam or any other growth models for data mining, clustering, classification tasks or any regression and ML techniques etc.