I also highly recommend R. Dplyr and ggplot2 (noted by baldfat) are exceptional. I recently wrote a tutorial on dplyr here: http://www.sharpsightlabs.com/dply

I also highly recommend R. Dplyr and ggplot2 (noted by baldfat) are exceptional. I recently wrote a tutorial on dplyr here: http://www.sharpsightlabs.com/dplyr-intro-data-manipulation-with-r/ http://www.sharpsightlabs.com/dplyr-intro-data-manipulation-... To put this simply, dplyr's syntax is set up to create streamlined workflows. All of the major data management tasks (sort, subset, group, summarize) are easy to do. And they can be "chained" together (much like using pipes in Unix). Ggplot (another R package) is an amazing data visualization tool. The syntax has a deep underlying structure, based on the Grammar of Graphics theoretical framework. I won’t go into that too much, but suffice it to say, when you learn the ggplot2 syntax, you’re actually learning how to think about data visualization in a very deep way. You’ll eventually understand how to create complex visualizations without much effort. GGplot and dplyr are the reason I settled on R (instead of Python). When you use them together (again, using "chaining") you can explore your data rapidly and also create really high quality analyses.