Both. R is great for learning Statistics and you can use it in real world applications all the time. Since R is common in academia new algorithms or techniques are usually packaged as R libraries first. I have never used Sage but I have used Python for data analysis (pandas, sci-kit-learn, stats-models) and it has certain advantages over R: first, as a general scripting language you can incorporate the data analysis into a larger program. This also makes it easy to work with non-Analysts (say when handing off code to an engineering team). Lastly, the Python data analysis community is very large (still growing too) and a lot of work is being put into the PyData eco-system, which means R is rarely ahead of Python for long (in the case of Machine learning algorithms Python is ahead).
(Sidenote: for data analysis I use the Anaconda python distribution. I do interactive computing I use iPython, and write scripts in NotePad++ and emacs)
Just remember: these are tools. You should know how to use multiple tools if you want to build anything interesting.