I have a Python background and recently signed for the Coursera course on R that just started (https://www.coursera.org/course/compdata) because I wanted to get a small taste of R and see how it differed from Python's scientific computing stack.
So right now I'm not far enough in the learning curve to see all the benefits R provides. Is it worth investing time in R now if I'm already pretty familiar with a good amount of the Python ecosystem? Or, would it make more sense to continue on in Python?
If you are serious about data analysis you should probably at least read R (and maybe Matlab) as lots of algorithms were released and only exist in one of those languages.
You could get by with a more general statistics course that happened to use R.
I would say it depends on precisely what scientific work you need to do. E.g. for phylogenetic statistics there are some nice R packages that bundle simulation techniques and measures that are so far not implemented as conveniently, or at all, in Python. So you need to explore what packages/libraries are out there that fulfill your needs (and also consider how much time/skill/interest you have to code your own packages/libraries where needed). R is still really popular for stats/prototyping/data viz and a useful language to have up your sleeve.
So right now I'm not far enough in the learning curve to see all the benefits R provides. Is it worth investing time in R now if I'm already pretty familiar with a good amount of the Python ecosystem? Or, would it make more sense to continue on in Python?