I just completed the Coursera data science track which took me from a complete R newbie to being at least somewhat proficient. Having previously used Python for a quite a bit of web programming, I disliked R at first except for its power in statistical programming. But I've since discovered a number of great R packages that make it a pleasure to use for things I would normally turn to Python for. Like I recently discovered the rvest package for webscraping.

Data visualizations with R seem vastly superior, unless I am missing something with Python (highly likely). And putting up a slick statistics app is easy with shiny or RStudio Presenter. But R can't really scale to a large production app, isn't that right?

So I feel I need to keep working with both Python and R.

Added: That's a nice list Lofkin. Thanks. Also, in the article he says that Python syntax feels more natural, which I also felt. But then I started to use things like the magrittr and dplyr packages in R which gives you nice things like pipes and that feeling starts to ebb.

For stats plotting and web apps in python: https://github.com/bokeh/bokeh

For calling r libraries in python: https://pypi.python.org/pypi/rpy2

For out of core datasets in python: https://github.com/blaze/dask https://github.com/blaze/blaze