I feel the need for speed. If you are messing about with decision-support in a security context, you probably do too. It turns out that for most of what I have needed to do in the last couple of weeks, Python has been taking closer me to my targets than R.
It will be a while (probably a long while) before Python tooling can match the comprehensiveness of R, which has > 4k packages available. http://r4stats.com/2013/03/19/r-2012-growth-exceeds-sas-all-time-total/
For pure statisticians, R is still the win, and I don't mean to trash the tool or the field in any way. If I hadn't found R, way back when, I would probably have thought MS Excel was an acceptable program for stats. Leading to FAIL.
But, Python tooling looks like being faster, in both execution and development speed for my needs of the moment. R may still be the winner in creating interactive doc. I still need to take a weekend and compare the two. But free weekends are in short supply right now, and it would have to be an awfully big win to make much of a difference. I am huge believer in 'go get knowledge, then teach it' but I am not primarily an educator, and tools such as the iPython HTML Notebook seem adequate.
Regarding iPython: don't use anything else. Seriously. The only time I ever enter 'python' instead of 'ipython' on the command line is if I need a quick basic calculator, or if all I want to do is import numpy, do a couple couple of quick array operations, and leave.
Don't laugh at the idea of using Python as a basic calculator (snarking on KDE Kcalc):
Seriously, kcalc, WTF is your problem? People have been able to take square roots on hand-held calculators for 40 years, but your software, running on this comparative pile-driver of awesome, cannot.
With iPython, you get log files and other huge advantages. Mess around with it for a couple of evenings, and you will never go back. I wished for something like this for ten years before we got it, so now I have been enjoying it. You will too.