It is also not nearly as bad as you depict. Vector drawing is handled nicely by pgf/tik. If you want meta-control of tikz, you can use the wonderful tikz backend for matplotlib. There are also beautiful ways to produce EPS or (better yet imho) PDF for LaTeX, with embedded TeX fonts, including Matplotlib and the amazingly powerful PyX. Btw, the graphics inclusion package is graphicx.

There is a petition to help this student, asking Dawson to reinstate him, make him whole financially, and apologize.

I think Lott is opinionated, inflammatory, and important. I suspect he is probably wrong in his concealed carry conclusions. However in a complex matter like this, as in pretty much any scientific inquiry, those of us who are not specialists should rely on the consensus of the experts. In this case, there is no consensus on how concealed carry affects murder rates, so holding a strong opinion is unjustified. (It follows that we can expect a lot of ideological shouting.)

One thing that is known to raise murder rates is high and sustained unemployment. Neither concealed carry laws nor unemployment rates appear relevant to this shooting, however.

I'm unaware of such "hard data". Cites please. Btw, if you are referring to John Lott's important work, note that the National Academy of Sciences reviewed this and did not back his core claims.

NumPy includes a matrix library: foo=mat('1 2;3 4'). In general, Python's syntax beats Matlab's syntax
hands down. (In this particular case there is almost a tie, but a trivial advantage for Matlab. I spend apporximately 0% of my time typing in data for array construction, and I suppose that is true of most users.) For help transitioning, see http://www.scipy.org/NumPy_for_Matlab_Users.

I also wonder how many who quote Lindzen on climate change also quote Lindzen on smoking?

R handles non-matrix data structures much, much better than Matlab does

This advantage is even larger for Python. Use the NumPy package for efficient array handling and basic linear algebra. Use SciPy for optimization and statistics. Use Matplotlib for amazingly powerful 2d graphics. And if you occasionally need R, which does have an wonderfully deep statistical library, you can access it with rpy.