Interesting post from Brendan O’Connor:

Comparison of data analysis packages: R, Matlab, SciPy, Excel, SAS, SPSS, Stata

Interesting post from Brendan O’Connor:

Comparison of data analysis packages: R, Matlab, SciPy, Excel, SAS, SPSS, Stata

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The neatness and power of the Python language alone is a powerful argument in favor of any Python-based solution. I think that some python packages can certainly be a mess, but I am not entirely sure they can qualify as immature. The scipy package goes back to Numerical Python. Overall, it is at least 10 years old. It might be messy, but then, can you come up with an objective “messiness” measure?

One of the most important considerations is how well the messiness is encapsulated. If the code is rock solid, well-documented, has a clean interface, and I don’t have to maintain it, I don’t care so much how clean it is inside. In Joel Spolsky’s phrase, the most important thing is whether the abstraction leaks.

I ve never used the Matlab Statistical Toolbox. I m wondering, how good is it compared to R?