This afternoon of talks will cover some basic libraries for Python data science. The session is a
part of the data science weekend that includes R-Bootcamps and the
theoretical and R-oriented machine learning session on Sunday morning. The Python topics covered here are stand-alone modules. This content has been chosen to both complement and extend upon the prior sessions.
Prerequisites: Ideal would be basic python knowledge,
although if you're just interested in seeing what Python can do for you,
it's a pretty easy language to read. If you want to install and try to
follow along, the simplest method is to install the Enthought free
distribution, which will solve many installation problems you might have
with the necessary libraries:
You will then want to install pandas, scikit-learn,
statsmodels, and patsy (links below). If you want to go-it-alone, you
should minimally get Python 2.7 and then install numpy, IPython,
matplotlib, scipy, and then the other libraries.