AstroML: Machine Learning and Data Mining for Astronomy.
astroML 0.2 was released in November. Source on Github.
Introduction to astroML received the CIDU 2012 best paper award.
From the webpage:
AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, and matplotlib, and distributed under the 3-clause BSD license. It contains a growing library of statistical and machine learning routines for analyzing astronomical data in python, loaders for several open astronomical datasets, and a large suite of examples of analyzing and visualizing astronomical datasets.
The goal of astroML is to provide a community repository for fast Python implementations of common tools and routines used for statistical data analysis in astronomy and astrophysics, to provide a uniform and easy-to-use interface to freely available astronomical datasets. We hope this package will be useful to researchers and students of astronomy. The astroML project was started in 2012 to accompany the book Statistics, Data Mining, and Machine Learning in Astronomy by Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray, published by Princeton University Press. The table of contents is available here: here(pdf), or you can view the book on Amazon.
Version 0.2 has improved documentation and examples.
Looking forward to the further development of this package!
BTW, be aware that data mining skills, save for domain knowledge, are largely transferable.