Data Science with Python

At the last Tech Talk Tuesday we took an overview of Python's  Data Science related packages.







The key packages for numerical computing are Numpy, Scipy and Scikit-learn.  The documentation for python is great, and makes presentations like this easy.  These packages are loaded with code samples, even for complex concepts like  Grid search and cross validation.    The machine learning package, scikit-learn also has exercises below the code samples.  Doing the exercises enforces the concepts, and is great preparation for solving problems like the ones in Kaggle competitions.

We also demoed iPython Notebooks, a fantastic way to create live data analysis documents.

Efficient Computing with NumPy - Jake Vanderplas by PyData

Jake Vanderplas Jake Vanderplas is an NSF post-doctoral fellow at University of Washington, working jointly between the Computer Science and Astronomy departments. His research involves applying recent advances in machine learning to large astronomical datasets, in order to learn about the Universe at the largest scales. He is co-author of "Statistics, Data Mining, and Machine Learning in Astronomy", a Python-centric textbook to be published by Princeton Press in 2013, and has presented many technical talks and papers in this subject area. In the Python world, Jake is active in maintaining and contributing to several core Python scientific computing packages, including Scikit-learn, Scipy, Matplotlib, and others. He occasionally blogs on python-related topics at http://ift.tt/1nhB2qI. What is PyData? PyData.org is the home for all things related to the use of Python in data management and analysis. This site aims to make open source data science tools easily accessible by listing the links in one location. If you would like to submit a download link or any items to be listed in PyData News, please let us know at: admin@pydata.org