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Open Content for self-directed learning in data science
Perl Python CSS
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  • A collection of Data Science Learning materials in the form of IPython Notebooks.
  • Associated data sets.

The initial beta release consists of four major topics

  • Linear Regression
  • Logistic Regression
  • Random Forests
  • K-Means Clustering

Each of the above has at least three IPython Notebooks covering

  • Overview (an exposition of the technique for the math-wary)
  • Data Exploration (the nuts and bolts of real world data wrangling)
  • Analysis (using the technique to get results)

One or more of these may have supplementary material. Each of these have worksheets that contain mostly the code sections so you can iteratively explore the code.

Three openly available data sets are used.


There's a need for open content to raise the level of awareness and training in basics, in the Data Science field (circa early 2013).

IPython Notebook provides an appropriate platform for rapid iterative exploration and learning.


Starting in 2013 and intended to extend for a long while.


Today github, tomorrow the world.


Learn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project Most of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at, and is freely available. Extensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.

IPython Notebooks at Beta.

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