Who
 Nitin Borwankar http://twitter.com/nitin  primary developer
(Sponsored by Pivotal Labs and Alpine Data Labs).
What
 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
 KMeans Clustering
Each of the above has at least three IPython Notebooks covering
 Overview (an exposition of the technique for the mathwary)
 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.
 For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com
 For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
 UN data on economic indicators of countries
Why
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.
When
Starting in 2013 and intended to extend for a long while.
Where
Today github, tomorrow the world.
How
Learn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org Most of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, 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.
 A0. Before You Begin
 A1. Linear Regression  Overview
 A2. Linear Regression  Data Exploration  Lending Club
 A3. Linear Regression  Analysis
 B1. Logistic Regression  Overview
 B1a. Odds, LogOdds and Logit Function
 B2. Logistic Regression  Data Exploration
 B3. Logistic Regression  Analysis
 C1. Random Forests  Overview
 C2. Random Forests  Data Exploration
 C3. Random Forests  Analysis
 D1. KMeans Clustering  Overview
 D2. KMeans Clustering  Data Exploration
 D3. KMeans Clustering Analysis
 WA1. Linear Regression Overview Worksheet
 WA2. Linear Regression  Data Exploration  Lending Club Worksheet
 WA3. Linear Regression  Analysis Worksheet
 WA4. Linear Regression  Data Cleanup Worksheet
 WB3. Logistic Regression  Analysis Worksheet
 WC3. Random Forests  Analysis  Worksheet
 WC4. Random Forests  Data Cleanup Worksheet
 WD2. KMeans Clustering  Data ExplorationWorksheet
 WD3. KMeans Clustering Analysis  Worksheet
 Z0. A quick tour of the IPython notebook
 Z1. Appendix 1 Plotting code snippets
Background
If you are unfamiliar with IPython Notebook you can start with http://ipython.org/notebook
Installation

Prerequisites
One of the following distributions is needed. Please note that even if you have Python installed it is important to have one of these distributions installed and the binary for this installation in your path. This is because these distributions come packaged with all the supplementary libraries needed and these have been historically difficult to install separately. EPD Free Enthought Python Distribution from http://enthought.com
 Anaconda Python from http://continuum.io
 Development has been done on v 1.5 of Anaconda distribution but EPD Free should work just as well.
The following steps assume you have installed one of the distributions mentioned in prerequisites.

From a zip or tar file
 download the zip or tar file
 unpack the file to a directory called learnds
 cd to the 'notebooks' subdirectory
 start IPython Notebook 'ipython notebook pylab=inline'

From the git repo
 clone the repo
 cd to 'notebooks'
 start IPython Notebook 'ipython notebook pylab=inline'