Open Data Science Training

Sponsored & Developed By:

• Alpine Data Labs http://alpinenow.com
• Pivotal Inc. http://gopivotal.com
• Nitin Borwankar https://github.com/nborwankar

What

• A collection of Open Data Science Training lessons in the form of IPython Notebooks.
• Associated data sets.

The initial beta release consists of four major topics

• Linear Regression - making predictions about real-world quantities
• Logistic Regression - resolving questions with binary or yes/no outcomes
• Random Forests - handling data where the number of variables is very high
• K-Means Clustering - discovering natural groupings or segments in data

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.

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 Data Science fundamentals.

The IPython Notebook format 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.

IPython Notebooks at beta

• A0. How to use this content.ipynb
• A1. Linear Regression - Overview.ipynb
• A2. Linear Regression - Data Exploration - Lending Club.ipynb
• A3. Linear Regression - Analysis.ipynb
• B1. Logistic Regression - Overview.ipynb
• B1a. Odds, LogOdds and Logit Function .ipynb
• B2. Logistic Regression - Data Exploration.ipynb
• B3. Logistic Regression - Analysis.ipynb
• C1. Random Forests - Overview.ipynb
• C2. Random Forests - Data Exploration.ipynb
• C3. Random Forests - Analysis.ipynb
• D1. K-Means Clustering - Overview.ipynb
• D2. K-Means Clustering - Data Exploration.ipynb
• D3. K-Means Clustering Analysis.ipynb
• WA1. Linear Regression Overview Worksheet.ipynb
• WA2. Linear Regression - Data Exploration - Lending Club Worksheet.ipynb
• WA3. Linear Regression - Analysis Worksheet.ipynb
• WA4. Linear Regression - Data Cleanup.ipynb
• WB3. Logistic Regression - Analysis- Worksheet.ipynb
• WC3. Random Forests - Analysis - Worksheet.ipynb
• WC4. Random Forests - Data Cleanup.ipynb
• WD2. K-Means Clustering - Data Exploration-Worksheet.ipynb
• WD3. K-Means Clustering Analysis - Worksheet.ipynb
• Z0. A quick tour of the IPython notebook.ipynb
• Z1. Appendix 1 Plotting code snippets.ipynb