General Python Resources

Important Tools and Libraries

• IPython: A REPL for easy interactive python development. Extremely useful for testing ideas out one line of code at a time. We will use IPython Notebook (a Web based interactive shell for Python) extensively in this class.
• Jupyter Notebook (formerly IPython Notebook)
• Jupyter Notebook Tutorial - Nice  tutorial video by Corey Schafer.
• matplotlib:  A very nice plotting library, capable of generating production-level visualizations programmatically. Matlab-like syntax makes plotting very easy.
• NumPy: The fundamental package for scientific computing with Python.
• SciPy: the open source library for mathematics, science and engineering
• scikit-learn: a robust machine learning library building on top of NumPy, SciPy and matplotlib. Includes of a wide variety of modeling techniques.
• Pandas (python data analysis library): data structures and tools for common data analysis tasks, including an efficient data frame implementation (similar to R).
• BeautifulSoup: A general parsing library particularly useful for parsing html and xml.
• NLTK: Natural Language Toolkit for Python, including tools for text preprocessing, tokenization, and vectorization (you may  also be interested in an online book that shows how NLTK is used).
• NetworkX: Python language library for the creation, manipulation, and analysis of graphs and networks.

Installation of Python and Scientific Libraries

• Anaconda - (Mac, Windows, Linux) Python distribution for large-scale data processing and scientific computing (includes scientific and data analysis libraries such as Numpy, Pandas, and scikit-learn, as well as IPython). This is the recommended package for this class. Anaconda provides distributions both for Python 3.x as well as Python.
• Managing Python Environments in Anaconda (e.g., to set up Python 2 and Python 3 kernels on the same machine)
• Notepad++: Excellent Python-friendly text editor
• Installing NumPy and SciPy
• Installing scikit-learn
• Installing Pandas
• Standalone Python Distributions

References for Data Analysis in Python

• Python Scientific Lecture Notes: including detailed notes on NumPy, Matplotlib, and Scipy
• NumPy and SciPy Documentation: including NumPy User Guide and Cookbook
• Pandas Tutorials: This is a guide to many pandas tutorials, geared mainly for new users.
• 10 Minutes to Pandas
• 10 Minute Tour of Pandas - A short video introduction by Wes Mackinny.
• Matplotlib User's Guide
• Matplotlib Pyplot Tutorial
• Jupyter Notebook Tutorials and Examples
• Natural Language Processing with Python: Online book on text processing and analysis using NLTK.

Other Relevant Tools & Resources

• Weka - Open source data mining package written in Java
• Weka Content Presentations on SlideShare
• The Weka Manual, as included in the distribution
• On-line stats book
• OpenIntro's textbook on basic statistics (openintro.org) - Basic statistics skills to get you started for analysis tasks.
• Statistical formulas for programmers
• Knowledge Discovery Nuggets (KDnuggets) - Great starting point: tools, links, companies, etc.

Data Sets