CS109 Readings

Readings are not mandatory, but highly recommended. Post your comments and questions using a “Note” in Piazza with the appropriate title (e.g., Readings Week 1: …). Always add the hashtag #readings. To avoid a proliferation of different notes on the same topic please use the “Followup discussions” feature in Piazza.


You can read O’Reilly books for free with a Harvard login at this web site: http://proquest.safaribooksonline.com.ezp-prod1.hul.harvard.edu/

  • Python for Data Analysis, O’Reilly Media - Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.
  • Machine Learning for Hackers, O’Reilly Media - If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning, a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.
  • A translation of the R examples in Machine Learning for Hackers to Python can be found here: http://slendrmeans.wordpress.com/will-it-python/
  • Probabilistic Programming and Bayesian Methods for Hackers - The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author’s own prior opinion.