Book Library

Internal Book Library

• Science Research Writing For Non-Native Speakers Of English, Hilary Glasman-Deal, 2009.
• Social Network Analysis for Startups, Maksim Tsvetovat and Alexander Kouznetsov, 2011.
• Statistics, Data Mining, and Machine Learning in Astronomy, Željko Ivezic ́et al, 2014.
• Real computing make Real, Forman S. Acton, 1996.
• Think Bayes: Bayesian Statistics in Python, 2013 (FREE)
• Python for Data Analysis, Wes McKinney, 2013.
• Python Cookbook, David Beazley and Brian K. Jones, 2013.
• Network Science: Theory and Application, Ted G. Lewis, 2014.
• Machine Learning for Hackers, Drew Conway and John Myles White, 2012.
• Algorithm Design, Jon Kleinberg, Eva Tardos, 2005.
• Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006.
• Probabilistic Programming & Bayesian Methods for Hackers
• Scopatz and Huff, Effective Computation in Physics, 2015.
• Effective Python: 59 Specific Ways to Write Better Python, Brett Slatkin, 2015.
• Introduction to the Modeling and Analysis of Complex Systems, Hiroki Sayama, 2015 (FREE)
• Python for Finance Analyze Big Financial Data, Yves Hilpisch, O'Reilly 2014
• The Boost Graph Tutorial (FREE)
• Data Science from Scratch First Principles with Python, Joel Grus, 2015
• Introduction to Programming in Python, Robert Sedgewick, Kevin Wayne, Robert Dondero
• A Guide to Temporal Networks
• Python Data Science Handbook
• Introduction to Linear Algebra (Gilbert Strang)
• Hands-On Machine Learning with Scikit-Learn and TensorFlow (Aurélien Géron)
• Visualization Analysis & Design, 2014
• Fundamentals of Data Visualization, Claus O. Wilke
• Epidemic on Networks
• Statistical Rethinking