Issue #111

July 4, 2021

This weeks Data Science Book is "Think Bayes (2nd Ed)" by Allen B. Downey. While Bayesian Statistics is a powerful tool in the toolbox of any Data Scientists, it is not the easiest of skills to learn if you are not mathematically inclined. In this book, Downey uses his down to earth, step by step style to make you proficient in the world of Bayesian Statistics by leveraging your pre-existing knowledge of Python instead of relying excessively on mathematical notation as most other books do. The book comes with a complete up-to-date GitHub repository so that you can more easily work your way through the example and cement your understanding of this important topic.

Think Bayes (2nd Ed)

1. The Future of Deep Learning Is Photonic [spectrum.ieee.org]
2. Neurons Unexpectedly Encode Information in the Timing of Their Firing [quantamagazine.org]
3. When Graphs Are a Matter of Life and Death [newyorker.com]
4. Deep Reinforcement Learning is a waste of time [jtoy.net]
5. GitHub’s AI Copilot Might Get You Sued If You Use It [medium.com/geekculture]
6. Data Structure Visualizations [cs.usfca.edu/~galles/]
7. Top 10 Ideas in Statistics That Have Powered the AI Revolution [news.columbia.edu]

• Banks, alternative institutions and the spatial–temporal ecology of racial inequality in US cities (M. L. Small, A. Akhavan, M. Torres, Q. Wang)
• Learning the language of viral evolution and escape (B. Hie, E. D. Zhong, B. Berger, B. Bryson)
• Uncovering the fragility of large-scale engineering projects (M. Santolini, C. Ellinas, C. Nicolaides)
• The Value of Statistical Learning to Cognitive Network Science (E. A. Karuza)
• Causal Reinforcement Learning using Observational and Interventional Data (M. Gasse, D. Grasset, G. Gaudron, P.-Y. Oudeyer)
• Growing Urban Bicycle Networks (M. Szell, S. Mimar, T. Perlman, G. Ghoshal, R. Sinatra)
• The Geography of Open Source Software: Evidence from GitHub (J. Wachs, M. Nitecki, W. Schueller, A. Polleres)

Plotly Tutorial

July 4, 2021

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