Issue #110

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. John Urschel: From NFL Player to Mathematician [quantamagazine.org]
2. You Don’t Understand Neural Networks Until You Understand the Universal Approximation Theorem [medium.com/analytics-vidhya]
3. Offline Policy Evaluation: Run fewer, better A/B tests [edoconti.medium.com]
4. Using sqlite3 as a notekeeping document graph with automatic reference indexing [epilys.github.io/bibliothecula]
5. Make Patterns Pop Out of Heatmaps with Seriation [nicolas.kruchten.com]
6. Is Facebook's "Prophet" the Time-Series Messiah, or Just a Very Naughty Boy? [microprediction.com]
7. Double Machine Learning for causal inference [towardsdatascience.com]

• Human social sensing is an untapped resource for computational social science (M. Galesic, W. B. de Bruin, J. Dalege, S. L. Feld, F. Kreuter, H. Olsson, D. Prelec, D. L. Stein, T. van der Does)
• Measuring algorithmically infused societies (C. Wagner, M. Strohmaier, A. Olteanu, E. Kıcıman, N. Contractor, T. Eliassi-Rad)
• Meaningful measures of human society in the twenty-first century (D. Lazer, E. Hargittai, D. Freelon, S. Gonzalez-Bailon, K. Munger, K. Ognyanova, J. Radford)
• Network Structure, Metadata, and the Prediction of Missing Nodes and Annotations (D. Hric, T. P. Peixoto, S. Fortunato)
• Multilayer networks for text analysis with multiple data types (C. C. Hyland, Y. Tao, L. Azizi, M. Gerlach, T. P. Peixoto, E. G. Altmann)
• Information Theory: A Tutorial Introduction (J. V. Stone)
• Untangling urban data signatures: unsupervised machine learning methods for the detection of urban archetypes at the pedestrian scale (G. D. Simons)
• When standard network measures fail to rank journals: A theoretical and empirical analysis (G. Vaccario, L. Verginer)
• Introducing PathQuery, Google's Graph Query Language (J. Weaver, E. Paniagua, T. Agarwal, N. Guy, A. Mattos)

Causal Inference

July 4, 2021

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