Issue #180

November 7, 2022

This weeks Data Science Book is " Essential Math for Data Science " by T. Nield. Here at Data For Science, we pride ourselves in taking a deep approach to the algorithms and models we employ. For this, good mathematical foundations are paramount. In his recent book T. Nield introduces the reader to enough of the mathematics underlying Data Science and Machine Learning to give you a head start in grokking both classic and state of the art algorithms that your likely to encounter in your career as a Data Scientist. A highly recommended read to anyone who wants to look inside the black boxes we use everyday.

Essential Math for Data Science

1. AlphaFold's new rival? Meta AI predicts shape of 600 million proteins [nature.com]
2. Mathics: A free, open-source alternative to Mathematica [mathics.org]
3. The Myers difference algorithm [nathaniel.ai]
4. Command-line data analytics made easy [danielcmoura.com]
5. Scientists Increasingly Can't Explain How AI Works [vice.com]
6. Road to Artificial General Intelligence [maraoz.com]
7. The Secret Microscope That Sparked a Scientific Revolution [wired.com]
8. Braess's paradox [resources.mpi-inf.mpg.de]

• Mapping nonlocal relationships between metadata and network structure with metadata-dependent encoding of random walks (science.org)
• Predicting network dynamics without requiring the knowledge of the interaction graph (B. Prasse, P. Van Mieghem)
• The Human Brain in Numbers: A Linearly Scaled-up Primate Brain (S. Herculano-Houzel)
• Disentangling the climate divide with emotional patterns: a network-based mindset reconstruction approach (R. Cremades, M. Stella)
• Urban Mobility (L. Alessandretti, M. Szell)
• How Democracies Polarize: A Multilevel Perspective (S. Huang, A. F. Siegenfeld, A. Gelman)
• A Survey on Digital Epidemiology: Networks and Models (D. Pastor-Escuredo, L. E. Olmos)

All the videos of the week are now available in ourYoutube playlist.

November 7, 2022

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