Issue #146

July 4, 2021

This weeks Data Science Book is " Causality " by J. Pearl. Causal Inference is a lively and fast developing area in Data Science that we believe has the potential to be truly revolutionary in coming years (you can get a quick overview of the main ideas in our Causal Inference series over at Medium). Judea Pearl is one of the most prominent founding fathers of this field that he introduces masterfully in this textbook. While the approach Pearl chooses is mathematically rigorous, thanks to his rich use of toy examples, the key ideas and concepts are easily grasped and adapted to real world datasets. Causal Inference is a powerful arrow in any Data Scientist's quiver and this is the ideal starting point if you're interested in taking the first steps in this exciting area.

Causality

1. Facebook Libra: the inside story of how the company’s cryptocurrency dream died [ft.com]
2. Principal Component Selection: The Broken-Stick Model [mohanwugupta.com]
3. Shopify's Data Science & Engineering Foundations [shopify.engineering]
4. Why Graph Computing is STELLAR [juliustech.co]
5. How to use undocumented web APIs [jvns.ca]
6. Damn Cool Algorithms: Levenshtein Automata [blog.notdot.net]
7. 5 Python Libraries That Will Help Automate Your Life [medium.com/geekculture]
8. What You Must Know about Memory, Caches, and Shared Memory [eidos.ic.i.u-tokyo.ac.jp/~tau]

• Covid-19 Vaccine Effectiveness against the Omicron (B.1.1.529) Variant (N. Andrews, J. Stowe, F. Kirsebom, S. Toffa, T. Rickeard, E. Gallagher, C. Gower, M. Kall et al)
• Homophily in Voting Behavior: Evidence from Preferential Voting (L. Coufalová, Š. Mikula, M. Ševčík)
• Modeling Communicable Diseases, Human Mobility, and Epidemics: A Review (D. Soriano-Paños, W. Cota, S. C. Ferreira, G. Ghoshal, A. Arenas, J. Gómez-Gardeñes)
• Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey: A repeated cross-sectional study (A. Gimma, J. D. Munday, K. L. M. Wong, P. Coletti, K. van Zandvoort, K. Prem, CMMID COVID-19 working group, P. Klepac, G. James Rubin, S. Funk, W. J. Edmunds, C. I. Jarvis)
• No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds (A. I. Humayun, R. Balestriero, A. Kyrillidis, R. Baraniuk)
• It is high time we let go of the Mersenne Twister (S. Vigna)
• Bandit Sampling for Multiplex Networks (C. Baykal, V. K. Potluru, S. Shah, M. M. Veloso)
• The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks (A. Bastounis, A. C. Hansen, V. Vlačić)

Dask Tutorial

July 4, 2021

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