Issue #147
March 20, 2022
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.
- 1. How I Discovered Thousands of Open Databases on AWS [infosecwriteups.com]
- 2. Introduction to K-Means Clustering [pinecone.io]
- 3. Vectorization, dependencies and outer loop vectorization: if you can’t beat them, join them [johnysswlab.com]
- 4. Python Design Patterns [python-patterns.guide]
- 5. Convert curl commands to Python, JavaScript, PHP, R, Go, Rust, Elixir, Java, MATLAB, Ansible URI, Strest, Dart or JSON [curlconverter.com]
- 6. Bayes Rules! An Introduction to Applied Bayesian Modeling [bayesrulesbook.com]
- 7. Researcher uses 379-year-old algorithm to crack crypto keys found in the wild [arstechnica.com]
- 8. Official Tweet Downloader [developer.twitter.com]
- • Machine learning and phone data can improve targeting of humanitarian aid (Emily Aiken, Suzanne Bellue, Dean Karlan, Chris Udry, J. E. Blumenstock)
- • Group interactions modulate critical mass dynamics in social convention (I. Iacopini, G. Petri, A. Baronchelli, A. Barrat)
- • The effect of anti-money laundering policies: an empirical network analysis (P. Gerbrands, B. Unger, M. Getzner, J. Ferwerda)
- • Reconstructing social mixing patterns via weighted contact matrices from online and representative surveys (J. Koltai, O. Vásárhelyi, G. Röst, M. Karsai)
- • Dynamic importance of network nodes is poorly predicted by static structural features (C. van Elteren, R. Quax, P. Sloot)
- • The Mathematics of Artificial Intelligence (G. Kutyniok)
- • Dynamics on higher-order networks: A review (S. Majhi, M. Perc, D. Ghosh)
Node centrality and ranking on networks
All our videos are also available in our YouTube playlist.
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