Issue #157
May 29, 2022
This weeks Data Science Book is " The Practitioner's Guide to Graph Data " by D. K. Gosnell and M. Broecheler. Graph Thinking and Graph Data are topics near and dear to our hearts here at D4Sci (checkout G4Sci if you haven't yet) and this book does an excellent job of introducing both fundamental and advanced topics and techniques using practical real world datasets and state of the art graph databases. The book is exceptionally well written and easy to follow, with practical "rules of thumb" generously sprinkled throughout along with practical examples that you can use to grok as the various concepts are they are introduced. A must have for anyone interested in Graph Thinking and Graph Databases.
- 1. Lessons Learned From Running Apache Airflow at Scale [shopify.engineering]
- 2. Physics origins of the most important statistical ideas of recent times [science-memo.blogspot.com]
- 3. (How to Write a (Lisp) Interpreter (in Python)) [norvig.com]
- 4. Useful Python decorators for Data Scientists [bytepawn.com]
- 5. The end of Big Data [benn.substack.com]
- 6. Why unprecedented bird flu outbreaks sweeping the world are concerning scientists [nature.com]
- 7. Complete Detailed Tutorial on Linear Regression in Python for Beginners [pub.towardsai.net]
- 8. Artificial intelligence is breaking patent law [nature.com]
- • Belief propagation for permutations, rankings, and partial orders (G. T. Cantwell, C. Moore)
- • Group mixing drives inequality in face-to-face gatherings (M. Oliveira, F. Karimi, M. Zens, J. Schaible, M. Génois, M. Strohmaier)
- • Addressing the socioeconomic divide in computational modeling for infectious diseases (M. Tizzoni, E. O. Nsoesie, L. Gauvin, M. Karsai, N. Perra, S. Bansal)
- • Are Prompt-based Models Clueless? (P. Kavumba, R. Takahashi, Y. Oda)
- • FLiB: Fair Link Prediction in Bipartite Network (P. Kansal, N. Kumar, S. Verma, K. Singh, P. Pouduval)
- • Autonomous graph mining algorithm search with best performance trade-off (M. Yoon, T. Gervet, B. Hooi, C. Faloutsos)
- • Neighbourhood matching creates realistic surrogate temporal networks (A. Longa, G. Cencetti, S. Lehmann, A. Passerini, B. Lepri)
Leslie Lamport: Thinking Above the Code
All our videos are also available in our YouTube playlist.
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