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D4S Sunday Briefing #111
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Jul 11, 2021
Welcome to the 111th issue of the Sunday Briefing.
You can also checkout the latest post at G4Sci: Network Motifs: Frequent patterns in Graphs where we introduce the ESU algorithm for exhaustive enumeration of all subgraphs of a given size. You should Subscribe to G4Sci to make sure you never miss a post!
Over at Medium, Competing CoVID-19 Strains is the most recent post on the Epidemiology series and Mediation is the latest for the Causality series while we continue to work on the particularly long section 3.8 of the Primer. Finally, As always you can find the code in the Epidemiology and Causality GitHub repos, respectively.
Our next webinar on Transforming Excel Analysis into Python and pandas Data Models, coming up on July 26th. In this webinar we will show you how to integrate Excel in your Python workflow so that you can easily extract data from Excel worksheets and export the output of your Python analyses as fully formatted Excel workbooks. If that’s sounds interesting, don’t forget to Register!
This week we consider if the Future of Deep Learning Is Photonic, how Neurons Unexpectedly Encode Information in the Timing of Their Firing and explore some Data Structure Visualizations and consider the Top 10 Ideas in Statistics That Have Powered the AI Revolution.
From the halls of academia, we learn how we can use NLP techniques to Learn the language of viral evolution and escape, explore Causal Reinforcement Learning using Observational and Interventional Data and how to Grow Urban Bicycle Networks.
Finally, this weeks ‘Data Science Book’ highlight is “Think Bayes (2nd Ed)” by Allen Downey and, as always you can find all the previous book recommendations on our website. In the video of the week we have a Plotly tutorial.
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The D4S Team
The latest post in the Causality series covers section ‘3.7 — Mediation’, a recipe to calculate the controlled directed effect. The code for each blog post in this series is hosted by a dedicated GitHub repository: https://github.com/DataForScience/Causality
The latest post in the CoVID-19 series, ‘Competing CoVID-19 Strains’ takes a look at the likely impact that the introduction of a more virulent strain might have in the course of the pandemic. As usual, all the code is available in GitHub: http://github.com/DataForScience/Epidemiology101
Data Science Book:
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.
Tutorials and blog posts that came across our desk this week.
- The Future of Deep Learning Is Photonic [spectrum.ieee.org]
- Neurons Unexpectedly Encode Information in the Timing of Their Firing [quantamagazine.org]
- When Graphs Are a Matter of Life and Death [newyorker.com]
- Deep Reinforcement Learning is a waste of time [jtoy.net]
- GitHub’s AI Copilot Might Get You Sued If You Use It [medium.com/geekculture]
- Data Structure Visualizations [cs.usfca.edu/~galles/]
- Top 10 Ideas in Statistics That Have Powered the AI Revolution [news.columbia.edu]
Fresh From The Press:
Some of the most interesting academic papers published recently
- Banks, alternative institutions and the spatial–temporal ecology of racial inequality in US cities (M. L. Small, A. Akhavan, M. Torres, Q. Wang)
- Learning the language of viral evolution and escape (B. Hie, E. D. Zhong, B. Berger, B. Bryson)
- Uncovering the fragility of large-scale engineering projects (M. Santolini, C. Ellinas, C. Nicolaides)
- The Value of Statistical Learning to Cognitive Network Science (E. A. Karuza)
- Causal Reinforcement Learning using Observational and Interventional Data (M. Gasse, D. Grasset, G. Gaudron, P.-Y. Oudeyer)
- Growing Urban Bicycle Networks (M. Szell, S. Mimar, T. Perlman, G. Ghoshal, R. Sinatra)
- The Geography of Open Source Software: Evidence from GitHub (J. Wachs, M. Nitecki, W. Schueller, A. Polleres)
Video of the Week:
Interesting discussions, ideas or tutorials that came across our desk.
All the videos of the week are now available in our Youtube playlist
Opportunities to learn from us:
- Jul 26, 2021 — Transforming Excel Analysis into Python and pandas Data Models [Register]
- Aug 9, 2021 — Graphs and Network Algorithms for Everyone [Register] 🆕
- Aug 30, 2021 — Why and What If — Causal Analysis for Everyone [Register] 🆕
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