Original Source Here
D4S Sunday Briefing #154
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
May 08, 2022
Welcome to the 154th edition of the Sunday Briefing.
This week we continue on hiatus from blogging. While we prepare a double feature for next week, you can catch up on the latest post in the G4Sci series: Neighborhood Overlap and Edge Weights. Over in the V4Sci substack, the latest post cover the NASA Climate Spiral, while on Medium we have a recap of the Top 10 Books we read in 2021.
On our regularly scheduled content we have a look at how the CDC Tracked Millions of Phones to See If Americans Followed COVID Lockdown Orders, a biography of Douglas Hofstadter, the Man Who Would Teach Machines to Think and 8 best practices for optimizing AWS Lambda functions.
While on the more academic front, we explore Analytical Models for Motifs in Temporal Networks, Betweenness centrality in dense spatial networks and a unified theory of information transfer and causal relation.
This weeks ‘Data Science Book’ highlight is Data Science Book is “The Practitioner’s Guide to Graph Data” by D. K. Gosnell and M. Broecheler. As always you can find all the previous book recommendations on our website. In the video of the week we have a look at How Netflix recommends movies with Matrix Factorization.
Data shows that the best way for a newsletter to grow is by word of mouth, so if you think one of your friends or colleagues would enjoy this newsletter, just go ahead and forward this email to them. This will help us spread the word!
The D4S Team
The latest post in the CoVID-19 series, ‘How to model the effects of vaccination’ takes a look at how simple modifications of the SIR model can help us better understand how vaccines work. As usual, all the code is available in GitHub: http://github.com/DataForScience/Epidemiology101
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
Data Science Book:
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.
Tutorials and blog posts that came across our desk this week.
- VSCode — Markdown Edition [blog.dendron.so]
- Use Fast Data Algorithms [jolynch.github.io]
- CDC Tracked Millions of Phones to See If Americans Followed COVID Lockdown Orders [vice.com]
- The Man Who Would Teach Machines to Think [theatlantic.com]
- Searching for What Connects Us, Carlo Rovelli Explores Beyond Physics [nytimes.com]
- How to professionally say [howtoprofessionallysay.akashrajpurohit.com]
- 8 best practices for optimizing Lambda functions [cloudash.dev]
Fresh From The Press:
Some of the most interesting academic papers published recently
- Deep Link-Prediction Based on the Local Structure of Bipartite Networks (H. Lv, B. Zhang, S. Hu, Z. Xu)
- Evaluation and promotion strategy of resilience of urban water supply system under flood and drought disasters (Z. Li, H. Zhao, J. Liu, J. Zhang, Z. Shao)
- Betweenness centrality in dense spatial networks (V. Verbavatz, M. Barthelemy)
- Analytical Models for Motifs in Temporal Networks (A. Porter, B. Mirzasoleiman, J. Leskovec)
- Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning (Di Jin, Cuiying Huo, Jianwu Dang, P. Zhu, W. Zhang, W. Pedrycz, L. Wu)
- A unified theory of information transfer and causal relation (Y. Tian, H. Hou, Y. Wang, Z. Zhang, P. Sun)
- The universality in urban commuting across and within cities (L. Dong, P. Santi, Y. Liu, S. Zheng, C. Ratti)
Video of the Week:
Interesting discussions, ideas or tutorials that came across our desk.
How does Netflix recommend movies? Matrix Factorization
All the videos of the week are now available in our Youtube playlist.
Opportunities to learn from us:
- May 20, 2022 — Transforming Excel Analysis into Python and pandas Data Models [Register]
- Jun 07, 2022 — Graphs and Network Algorithms for Everyone [Register] 🆕
- Jun 23, 2022 — Why and What If — Causal Analysis for Everyone [Register] 🆕
Long form tutorials:
- Natural Language Processing 5.5h, covering basic and advancing techniques using NLTK and Keras
- Times Series Analysis for Everyone 6h covering data pre-processing, visualization, ARIMA, ARCH and Deep Learning models
Thank you for subscribing to our weekly newsletter with a quick overview of the world of Data Science and Machine Learning. Please share with your contacts to help us grow!
Publishes on Sunday.
Read all stories on Medium: https://bgoncalves.medium.com/membership
Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot