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D4S Sunday Briefing #162
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Jul 4, 2022
Welcome to the July 4th edition of the Sunday Briefing. This week we’re on hiatus from blogging, but you can still catch up on our recent posts. First, we celebrate World Goth Day with the latest V4Sci post “Unknown Pleasures Plot: Recreating an iconic album cover and your favorite 80s t-shirt”. Over at Medium, we have “Exploring the Dow-Jones Industrial Average using Linear Regression”. You can also catch up on the latest post in the G4Sci series: Prim’s Minimum Spanning Tree Algorithm: Finding the shortest path to every node.
In addition to the three new webinars we announced in these months issues (Deep Learning for Everyone on July 7the, Time Series for Everyone on July 26th and Advanced Time Series for Everyone on Aug 9) we’re proud to announce the latest edition of Natural Language Processing (NLP) for Everyone on Aug 25th!
On our regularly scheduled content we have a look at Graph Machine Learning at Airbnb, an exploration of of how to Visualize Algorithms and An Engineer’s Best Tips for Writing Documentation Devs Love.
While on the more academic front we have a review of Network resilience, a compilation of Pen and Paper Exercises in Machine Learning and a new approach to Inferring Tie Strength in Temporal Networks.
This weeks ‘Data Science Book’ highlight is Data Science Book is “Analytical Skills for AI & Data Science” by D. Vaughan. As always you can find all the previous book recommendations on our website. In the video of the week we have an Introduction to GIS Analysis with GeoPandas using Python.
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The D4S Team
The latest post on the Visualization for Data Science substack: Unknown Pleasures Plot: Recreating an iconic album cover and your favorite 80s t-shirt is now out, Don’t forget to Subscribe so you’re first in line to receive every post.
The latest post on the Graphs for Data Science substack: Prim’s Minimum Spanning Tree Algorithm: Finding the shortest path to every node is now out.You should Sign Up to make sure you never miss a post!
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
This weeks Data Science Book is “Analytical Skills for AI & Data Science” by D. Vaughan. This is an unusual book that takes a holistic approach to AI and Data Science from a Business perspective. Aimed at managers with limited Data Science experience, this book uses increasingly complex practical examples to introduce a wide range of concepts and analytical techniques. Even if you have no direct interest in AI and Data Science, this book will give you enough background knowledge to be able to successfully manage Data Scientists, break down complex problems into individual components and help guide your team towards the right solution to your business problems.
Tutorials and blog posts that came across our desk this week.
- Just say no (to content): Nietzsche’s surprising “information diet” [arstechnica.com]
- Graph Machine Learning at Airbnb [medium.com/airbnb-engineering]
- Can Computers Be Mathematicians? [quantamagazine.org]
- An Engineer’s Best Tips for Writing Documentation Devs Love [thenewstack.io]
- Visualizing Algorithms [bost.ocks.org]
- The Feynman Technique 2.0: How to Take Your Learning to the Next Level [themindcollection.com]
- The dark side of Graph Neural Networks [appliedexploration.com]
Some of the most interesting academic papers published recently
- Network resilience (X. Liu, D. Li M. Ma, B. K. Szymanski, H. E. Stanley, J. Gao)
- Event-level prediction of urban crime reveals a signature of enforcement bias in US cities (V. Rotaru, Y. Huang, T. Li, J. Evans, I. Chattopadhyay)
- A Weighted Symmetric Graph Embedding Approach for Link Prediction in Undirected Graphs (Z. Wang, Y. Chai, C. Sun, X. Rui, H. Mi, X. Zhang, P. S. Yu)
- Eigenvalue ratio statistics of complex networks: Disorder versus randomness (A. Mishra, T. Raghav, S. Jalan)
- Community Detection in the Heterogeneous Stochastic Block Model (D. Saulpic, F. Mallmann-Trenn, V. P. Cohen-addad)
- Pen and Paper Exercises in Machine Learning (M. U. Gutmann)
- Modeling Teams Performance Using Deep Representational Learning on Graphs (F. Carli, P. Foini, N. Gozzi, N. Perra, R. Schifanella)
- Inferring Tie Strength in Temporal Networks (L. Oettershagen, A. L. Konstantinidis, G. F. Italiano)
Interesting discussions, ideas or tutorials that came across our desk
Introduction to GIS Analysis with GeoPandas using Python
All the videos of the week are now available in our Youtube playlist.
Opportunities to learn from us:
- Jul 07, 2022 — Deep Learning for Everyone [Register] 🆕
- Jul 26, 2022 — Time Series for Everyone [Register] 🆕
- Aug 9, 2022 — Advanced Time Series for Everyone [Register] 🆕
- Aug 25, 2022 — Natural Language Processing 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
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