Dear friends,

Welcome to the 53rd edition of the Sunday Briefing.

Our latest blog post in the CoVID-19 series is now available on the blog: Visualizing the spread of CoVID-19 In this post we take a deep dive into the Johns Hopkins University data repository and some of the visualizations and analyses that can be made with it. As always, you can follow along with the GitHub repository containing the respective Python code. We hope you find it useful and gladly welcome any comments you might have.

This week in our usual content, we continue our exploration of Causal Inference with an informative research report on  Causality for Machine Learning from Fast Forward Labs and a paper on Causal Bayesian Optimization. On a more practical side, we have a a beginner's guide to web scraping with Python and "the" Ultimate Guide to Linear Regression.

On the academic front, we look at Statistical learning theory of structured data, Estimates of the proportion of SARS-CoV-2 infected individuals in Sweden as well as  Temporal Network Motifs: Models, Limitations, Evaluation.

Finally,  in the video of the week, Dr. Ahmad Bazzi guides through the Linear Algebra capabilities of NumPy, the fundamental building block of most machine learning and numerical algorithms in Python.

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Semper discentes,

The D4S team