Dear friends,

Welcome to the July 19th edition of the Sunday Briefing.

This week we have exciting news on our blog. We just published the third installment of the Causal Inference Series, where we lay the foundations of Graph Theory that we'll need going forward. We hope you find it useful and look forward to your insightful comments. This new post resulted in an interesting Twitter discussion with Judea Pearl himself! We also refreshed the Causality GitHub repository with links to all the posts of the series and full Binder support.

A new post on the CoVID-19 series post in currently in the works (be on the look out for it later this week) and in the mean time, you can catch up on our previous posts and follow along with the code hosted on our Epidemiology101 GitHub repository.

We're also proud to announce a new webinar series on transitioning your analyses from Excel to Python. This is a new course that tries to introduce some of the features of Pandas and the Python ecosystem to Excel users who have become frustrated with its limitations and want to take their analytics to the next level. If you're interested can already sign up for the first edition here.

This week we take a look at the Data Structures & Algorithms that are actually used in major tech companies, the fundamentals of Entropy in Information Theory, the Mathematics of Mass Testing for COVID-19 and Second Wave of Algorithmic Accountability.

On the academic front, we confront the Computational Limits of Deep Learning, look at how we can predict mortality from 57 economic, behavioral, social, and psychological factors, how to learn Graph Structure With A Finite-State Automaton Layer  and, last but not least, we share a Survey on Transfer Learning in Natural Language Processing.

Finally, the video of the week Judea Pearl guides us through The Foundations of Causal Inference with an expert hand.

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Today, more than ever,
Semper discentes,

The D4S team