Dear friends,
Welcome to the 101st issue of the Sunday Briefing.
This week we’re on hiatus from blogging but you can catch up on the latest post over at G4Sci: Graph Components: Strongly and Weakly Connected Components an overview of algorithms to identify connected components and how they can be used to better understand a real world communication network. 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.
May is dedicated to Time Series analysis with the first webinar, Time Series for Everyone happening tomorrow May 3rd and will guide you through the details of the ARIMA class of models. There’s a few spots left so don’t miss it! The second Time Series webinar, Advanced Time Series for Everyone is happening on May 26th and takes the concepts introduced in the first lecture further and introduces more advanced techniques and models.
This week we have a critical analysis of the Turing Test and what might replace it, how to look at Fourier transforms from a Neural Network perspective, Hopfield Networks and the Evolution of random number generators.
From the Ivory Tower, we look at Belief propagation for networks with loops, Circadian Regularities in Social Media Use, a survey on Text Classification Algorithms and a Review of Formal Methods applied to Machine Learning.
Finally, this weeks ‘Data Science Book ’ highlight is “Data Analysis: A Bayesian Tutorial” by D. S. Sivia and J. Skilling and in the video of the week we have a Selenium Tutorial For Beginners.
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Semper discentes,
The D4S Team
This weeks Data Science Book is " Data Analysis: A Bayesian Tutorial " by D. S. Sivia and J. Skilling. Bayesian analysis is a statistical approach with a long and rich history that allows us to use probability statements to quantify our uncertainty about specific parameters. This short book provides an excellent first introduction to this powerful family of techniques with practical examples. The book quickly guides us from the fundamental intuition behind Bayes theorem more advanced concepts and applications such as Model comparison, Inference and Non-Parametric Estimation.
