Issue #89

Feb 07, 2021

Dear Reader,
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Welcome to the 89th issue of the Sunday Briefing.

This week we're on hiatus from blogging. While we work on the next posts, you have some extra time to catch up on our most recent post over at Graphs For Data Science: Understanding DBSCAN and K-NN with Random Geometric Graphs where we explore the connections between Random Geometric Graphs and the K-Nearest Neighbor and DBSCAN machine learning algorithms. You should Subscribe to G4Sci to make sure you never miss a post!

You can catch up on our recent posts on Competing CoVID-19 Strains and Inverse Probability Weighing and as always you can find the code in the Epidemiology and Causality GitHub repos, respectively.

This week we explore the mathematics of shuffling, naked shorting, and some of the Hard Lessons of Modeling the Coronavirus Pandemic requiring a National Pandemic Prediction Agency.

From the Ivory Tower we have a look at Entropic Dynamics of Networks, an overview of Deep Learning through the eyes of a statistitian and, finally, an evolutionary view on the emergence of Artificial Intelligence.

Finally, this weeks 'Data Science Book' highlight is Allen B. Downey's Think Stats and in the video of the week, we have a tutorial on Web Scraping Using Scrapy.

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!

Semper discentes,

The D4S Team

Blog:

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The latest post on the Graphs for Data Science substack: Understanding DBSCAN and K-NN with Random Geometric Graphs is now out. In this post we use Random Geometric Graphs to explore classification and clustering algorithms. You should Sign Up to make sure you never miss a post!

The latest post in the CoVID-19 series, 'Competing CoVID-19 Strains' takes a look at the likely impact that the introduction of a more virulent strain might have in the course of the pandemic. As usual, all the code is available in GitHub: http://github.com/DataForScience/Epidemiology101​

The latest post in the Causality series covers section '3.6 - Inverse Probability Weighing', a recipe to calculate the effect of covariate stratified interventions. The code for each blog post in this series is hosted by a dedicated GitHub repository: https://github.com/DataForScience/Causality​

Data Science Book:
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This weeks Data Science Book is "Think Stats" by Allen B. Downey. Despite the natural trend to be star struck with latest models and algorithms, the truth is that most of the work of a Data Scientists is must more basic and fundamental. Having a solid basis in traditional statistics is paramount to a successful data science career. Think Stats takes an empirical approach that helps you completely and truly grok the concepts as they are being introduced. Using Python examples and detailed explanations Downey guides the reader through important conceptual tools like Probability Distributions, Hypothesis Testing, Regression and Survival Analysis that should be part of every practitioners toolkit,

Top Links:

Tutorials and blog posts that came across our desk this week.

1. ​It’s Time for a National Pandemic Prediction Agency [wired.com]
2. ​How to write readable code [jeremymikkola.com]
3. ​Machine Learning, Kolmogorov Complexity, and Squishy Bunnies [theorangeduck.com]
4. ​The mathematics of shuffling [plus.maths.org]
5. ​Naked shorting: The curious incident of the shares that didn't exist [euromoney.com]
6. ​How to Increase Your Luck Surface Area [codusoperandi.com]
7. ​The Hard Lessons of Modeling the Coronavirus Pandemic [quantamagazine.org]

Fresh From The Press:

Some of the most interesting academic papers published recently

• ​Superspreading genomes (S. Alizon)
• ​Tracing contacts, finding ourselves (N. Naseer, K. Strelau)
• ​Language left behind on social media exposes the emotional and cognitive costs of a romantic breakup (S. Seraj, K. G. Blackburn, J. W. Pennebaker)
• ​Entropic Dynamics of Networks (F. X. Costa, P. Pessoa)
• ​Modeling how social network algorithms can influence opinion polarization (H. F. de Arruda, F. M. Cardoso, G. F. de Arruda, A. R. Hernández, L. da F. Costa, Y. Moreno)
• ​tf.data: A Machine Learning Data Processing Framework (D. G. Murray, J. Simsa, A. Klimovic, I. Indyk)
• ​An evolutionary view on the emergence of Artificial Intelligence (M. E. Leusin, B. Jindra, D. S. Hain)
• ​A Statistician Teaches Deep Learning (G. J. Babu, D. Banks, H. Cho, D. Han, H. Sang, S. Wang)
• ​Embodied Intelligence via Learning and Evolution (A. Gupta, S. Savarese, S. Ganguli, L. Fei-Fei)

Video of the Week:

Interesting discussions, ideas or tutorials that came across our desk.

Web Scraping Using Scrapy Tutorial For Beginners: Learn Scrapy From Scratch

All the videos of the week are now available in our Youtube playlist.