D4S Sunday Briefing #110


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ISSUE #110

D4S Sunday Briefing #110

A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.

Jul 4, 2021​

Dear friends,

Welcome to the July 4th issue of the Sunday Briefing.

This week we’re happy to announce the second post of “Visualization for Science” substack: Time Series State Map so check it out and don’t forget to Subscribe to V4Sci so you never miss a post!

You can also checkout the latest post at G4Sci: Network Motifs: Frequent patterns in Graphs where we introduce the ESU algorithm for exhaustive enumeration of all subgraphs of a given size. 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.

Our next webinar on Applied Probability Theory For Everyone where we guide you from the fundamental definition of probability all the way through to applications of Bayesian Statistics is coming up this Friday, Jul 9th. There’s still some spots left, so you can still Register!

This week we consider how the Universal Approximation Theorem can help us understand neural networks, how we can use sqlite3 as a notekeeping document graph and if Facebook’s “Prophet” is the Time-Series Messiah, or Just a Very Naughty Boy?

From the halls of academia, we have a tutorial introduction on Information Theory, some thoughts on measuring algorithmically infused societies and an introduction to PathQuery, Google’s Graph Query Language.

Finally, this weeks ‘Data Science Book’ highlight is “Think Bayes (2nd Ed)” by Allen Downey and, as always you can find all the previous book recommendations on our website. In the video of the week we have a lecture on Causal Inference by David Sontag.

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


The second post on the Visualization for Data Science substack: Time Series State Map 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: Network Motifs: Frequent patterns in Graphs is now out.You should Sign Up to make sure you never miss a post!

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

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

Data Science Book:​

This weeks Data Science Book is “Think Bayes (2nd Ed)” by Allen B. Downey. While Bayesian Statistics is a powerful tool in the toolbox of any Data Scientists, it is not the easiest of skills to learn if you are not mathematically inclined. In this book, Downey uses his down to earth, step by step style to make you proficient in the world of Bayesian Statistics by leveraging your pre-existing knowledge of Python instead of relying excessively on mathematical notation as most other books do. The book comes with a complete up-to-date GitHub repository so that you can more easily work your way through the example and cement your understanding of this important topic.

(affiliate link)

Top Links:

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

  1. John Urschel: From NFL Player to Mathematician [quantamagazine.org]
  2. You Don’t Understand Neural Networks Until You Understand the Universal Approximation Theorem [medium.com/analytics-vidhya]
  3. Offline Policy Evaluation: Run fewer, better A/B tests [edoconti.medium.com]
  4. Using sqlite3 as a notekeeping document graph with automatic reference indexing [epilys.github.io/bibliothecula]
  5. Make Patterns Pop Out of Heatmaps with Seriation [nicolas.kruchten.com]
  6. Is Facebook’s “Prophet” the Time-Series Messiah, or Just a Very Naughty Boy? [microprediction.com]
  7. Double Machine Learning for causal inference [towardsdatascience.com]

Fresh From The Press:

Some of the most interesting academic papers published recently

Video of the Week:

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

Causal Inference

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

Upcoming Events

Opportunities to learn from us:

  1. Jul 9, 2021 — Applied Probability Theory For Everyone [Register]
  2. Jul 26, 2021 — Transforming Excel Analysis into Python and pandas Data Models [Register]
  3. Aug 9, 2021 — Graphs and Network Algorithms for Everyone [Register] 🆕
  4. Aug 30, 2021 — Why and What If — Causal Analysis for Everyone [Register] 🆕

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Publishes on Sunday.​

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