D4S Sunday Briefing #53

Original article was published on Deep Learning on Medium

Issue #53

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

May 31, 2020

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.

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 and help us spread the word!

Semper discentes,

The D4S team


Our latest blog post in the CoVID-19 series, ‘Visualizing the spread of CoVID-19’ takes a detailed look at the current state of the pandemic and how various informative visualizations can be made with publicly available data. GitHub: github.com/DataForScience/Epidemiology101

The latest post in the Causality series covers the first part of section 1.3 Probability Theory and Statistics, an overview of some of the fundamental theoretical requirements for the journey ahead. The code for each blog post in this series is hosted by a dedicated GitHub repository for this project: github.com/DataForScience/Causality

Blog Posts:
Epidemic Modeling:
CoVID-19: Everything you need to know
Visualizing the spread of CoVID-19

Epidemic Modeling 101: Or why your CoVID-19 exponential fits are wrong
Epidemic Modeling 102: All CoVID-19 models are wrong, but some are useful
Epidemic Modeling 103: Adding confidence intervals and stochastic effects to your CoVID-19 Models
Epidemic Modeling 104: Impact of Seasonal effects on CoVID-19

GitHub: github.com/DataForScience/Epidemiology101

1.2 — Simpson’s Paradox
1.3 — Probability Theory and Statistics

GitHub: github.com/DataForScience/Causality

Top Links:

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

  1. Causality for Machine Learning [fastforwardlabs.com]
  2. A beginner’s guide to web scraping with Python [opensource.com]
  3. The Ultimate Guide to Linear Regression [learningwithdata.com]
  4. Practical Estimation of Missing Values Using Conditional Expectation [towardsdatascience.com]
  5. Visualizing Science: How Color Determines What We See [eos.org]
  6. How to apply Reinforcement Learning to real life planning problems [medium.com/free-code-camp]
  7. Detecting the Fault Line Using Principal Component Analysis (PCA) [medium.com/@ns2586]

Fresh off 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.

NumPy Linear Algebra

Upcoming Events:

Opportunities to learn from us

  1. Jun 1, 2020 — Data Visualization with matplotlib and seaborn for Everyone [SOLD OUT]
  2. Jun 17, 2020 — Deep Learning for Everyone [Register] 🆕
  3. Jul 29, 2020 — Time Series for Everyone [Register] 🆕

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