Issue #44

Mar 29, 2020

Dear friends,

Welcome to the 44th edition of the Sunday Briefing.

This week we take a look at epidemic modeling strategies with a new blog post: Epidemic Modeling 101: Or why your CoVID19 exponential fits are wrong along with a GitHub repository with the respective Python code. We hope you find it useful and gladly welcome any comments you might have.

Further, we take a look at how to use Color in Data Visualization, how to best Visualize Neural Networks and how to make the best of the current wave of CoVID induced Remote Work.

On the academic front, we dip our toes into the Julia Language a promising new language designed from scratch for Data Science, how to use word embeddings for text networks and Stanza: A new Python NLP Toolkit.

Finally, in the video of the week, we have AlphaGo - The Movie and excellent documentary about Google's ground breaking Go computer program.

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

Blog:

Our latest blog post covers the fundamentals of Epidemic Modeling and how it can be used to understand the current CoVID-19 pandemic. 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:
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.

An Introduction to Python and Programming [github.com/webartifex]
Color in Data Visualization: Less How, More Why [towardsdatascience.com]
How to Make Remote Work Effective for Data Science Teams [www.comet.ml]
Predicting Dengue Spread Using Seasonal ARIMAX Model and Meteorological Data [towardsdatascience.com]
Automate These 3 (Boring!!) Excel Tasks with Python! [towardsdatascience.com]
SimPy: Simulating Real-World Processes With Python [realpython.com]
Visualizing Neural Networks with the Grand Tour [distill.pub]
Write Unbreakable Python [jessewarden.com]

Fresh off the press:

Some of the most interesting academic papers published recently.

Machine learning for cluster analysis of localization microscopy data (D. J. Williamson, G. L. Burn, S. Simoncelli, J. Griffié, R. Peters, D. M. Davis, D. M. Owen)
Human migration: the big data perspective (A. Sîrbu, G. Andrienko, N. Andrienko, C. Boldrini, M. Conti, et al)
With COVID-19, modeling takes on life and death importance (M. Enserink, K. Kupferschmidt)
Time series and machine learning to forecast the water quality from satellite data (M. R. A. Shehhi, A. Kaya)
FastDTW is approximate and Generally Slower than the Algorithm it Approximates (R. Wu, E. J. Keogh)
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues (K. Gao, J. Tu, Z. Huo, G. Mei, F. Piccialli, S. Cuomo)
Using word embeddings to improve the discriminability of co-occurrence text networks (L. V. C. Quispe, J. A. V. Tohalino, D. R. Amancio)
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages (P. Qi, Y. Zhang, Y. Zhang, J. Bolton, C. D. Manning)

Video of the week:

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

AlphaGo - The Movie

https://www.youtube.com/watch?v=WXuK6gekU1Y

Upcoming Events:

Opportunities to learn from us

Apr 8, 2020 - Time Series for Everyone [Register]
Apr 29, 2020 - Applied Probability Theory for Everyone [Register]

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Publishes on Sunday.

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