A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Welcome to the slightly belated 148th edition of the Sunday Briefing.
This week we have a new G4Sci post: Bipartite Graphs 101 where we start our exploration of this important class of graphs. In the Visualization for Science substack we have “3D Surface Plot: The US population distribution”, while on Medium we have a recap of the Top 10 Books we read in 2021.
On our regularly scheduled content we have a deep dive on Churn — How it works operationally and ways to calculate it, the DARPA Perspective on Artificial Intelligence and how Pamplona Bull Runs Reveal Dynamics of Crowds in Danger.
From the Ivory Tower we have a look at Link Prediction in Time Varying Social Networks, the Dynamics of ranking and Analyzing EU-15 immigrants’ language acquisition using Twitter data.
This weeks ‘Data Science Book’ highlight is Data Science Book is “Causality” by J. Pearl. As always you can find all the previous book recommendations on our website. In the video of the week we have a lecture on The Craft of Writing Effectively, a topic that rarely gets the attention it deserves.
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The D4S Team
The latest post in the CoVID-19 series, ‘How to model the effects of vaccination’ takes a look at how simple modifications of the SIR model can help us better understand how vaccines work. As usual, all the code is available in GitHub: http://github.com/DataForScience/Epidemiology101
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
This weeks Data Science Book is “Causality” by J. Pearl. Causal Inference is a lively and fast developing area in Data Science that we believe has the potential to be truly revolutionary in coming years (you can get a quick overview of the main ideas in our Causal Inference series over at Medium). Judea Pearl is one of the most prominent founding fathers of this field that he introduces masterfully in this textbook. While the approach Pearl chooses is mathematically rigorous, thanks to his rich use of toy examples, the key ideas and concepts are easily grasped and adapted to real world datasets. Causal Inference is a powerful arrow in any Data Scientist’s quiver and this is the ideal starting point if you’re interested in taking the first steps in this exciting area.
Tutorials and blog posts that came across our desk this week.
- Churn — How it works operationally and ways to calculate it [causal.app]
- Varieties of mathematical understanding [ams.org]
- In Praise of Memorization [pearlleff.com]
- A DARPA Perspective on Artificial Intelligence [darpa.mil]
- Dennis Sullivan, Uniter of Topology and Chaos, Wins the Abel Prize [quantamagazine.org]
- Word2Vec Explained [towardsdatascience.com]
- Machine Learning System Automatically Translates Long-Lost Languages [cacm.acm.org]
- The counter-intuitive rise of Python in scientific computing [cerfacs.fr]
- Pamplona Bull Runs Reveal Dynamics of Crowds in Danger [scientificamerican.com]
Some of the most interesting academic papers published recently
- Dynamics of ranking (G. Iñiguez, C. Pineda, C. Gershenson, A.-L. Barabási)
- Large deviations of a susceptible-infected-recovered model around the epidemic threshold (Y. Feld, A. K. Hartmann)
- A Highly-Available Move Operation for Replicated Trees (M. Kleppmann, D. P. Mulligan, V. B. F. Gomes, A. R. Beresford)
- Link Prediction in Time Varying Social Networks (V. Carchiolo, C. Cavallo, M. Grassia, M. Malgeri, G. Mangioni)
- Mathematical analysis of a hybrid model: Impacts of individual behaviors on the spreading of an epidemic (G. Cantin, C. J. Silva, A. Banos)
- Bayesian inference in Epidemics: linear noise analysis (S. Bronstein, S. Engblom, R. Marin)
- Analyzing EU-15 immigrants’ language acquisition using Twitter data (S. Gil-Clavel, A. Grow, M. J. Bijlsma)
Interesting discussions, ideas or tutorials that came across our desk.
The Craft of Writing Effectively
All the videos of the week are now available in our Youtube playlist.
Opportunities to learn from us:
- Apr 20, 2022 — Natural Language Processing (NLP) for Everyone [Register]
- Apr 27, 2022 — NLP with Deep Learning for Everyone [Register]
- May 06, 2022 — Applied Probability Theory for Everyone [Register] 🆕
- May 20, 2022 — Transforming Excel Analysis into Python and pandas Data Models [Register] 🆕
Long form tutorials:
- Natural Language Processing 5.5h, covering basic and advancing techniques using NLTK and Keras
- Times Series Analysis for Everyone 6h covering data pre-processing, visualization, ARIMA, ARCH and Deep Learning models
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Publishes on Sunday.