Issue #203
May 29, 2023
Dear friends,
Welcome to the Memorial Day Weekend edition of the Sunday Briefing. This week we continue our hiatus from blogging but we have several posts in the final stages of writing. In the meantime, you can catch up on the latest post in the Viz4Sci series: Waterfall Chart. The latest Medium post “Top 10 Books we read in 2022” and the most recent post on the G4Sci series: Network Attacks: Breaking up a network without observing it completely.
This week we are proud to announce the next edition of the Natural Language Processing for Everyone webinar, coming up on Jul 18th. Register now so you don’t miss your spot!
On our regularly scheduled content, we learn about Tracing Python, How to Access the Fantasy Premier League API, Build a Dataframe, and Analyze Using Jupyter, Python, and Pandas and dive into an in depth tutorial on Principal Components Analysis.
On the academic front, we have causal evidence that herpes zoster vaccination prevents a proportion of dementia cases, a positive statistical benchmark to assess network agreement and a look at how assortative and preferential attachment lead to core-periphery networks.
Our Data Science Book is “Fluent Python” by L. Ramalho. As always you can find all the previous book recommendations on our website. In the video of the week, we have a look at Network Science: From Abstract to Physical Networks.
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Semper discentes,
The D4S Team
This week’s Data Science Book is " Fluent Python " by L. Ramalho, which is, in my opinion, the best book on Python programming available as it teaches readers to truly understand how Python works and how to utilize it effectively. This is a book for those who want a comprehensive and in-depth understanding of the language, covering advanced topics such as metaprogramming with "dunder()" methods without getting lost in the weeds. It covers exactly what you need to know, without overwhelming you with unnecessary information. "Fluent Python" is a must-have for any serious Python programmer as even after almost twenty years of working with Python, I continue to learn new things with each chapter. The second edition weighs in at over 1,000 pages for an in depth, comprehensive cover, of everything you may ever need to know about Python.
- 1. Tracing Python [blog.koehntopp.info]
- 2. A tutorial on Principal Components Analysis [cs.otago.ac.nz]
- 3. Our model suggests that global deaths remain 5% above pre-covid forecasts [economist.com]
- 4. Finetuning LLMs Efficiently with Adapters [magazine.sebastianraschka.com]
- 5. How to Access the Fantasy Premier League API, Build a Dataframe, and Analyze Using Jupyter, Python, and Pandas [towardsdatascience.com]
- 6. More Than Just Algorithms [queue.acm.org]
- 7. Deep Neural Networks As Computational Graphs [medium.com/tebs-lab]
- • Causal evidence that herpes zoster vaccination prevents a proportion of dementia cases (M. Eyting, M. Xie, S. Heß, S. Heß, P. Geldsetzer)
- • A positive statistical benchmark to assess network agreement (B. Hao, I. A. Kovács)
- • Hierarchical community structure in network
- • Epidemic control in networks with cliques (L. D. Valdez, L. Vassallo, L. A. Braunstein)
- • A PhD Student's Perspective on Research in NLP in the Era of Very Large Language Models (O. Ignat, Z. Jin, A. Abzaliev, L. Biester, S. Castro, N. Deng, X. Gao, A. Gunal, J. He, A. Kazemi, et al)
- • Urban Dynamics Through the Lens of Human Mobility (Y. Xu, L. E. Olmos, D. Mateo, A. Hernando, X. Yang, M. C. Gonzalez)
- • Assortative and preferential attachment lead to core-periphery networks (J. Ureña-Carrion, F. Karimi, G. Iñiguez, M. Kivelä)
Network Science: From Abstract to Physical Networks
All our videos are also available in our
YouTube playlist.
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