Issue #204

June 4, 2023

This week’s Data Science Book, " Network Science with Python ", by D. Knickerbocker is a highly recommended book for anyone interested in network analysis. It provides a comprehensive and accessible introduction to the topic. The book's linear progression and friendly tone make it highly engaging and easy to follow. The author's contagious enthusiasm and practical examples effectively communicate the power and importance of network analysis. The book covers various domains, including language and social media data mining, and explores the relationship between NLP and networks, an approach similar to our very own Graphs for Data Science substack. It emphasizes the value of actionable insights in the conversational AI domain and provides historical context and real-world use cases for NLP solutions. The book also introduces the Python packages used and dives into network science using the NetworkX library. It demonstrates how graphs can be used in machine learning and covers important concepts like betweenness centrality, page rank, and community detection with real-world applications. Overall, "Network Science with Python" is a well-written and comprehensive guide that offers practical insights and is suitable for readers of all levels.

Network Science with Python

1. Using graphs to model and analyze the customer journey [medium.com/data-science-at-microsoft]
2. All the Hard Stuff Nobody Talks About when Building Products with LLMs [honeycomb.io]
3. A Data-Centric Introduction to Computing [dcic-world.org]
4. What's the big deal with Generative AI? Is it the future or the present? [txt.cohere.com]
5. Most Important Papers for Quantitative Traders [qmr.ai]
6. Stop Eliminating Perfectly Good Candidates by Asking Them the Wrong Questions [hbr.org]
7. Beyond the Pipelines: The Hidden Duties of Data Engineers [medium.com/data-engineer-things]

• The effects of cash transfers on adult and child mortality in low- and middle-income countries (A. Richterman, C. Millien, E. F. Bair, G. Jerome, J. C. D. Suffrin, J. R. Behrman, H. Thirumurthy)
• Trust within human-machine collectives depends on the perceived consensus about cooperative norms (K. Makovi, A. Sargsyan, W. Li, J.-F. Bonnefon,T. Rahwan)
• Why Are There Six Degrees of Separation in a Social Network? (I. Samoylenko, D. Aleja, E. Primo, K. Alfaro-Bittner, E. Vasilyeva, K. Kovalenko, D. Musatov, A. M. Raigorodskii, R. Criado, M. Romance, D. Papo, M. Perc, B. Barzel, S. Boccaletti)
• How Much Does Education Improve Intelligence? A Meta-Analysis (S. J. Ritchie, E. M. Tucker-Drob)
• Incorporating signals into optimal trading (C.-A. Lehalle, E. Neuman)
• A Brief Introduction to Machine Learning for Engineers (O. Simeone)
• A First Course in Causal Inference (P. Ding)

Message Passing Algorithms for Network Scheduling

June 4, 2023

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