Welcome to the 287th edition of the Data Science Briefing! This week, we continue celebrating the 6th anniversary of this humble newsletter!
We're proud to announce that a brand new Data Visualization with Python on-demand video is now available on the O'Reilly website: Python Data Visualization: Create impactful visuals, animations and dashboards. This in depth tutorial is almost 7h in length and covers fundamental and advanced usage of matplotlib, seaborn, plotly and bokeh as well as tips on how to use Jupyter widgets. Check it out!
The latest blog post on the Epidemiology series is also out:
Demographic Processes. In this post we explore how to include birth and death rates in your epidemik models. Check it out!
This week's newsletter offers a diverse look into the evolving landscape of data science and machine learning, from practical storage solutions to cutting-edge research and development. Anthropic’s teams are leveraging Claude Code, their agentic coding tool, to accelerate workflows across disciplines: from data scientists efficiently navigating massive codebases and visualizing data without needing to program in JavaScript, to marketers automating the generation of hundreds of ad variations within minutes. This showcases how AI is breaking down barriers between technical and non-technical work, enabling teams to prototype and automate complex processes with ease.
On the infrastructure side, an innovative approach to bulk storage describes how using Linux’s Logical Volume Manager (LVM) to cache frequently accessed data on SSDs, while maintaining HDDs for larger, slower storage, can blend speed and cost-effectiveness for large projects and local AI workloads. And as AI’s reach expands, new research reveals a dramatic effect on web traffic: Google's AI Overviews, now a regular part of search results, have been shown to nearly halve click-through rates to external websites, signaling a significant shift in how users access information and raising fresh questions about the future of digital content and web traffic.
Recent academic and industry research offers remarkable insights into the frontiers of AI, public health, and neural architectures. An ambitious, data-driven study mapping three decades of urban life shows how AI and computer vision are transforming our understanding of public spaces, revealing faster pedestrian movement and shorter gatherings in major U.S. cities. Meanwhile, a systematic review of mathematical models from SARS outbreaks compiles hundreds of parameters and risk factors, painting a clear picture of the disease’s high fatality, transmissibility, and its propensity for superspreading, a resource designed to inform future pandemic responses. At the edge of neural networks, new work introduces curved statistical manifolds to model “explosive” phase transitions and enhanced memory capacity, pushing classical associative memory frameworks into novel, non-linear territory.
Also making waves: a provocative analysis on large language models (LLMs) challenges their Bayesian credentials, showing that while they approximate Bayesian behavior on average, they systematically violate key theoretical properties in individual cases. Finally, a new Hierarchical Reasoning Model, inspired by multi-timescale processing in the brain, is outperforming much larger models on reasoning tasks, renewing hopes for architectures that enable general-purpose, robust problem solving. These advances collectively point to an AI and data science landscape that is rapidly growing in both sophistication and cross-disciplinary impact.
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Semper discentes,
The D4S Team
"Behavioral Network Science: Language, Mind, and Society" by T. T. Hills successfully bridges two distinct scientific domains, demonstrating how network analysis can reveal hidden patterns in human behavior. The book tackles an impressive scope of topics, from language evolution and childhood learning to cognitive aging, creativity, and social dynamics, while maintaining remarkable coherence throughout. What sets this work apart is Hills' commitment to practical application, equipping readers with concrete tools, including an introductory guide to network science and accompanying R code that enables hands-on analysis.
This practical approach makes the book uniquely valuable to a diverse audience. Behavioral scientists unfamiliar with network methods will find an accessible entry point, while data scientists can discover rich applications in behavioral research. Hills demonstrates particular skill in addressing contemporary social issues through a network lens, offering fresh perspectives on polarization, echo chambers, and conspiracy theories. The interdisciplinary framework proves especially powerful when examining how individual cognitive processes scale up to shape collective behavior and social structures.
The book's most significant achievement lies in its clarity without oversimplification. Hills effectively conveys complex concepts with precision while maintaining an engaging and accessible tone. This balance makes "Behavioral Network Science" essential reading for anyone seeking to understand how network structures influence human behavior across scales—from individual minds to entire societies.
(view in browser) May 6th Next webinar: May 27, 2026 - Code Development with AI Assistants [Register] Dear Reader, Announcements Ever wonder how we can turn thousands of unstructured news articles into structured, actionable insights? In the latest post from Data4Sci, we dive into the fascinating process of transforming raw text from news articles into interconnected networks of information. If you're interested in Natural Language Processing (NLP), entity extraction, and how to connect the...
(view in browser) Apr 30th Next webinar: May 6, 2026 - Automate the Boring Developer Stuff with LLMs [Register] Dear Reader, Announcements ✈️ Mapping the skies: How do we visualize airline traffic between states? We often think of air travel in terms of airports, but viewing it as a network of state-to-state connections reveals fascinating patterns in how our country moves. Our latest substack uses data visualization to turn raw statistics into a clear story about infrastructure and mobility....
(view in browser) Apr 22nd Next webinar: Apr 29, 2026 - Claude API for Python Developers [Register] Dear Reader, Announcements ✈️ Mapping the skies: How do we visualize airline traffic between states? We often think of air travel in terms of airports, but viewing it as a network of state-to-state connections reveals fascinating patterns in how our country moves. Our latest substack uses data visualization to turn raw statistics into a clear story about infrastructure and mobility. 👉 Airline...
