| | Most data workflows don’t fail in analysis. They fail in production. This free guide looks at how teams design, scale, and monitor Airflow pipelines so models, reports, and features keep running reliably instead of quietly breaking downstream.
*Message from this week's sponsor, Astronomer |
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| | | Here’s a practical post for optimizing your Positron (or VS Code) setup. Real settings, real extensions, and small UI tweaks that genuinely improve focus, readability, and everyday data science work, all easy to copy and paste into your own settings.
Emil Hvitfeldt |
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| | What happens when you give an LLM nearly a decade of your own health data? This thoughtful case study walks through labeling messy real-world data, engineering personal-baseline features, and why a small gradient-boosted model beat more complex approaches for early detection.
Ian Rowan |
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| | This is less about regression theory and more about doing regression well in R. It's a step-by-step example that emphasizes interpretation, model comparison, and modern tooling; ideal for instructors, analysts, and anyone refining their everyday modeling workflow.
Andrew Heiss |
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| | | | pandas 3.0 just landed and it finally fixes some long-standing pain points. This post explains what’s new, what improved, and what didn’t, including safer dataframe mutation, better pipelines, and where pandas still trails Polars.
Marc Garcia |
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| | This site highlights the R packages that turn LLMs into real tools: structured prompts, model routing, IDE assistants, local models for privacy, plus agent and MCP plumbing so models can actually use your R session.
Luis D. Verde Arregoitia |
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| | | Deep learning isn’t “new” anymore, but it’s been changing fast. This newly updated, free-to-read edition walks from first principles to transformers and diffusion, with runnable code and minimal math. A great reset for people who work with data and want a clear, hands-on mental model of modern deep learning.
François Chollet and Matthew Watson |
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| | Ever wonder whether your model is answering a causal question or just correlating stuff? This graduate-level course walks through counterfactuals, identification, and classic causal designs, with problem sets in R. Lectures and problem sets will be posted on this site throughout the semester.
University of Wisconsin-Madison | Anton Strezhnev |
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| | | Why does every “small change” in data turn into a week-long project? This post argues that we’ve been solving the same data problems for decades, just with better marketing. This is a sharp, experience-driven take on fundamentals, modeling, and why the loop never really changes.
Simon Späti |
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| | | This week's issue sponsored by: | | |
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