Data Science Weekly - Issue 659

Data Science Weekly Newsletter

SubscribeSign in

Data Science Weekly - Issue 659<br>Curated news, articles and jobs related to Data Science, AI, & Machine Learning<br>Data Science Weekly<br>Jul 10, 2026

Share

Issue #659<br>July 09, 2026

Hello!<br>Once a week, we write this email to share the links we thought were worth sharing in the Data Science, ML, AI, Data Visualization, and ML/Data Engineering worlds.

And now…let’s dive into some interesting links from this week.

Editor's Picks

A visual introduction to information theory<br>We present a visual, intuition-driven guide to key concepts in information theory. We show how entropy, mutual information, and channel capacity follow from basic probability, and how they determine the shortest possible encoding of a data source and the maximum rate of reliable communication through a noisy channel. Our presentation assumes only a familiarity with basic probability theory…

That’s weird! Anomaly detection using R<br>This book is about tools and techniques for finding and understanding anomalies. We will begin with some simple data sets containing only one variable, and build up slowly to much more complicated data. We will cover popular but inadvisable methods to identify anomalies (pointing out their shortcomings), as well as more reliable and recommended approaches….

Rethinking Validation for Spatial Machine Learning: Takeaways from the Talk<br>A summary of key points from my keynote and workshop at the Machine Learning for Earth Observation conference in Exeter (2026-06-22 and 2026-06-23)…

What’s on your mind

This Week’s Poll:

Loading...

Last Week’s Poll:

Data Science Articles & Videos

Benchmarking Coding Agents on Databricks’ Multi-Million Line Codebase<br>This article shares the results and methodology of the internal coding benchmark we built at Databricks, which evaluates tools on actual coding tasks our engineers performed on the Databricks codebase. Tasks featured edits against a multi-million line codebase covering many popular languages (Python, Go, Typescript, Scala, etc.) and both tasks and solutions were carefully reviewed to ensure accuracy. This isn’t meant to be comprehensive, but the exercise surfaced insights that have already made our engineering team meaningfully more efficient with coding agents. Below, you can see how models and harnesses scored on the overall benchmark…

How much do you actually trust AI output for real reporting? [Reddit]<br>Feels like everyone’s using ChatGPT/Copilot for something now, but I can’t tell whether it’s mostly “write me a first draft I’ll review” or something people are comfortable using for production reporting after proper validation. Anyone run into cases where AI-generated code looked completely reasonable but was subtly wrong? How much manual verification do you guys still do vs. just running with it?…

Matrix Multiplication on Blackwell: Part 1 - Introduction<br>In Part 1 (this blog post) we cover what a Matrix Multiplication (matmul) is, its importance for LLMs, and why we need to optimize it. Then we explain what a GPU is, GPU history since Ampere, and finally how to write a simple (not super performant) implementation of matmul on a GPU in 4 lines of Mojo. In part 2, we’ll explain the hardware instructions introduced in Blackwell GPUs, and continue improve on our kernels’ performance to make it leverage the new hardware instructions. As we continue through the blog series, we will incrementally leverage new Blackwell features to improve our matmul implementation until the end of the series where we achieve performance that surpasses that of NVIDIA’s cuBLAS library…

Optimal Transport for Actuarial Science<br>These lecture notes introduce optimal transport as a mathematical language for actuarial science. They treat losses, premiums, scores, reserves, capital scenarios, climate losses and lifetime distributions as probability measures that can be compared, transported, averaged, stressed and interpolated. The first part develops the main tools: couplings, push-forwards, discrete and continuous Kantorovich problems, duality, Wasserstein distances, quantile transport, barycenters, entropic regularization and statistical optimal transport. The second part applies these tools to risk measures, Wasserstein robustness, pricing and capital, portfolio drift, reserving cash-flow distributions, climate-prevention diagnostics, reinsurance, dependence uncertainty, capital allocation, distributional fairness diagnostics and longevity risk…

Does additional data always reduce posterior variance?<br>Additional data does not always decrease the size of a confidence interval. This post will look at this from a Bayesian perspective. In general, new information reduces your uncertainty regarding whatever you’re estimating. The posterior distribution becomes more concentrated as more data are collected. That’s what happens “in general” but does it necessarily happen every time you get new data? Conceivably if you...