Data Science Weekly - Issue 661

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Data Science Weekly - Issue 661<br>Curated news, articles and jobs related to Data Science, AI, & Machine Learning<br>Data Science Weekly<br>Jul 23, 2026

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Issue #661<br>July 23, 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

Why non-invasive glucose monitoring is hard<br>Continuous glucose monitoring on Apple Watch and other smartwatches has been “5-7 years away” for roughly a decade…I trained one of the first deep neural networks to detect signs of diabetes with consumer health sensors. This post will explain what makes glucose sensing so hard, what hardware and machine learning techniques have been tried, and try to describe which research techniques are actually feasible on a consumer device like an Apple Watch, Pixel, Oura, or Samsung Watch. Let’s start by explaining why an already-launched feature, blood oxygen sensing, actually works in practice using relatively cheap optical sensors…

Navigating Challenges in Spatial Machine Learning<br>Spatial machine learning has become a standard tool for producing environmental and geographic prediction maps. It is now relatively (technically) easy to combine field observations with remote sensing, climate, terrain, or other predictor layers and fit a strong machine learning model. The harder question is whether the resulting map is reliable, transferable, and reproducible…Spatial dependence, clustered and biased sampling, heterogeneous landscapes, and domain transfer all affect how models should be evaluated and interpreted. A model can appear accurate under a standard validation approach and still be unreliable where predictions are needed…

Exploring Gymflation with AI<br>You might be familiar with super hero inflation? Over time, super hero physiques on screen have become increasingly exaggerated. Batman’s progression from Adam West to Ben Affleck is a great example of this…Gymflation is much the same idea. As gym culture has skyrocketed, it seems like so too have people’s feats of strength - as recorded on social media. Going on YouTube or Instagram one gets the feeling that a 200kg deadlift is really rather average nowadays. But is it really true? Or are we just feeling the effects of the Algorithm, pushing extreme examples in our blue-lit faces? What follows is a little project to find out…

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Data Science Articles & Videos

Easy A’s, Less Pay: The Long-Term Effects of Grade Inflation<br>Average grades continue to rise in the United States, raising the question of how grade inflation impacts students. We provide comprehensive evidence on how teacher grading practices affect students' long-run success. Using administrative high school data from Los Angeles and from Maryland that is linked to postsecondary and earnings records, we develop and validate two teacher-level measures of grade inflation: one measuring average grade inflation and another measuring a teacher's propensity to give a passing grade…The cumulative impact is economically significant: a teacher with one standard deviation higher average grade inflation reduces the present discounted value of lifetime earnings of their students by $213,872 per year…

What Do Today’s Data Science Graduates Commonly Lack? [Reddit]<br>I often read comments from hiring managers and interviewers saying they’re disappointed with recent data science graduates. I’m curious, what do you think these graduates are lacking? If someone wants to become a data scientist, what skills should they focus on? Strong software engineering skills? Math and statistics? Something else?…

How Hollywood Stopped Making Movies in Hollywood<br>The economics behind where movies are filmed, why Hollywood left Los Angeles, and whether audiences can tell the difference…

The Unreasonable Difficulty of Time Series Forecasting<br>I’ve been thinking recently about what makes time series forecasting problems so difficult compared to other sequence learning tasks or IID Machine Learning problems…for many ML problems, we don’t have full information about the casual factors either yet are able to do something reasonable, or at least better than a random walk. Hence, this post investigates the nature of the forecasting problem and what makes it so much more difficult than classical machine learning…

Holding the LLM Stack in Your Head<br>A dependency-ordered walk through the modern LLM stack, from the linear algebra under a single attention head, through training and inference, out to agent protocols shipping in 2026. Ten arcs, eighty-odd posts. The goal isn't rigor, it's intuition that survives contact with real systems…

How to Bake a [Golf Shot] Dispersion Pattern from Scratch<br>I’ve built a realistic golf shot...