Data Science Weekly – Issue 661

9 1 Share Issue #661 July 23, 2026 Hello!

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.

Why non-invasive glucose monitoring is hard 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 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 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…

Easy A’s, Less Pay: The Long-Term Effects of Grade Inflation 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…