Data Science Weekly - Issue 653

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Data Science Weekly - Issue 653<br>Curated news, articles and jobs related to Data Science, AI, & Machine Learning<br>Data Science Weekly<br>May 28, 2026

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Issue #653<br>May 28, 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

The quadratic sandwich<br>If you have ever tried to minimize a function with gradient descent, you probably noticed that some functions are a joy to optimize and others are a nightmare. The difference often boils down to two properties: strong convexity and L-smoothness. These two concepts define a “sandwich” of quadratic bounds around your function that tells you exactly how well-behaved it is. If the sandwich is tight, life is good. If one slice of bread is missing, things get ugly fast…In this post we’ll build up both concepts from scratch, see how they combine into the quadratic sandwich, understand what happens at the level of the Hessian’s eigenvalues, and pick up a neat trick to verify L-smoothness without ever computing an eigenvalue…

What it takes to transpose a matrix<br>In this article we are going to gradually build a sequence of progressively more efficient implementations of matrix transpose, with the most sophisticated implementation being up to x25 times faster than the naive one. During each step we will locate the bottleneck, figure out what has caused it, and think of a solution to overcome it. This article is intended to serve as an introduction to optimizing matrix algorithms for x86_64, presented from the perspective of a real-world problem…

Why is Kullback-Leibler divergence not a distance?<br>The Kullback-Leibler divergence between two probability distributions is a measure of how different the two distributions are. It is sometimes called a distance, but it’s not a distance in the usual sense because it’s not symmetric. At first this asymmetry may seem like a bug, but it’s a feature. We’ll explain why it’s useful to measure the difference between two probability distributions in an asymmetric way. The Kullback-Leibler divergence between two random variables X and Y is defined as…

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Data Science Articles & Videos

Friday Pins vs Sunday Pins or: How to Illustrate Something Completely Obvious<br>In my previous article, I Spent the Last Month and a Half Building a Model that Visualizes Strategic Golf, I laid out the very basics of the golf model I built, the underlying reason that compelled me to work on it, and novel maps it could create. However, I barely scratched the surface of what this model can illustrate about golf course architecture. Here, I want to talk about how we can specifically look at one kind of dynamic architectural interest. That is, features of golf architecture that appear as we change the course setup. Specifically, I want to look at how different hole locations change the architectural interest for players, how we can show that, and what it looks like…

What DS job market trends are you seeing? [Reddit]<br>I have 20 YOE, but I do a generic “data science” search on LinkedIn every 3 months to see how the job market is trending. Here are my latest observations. I would love to hear what others think.<br>The number of AI postings is going down. ML and DE skills are back in fashion.

Salaries are down across the board.

Non-technical responsibility is up. I see “Data Scientist” roles being asked to create a roadmap and drive organizational change. That used to be the responsibility of the manager or maybe the lead.

I haven’t applied for any of these jobs, so I don’t know what’s actually real. I wonder if Data Science is no longer the hot keyword and I should be searching for something else…

Thoughts About the Roles of AI for Statistics<br>This talk covers what I’ve learned from using large language models in my work for the past two years. For statistical programming, success has come when I play the role of specification writer and comprehensive tester. For statistical methodology, AI has been successful serving me as a mathematical statistical assistant and a critic. Instead of avoiding AI we should embrace it, but we should always set a higher bar for the quality of our work as a result…

Evaluating detection & classifier algorithm accuracy<br>Let’s say i have images with a mixture of normal cells and sick cells on each image. Humans can reliably distinguish normal cells from sick cells, however it takes a lot of time to mark up the images as there are hundreds of cells in one field of view. I have an algorithm that can also distinguish normal and sick cells. The outputs from both manual markup and my algorithm is 2 lists of (x, y) coordinates -- one list for sick cells, one for healthy. What are the best...