Data Science Weekly - Issue 660
Curated news, articles and jobs related to Data Science, AI, & Machine Learning
Issue #660
July 17, 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.
Editor's Picks
How do you visualize SQL in your head? [Reddit]
I'm a software engineer that currently helping to build data team, so I work a lot as an "analyst" and build a bunch of data models…During code review, I’m hardly able to tell potential bugs (like joining using wrong key, potential row explosion, etc) at a glance, unlike when reviewing eg: python code. Atm, I’m 50:50 asking claude to generate me a simple viz that could help me trace what happens in each cte transformation. Not always useful, but it slightly helps me. How do you personally tackle this? Do you have an easier mental model that you want to share?…
Football DataPortraits
It's an impression, but one built entirely from data. Nothing is staged: each match is reconstructed from roughly 1,500 recorded events — every touch, pass, shot and card…ARIMA Is Boring, and That Is Why I Still Like It
ARIMA is old, limited, and a little annoying. I still like it because it makes the assumptions and uncertainty in a forecast harder to ignore…
What’s on your mind
This Week’s Poll:
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Last Week’s Poll:
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Data Science Articles & Videos
The LLM Critics Are Right. I Use LLMs Anyway.
This week I was at Local-First Conf in Berlin, and the dissonance was everywhere…I spoke at that conference myself, and when I later talked to some of the people, they described the feeling as pretty similar to mine, which is a relief, because I know I am not alone with this…So this article is me trying to describe it. I’ll start by going through all of the fair and valid concerns about using LLMs, the things that would get the big round of applause. Then I will explain what makes me still use LLMs. And I’ll finish up with some of the patterns I found, in the hope that by giving concrete examples, others can step in as well and describe their experiences, so we can all come together and get a better understanding of this dissonance….Mid-sized company
Cloud-native
Batch + streaming
SQL-heavy analytics
Some ML workloads…
Enterprise Haskell at H-E-B
When I joined H-E-B back in 2018, I was entering a world far removed from any of my previous roles in technology. H-E-B is a retail company first, and the largest privately held company in Texas. Our customers care about full shelves, not the systems behind them, and for decades those systems delivered: rock-solid COBOL mainframes doing their job reliably, day in and day out…The Little Book of Reinforcement Learning
This book is a short introduction to Reinforcement Learning, from the basics to applied algorithms…From Peer Review to Mentorship: My rOpenSci Story
I first came to rOpenSci in 2022, though at the time I barely knew what it was. I was getting a statistical package of mine ready to submit to the Journal of Statistical Software, and that is how I was pointed toward rOpenSci review: the journal directs authors to rOpenSci’s statistical software standards, so going through the review looked like a convenient step along the way. At the time, my focus was on polishing the software for the journal submission, not on rOpenSci itself…
What is a Smith Chart?
An interactive introduction to the chart at the heart of RF engineering…Latent Thought Flows with Text Compression
For images, audio, video, and actions, modern generative modeling increasingly shares one recipe: compress the signal into continuous latent tokens, train a generator to map noise to those tokens, and decode them back into the original domain. Language has been the exception…Our core message is simple: text can be compressed into a short sequence of continuous latents that remains useful for generation. This lets language share the same latent-generative recipe as the other modalities, while a text decoder handles surface realization…A History of Large Language Models
I trace an academic history of some of the core ideas behind large language models, such as distributed representations, transducers, attention, the transformer, and generative pre-training…A Bayesian framework for longitudinal EHR and genetic discovery
Electronic health records (EHRs) provide rich longitudinal disease histories, but existing methods for analyzing these data typically treat diseases in isolation and rarely integrate germline genetics. Here we present ALADYNOULLI, a Bayesian generative framework that jointly models longitudinal EHR diagnoses, age and polygenic risk to recover latent time-varying disease signatures and patient-specific signature loadings…
A ranked-choice election in Maine, USA: Using voting data to understand preferences
Maine uses ranked choice voting (RCV) in primaries and federal elections, so voters could rank up to six choices for Governor. Using the instant runoff algorithm, candidates were sequentially dropped based on who had the fewest first-choice votes, and ballots were reallocated to each voter’s next-ranked choice…But analyses of the individual ballots cast in the primary reveal a surprising mathematical fact: though she was eliminated before them, Bellows would have defeated either Shah or Jackson in one-on-one elections…This unintuitive fact is a generalization of a well-known feature of ranked choice voting elections: it does not satisfy the Condorcet winner criterion….Learning from experience instead of curated datasets
Learning from experience is different from learning from curated datasets. Algorithms that learn from curated datasets can assume that all data is useful for learning. This is usually guaranteed by humans, who collect, clean, and filter raw data so that it is ready to be consumed by a learning algorithm. Experience, on the other hand, does not always contain learnable associations…
All these layoffs have made me question my job search [Reddit]
Right now I’m at a company that hasn’t done layoffs since maybe the financial crisis. I know how fortunate that is. But if I switch jobs, I could make an extra $50K. So I keep asking myself: is that extra 50K worth the instability that comes with tech jobs right now? What if I join a company and get laid off within a year?…Users are Humans in Some Context
If we model a human as a conditional probability distribution: P(action | context),then the essence of understanding human behavior—and designing systems for them—lies in unpacking what actually constitutes that context and how the action is selected…
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Last Week's Newsletter's 3 Most Clicked Links
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* Based on unique clicks.
** You can find last week's issue #659 here.
Cutting Room Floor
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Thank you for joining us this week! :)
Stay Data Science-y!
All our best,
Hannah & Sebastian