Issue #656
June 18, 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

• Free SQL→ER diagram tool, runs in the browser, nothing uploaded
  Paste a SQL schema (CREATE TABLE statements) → get a clean, interactive ER diagram. Open source and 100% local — it runs entirely in your browser, so your schema never leaves your machine. No server, no signup, no upload…

• Preschoolers search semantic networks in a broader and more variable way than adults: Implications for hypothesis generation
  We find that adults show greater dependencies between sequential guesses than preschoolers, and generate a less diverse set of options. These findings may support the idea that development can be viewed as analogous to simulated annealing strategies in machine learning that start “hot” (in early childhood), generating wider and more variable searches, and eventually cool (in adulthood) to generate narrower searches…

• New CRAN Packages: signal or noise?
  CRAN continues to be the most accessible repository for statistical knowledge on the planet, and the number of new packages being accepted by CRAN is growing faster than ever. But, is the R community really benefiting from this new growth?…

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 90-year-old idea behind JEPA models: Canonical Correlation Analysis (CCA)
  Harold Hotelling’s 1936 Canonical Correlation Analysis (CCA) [modern terminology, “CCA is used to find a common signal among two large matrices”] forms the theoretical and intuitive foundation for modern embedding prediction techniques, including JEPA models…
  

• 2026 Data Science Tech Stack at your Job [Reddit]

  What is your current tech stack at your job?

• A Beginner’s Guide to Robotics Hardware
  Building an open-source robot begins in a way that is familiar to assembling IKEA furniture…The similarity ends once the robot is powered on. A bookshelf is designed to stay exactly as assembled, whereas a robot has to move while remaining correct about its own position and the state of its surroundings…When designing and building robots, this need for correctness both numerically and temporally is a key consideration…The rest of this post looks at how that difference manifests, using a common framing in robotics that divides the hardware into three parts: the movement, the body, and the sensor…
  

• PyData London 2026 Talks
  All the talks from the PyData London 2026 are now available…
  

• Data Engineering Acquisitions (2022-2026)

  Consolidation in the Data Engineering market is happening quickly. Tools from the Modern Data Stack get unified into bigger Data Platforms. This note highlights the latest acquisitions across data engineering. It serves as an overview of the latest consolidations. Find attached the acquisition overview from 2022 to today…
  

• The software industry: annealing, but wrong
  In recent months I’ve heard of several teams with an interesting policy: each pull request should be no more than a few files, and no more than a certain number of lines (say 500). And do just one thing and do it well. And be easy for a human to review. And be fully tested by the test suite…And often, the results are good. Sure, splitting a single 6000-line feature or fix into twelve 500-line PRs is more work, but each of those PRs is surely easier to review. And you can git bisect them when there’s a bug! And maybe revert the individual change that broke something. ...and also cause 12x as many context switches for your reviewers as they review each one sequentially. But that’s just the cost of software quality! Right?…


• AI and Survey Sampling Problems
  My previous post discussed the performance of the artificial intelligence (AI) interface Gemini on undergraduate statistics problems. Now let’s look at how Gemini answers some of the problems in my sampling textbook (Lohr, 2022), and talk about how Gemini could help students learn sampling…
  

• Test Doubles Taxonomy for R: Dummy, Stub, Spy, Mock, Fake
  You might call them all “mock”…Mock the database. Mock the API. Mock the function. The word becomes a catch-all for any test double, any object you substitute for a real dependency in a test. Lumping them together makes it harder to choose the right tool, and the wrong choice leads to brittle, misleading tests. There are five distinct types, each with a specific job. Knowing which is which is how you stop writing tests that do the wrong thing…
  

• Fractional Brownian Motion

  A Brownian Motion (BM), without the “fractional” part, is a motion where the position of a given object over time changes in random increments…A Fractional Brownian Motion is a similar process in which the increments are not completely independent from each other, but there’s some sort of memory to the process…I believe fBM is not necessarily a well understood mechanism. So this article describes how it functions and their different spectral and visual characteristics for various values of their main parameter H, backed with some experiments and measurements…
  

• Running local models is good now
  I’ve been working with local models since they came out, and finally, they’re surprisingly good now…
  

• Snapshot Testing in R: Beyond Screenshots

  In this post I want to walk through using snapshot testing for what it is good for, and the practices that make it efficient…
  

• What algorithm is surprisingly new? [Reddit]
  Other than any AI stuff, I’m talking about the types of algorithms you learn about in any standard Data Structures and Algorithms University course…I’m surprised that alot of these algorithms were actually invented HUNDREDS of years ago…
  

• The Ghost Couple: Correlated LLM Name Priors and Their Haunting of the Web and Academic Publishing
  These names do not exist. Elena Vasquez and Marcus Chen have appeared as volcano experts, astronauts, thriller protagonists, podcast hosts, and academic co-authors across hundreds of independently produced AI-generated documents, never having lived. We show that llms do not merely default to high-probability individual names when generating fictional experts: they produce correlated character ensembles, pairs and trios whose co-occurrence rates far exceed chance and are consistent across independent generations. These priors are model-family-specific (Claude: Elena Vasquez + Marcus Chen + Amara Okafor; Gemini: Aris Thorne + Lena Petrova; GPT: Elara Voss with no fixed partner)…

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Last Week's Newsletter's 3 Most Clicked Links

• Forecasting: Principles and Practice, the Pythonic Way

• My unvarnished guide to solution engineering

• An Overview of Modern AI Robotics from First Principles

.
* Based on unique clicks.
** Please take a look at last week's issue #655 here.

Cutting Room Floor

• 2026 Rousseeuw Prize for Statistics Awarded to R Core Team for Transforming Statistics Computing Worldwide

• Learning Amino Acids Part 1: Non-Polar Amino Acids, Rodrigues Rotation, and Lennard-Jones Potential

• Gambling provides a gentle rocking of the emotions to put you in a pleasant baby-like state

• The Caitlin Clark Effect

• Formal methods and the future of programming

• Crunch Big Data on Your Laptop With Polars Streaming

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Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Issue #655
June 11, 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

• An Overview of Modern AI Robotics from First Principles
  There is a deceptively simple way to describe what physical AI is all about, a way in which anyone with a STEM background will intuitively understand. Like all other AI models, a model which controls a robot is also a function. It takes in observations (camera pixels, joint angles, the felt resistance of a gripper, etc) and it outputs actions, the next set of positions and torques for its motors…If you’ve ever trained a model that maps inputs to outputs, you can already grasp the shape of the problem. The interesting part is what happens when you take this familiar shape and drop it into a moving, active world…This sounds like ordinary machine learning, and for a while you can pretend it is. But robotics introduces a third axis that classic ML never had to respect: inference time…

• My unvarnished guide to solution engineering
  Nowadays I feel more or less comfortable interacting with customers. But I was awful at first. I know because one of the cofounders gave me harsh feedback after a call with our first serious customer. I still remember slamming the lid of my computer when we debriefed. What I perceived as harsh feedback at the time turned out to help me grow quickly…I used to be a regular data scientist assigned to internal projects. Talking to prospects and customers got me out of my comfort zone. You owe them a service, and they expect you to deliver something. If something goes wrong they’ll go above your head to your founders, at which point you start feeling the heat. It can be quite harsh. But it can also be rewarding when things go well…

• Navier-Stokes fluid simulation explained with Godot game engine
  Let me start with the mathematical description of what we will do in this blog post. This description might sound daunting, but don’t worry - we’ll explain everything as we go. Here goes: we will simulate fluid flow by moving a scalar density field through a vector velocity field. We’ll simulate velocity diffusion and advection as well as density diffusion and advection. Then we will add velocity projection with the goal of making the fluid obey the law of mass conservation - which will happen by balancing divergence with a pressure field. We will use bilinear interpolation and Gauss-Seidel relaxation for approximating values where needed…

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 Anti-Scaling Law in Biology, and Why AI Could Make Crowding Worse Before Making Drug Development Better
  One of the main reasons for the tech community’s optimism is the scaling-law. Once you demonstrated 0-1, you can do 1-100 much quicker. The internet, social media, and so on…In biology and drug development, I think there is a mirror image, the anti-scaling law. Because of that, here’s my contrarian view: AI could make crowding in drug development worse, before making it better. And that’s my perspective as a genuine believer in the transformative power of AI, and an AI practitioner who used $14,000 worths of AI tokens in the past 2 months…
  

• What is there besides Frequentist and Bayesian stats? [Reddit]

  I am wondering whether there are lesser known statistical paradigms. like most people, I was first acquainted with the Frequentist framework, and later got introduced to Bayesian stats. I really like the way this made me reconsider some of what I thought were basic assumptions, so now I’m wondering what the next thing could be? Are there any other branches/frameworks which are not as well known?…

• Forecasting: Principles and Practice, the Pythonic Way
  This textbook is based on Forecasting: Principles and Practice (3rd ed) and is intended to provide a comprehensive introduction to forecasting methods and to present just enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will hopefully fill in many of those details…
  

• The Simplest Learning Machine, Pt.2
  In the previous article I outlined the concept of the Simplest Learning Machine. It’s an imaginary algorithm that uses one byte of persistent memory and learns to predict something about a stream of binary events…Can we actually write something like that? How would it work?…One semi-obvious thing we can learn is the rate of positive events in the stream. This would give us some predictive power, as long as that rate is different from 50%. A bit of a stretch to call this “machine learning”, sure, but I’ll get to the questions of usefulness later…
  

• On Training Data for Bio AI Models

  As we advance biological foundation models, which lessons from LLM data curation transfer, and which need rethinking?…
  

• Our goodpractice Package Has New Superpowers
  The goodpractice package has been recommended by rOpenSci since it was first started just over 10 years ago by Gábor Csárdi. We used to ask our editors to manually run goodpractice on all packages submitted to software peer-review, and then to ask authors to fix any notable issues flagged by the package…We’re really pleased to share that we’ve recently rolled out a host of updates and extensions to the package. These make it both easier to use, and more powerful…I think that is the aspect that I found most surprising: That the use of Claude made our collaboration feel less technical, and therefore somehow even more human. And that gave us the ability to work though 70 pull requests representing over 100 new checks, all ready for everybody to use…


• The Warehouse
  The Problem

  With over 23,000 packages on CRAN alone, finding the right package for your task is overwhelming:

  • Searching by keywords often misses relevant packages

  • No easy way to compare similar packages

  • Quality indicators are scattered or missing

  • GitHub-only packages are hard to discover

  The Warehouse Solution provides:

  • Function-first search: “estimate serial interval” → find all relevant packages

  • Quality scores: Automated assessment of tests, documentation, and maintenance

  • All sources: CRAN, GitHub, Bioconductor in one place

  • Community reviews: Real user experiences and recommendations

  • Smart categorization: Browse by what packages actually do…
    

• Robot Learning: From Fundamentals to Foundation Models
  This course provides a comprehensive introduction to modern robot learning, combining classical techniques with the latest advances in large-scale models: Students will start by learning the fundamentals of imitation learning, reinforcement learning, and policy optimization, and gradually progress to advanced topics including Vision-Language-Action (VLA) models and foundation models for robotics The objectives of this course are:

  • Understand the core principles of imitation learning, reinforcement learning, and policy learning.

  • Implement basic robot learning systems in simulation and on real robots.

  • Explore state-of-the-art Vision-Language Action and foundation models for robotics.

  • Design and evaluate scalable robot learning pipelines integrating perception, control, and multi-modal reasoning…
    

• ML Job Interviews: The Ultimate Guide

  How I found a Research Scientist role after a PhD in Machine Learning…My process was, overall, successful: I received offers from every company I completed interviews with including: DeepMind (which I accepted), Isomorphic Labs, Cohere, Meta, and a startup in stealth. A few caveats to the first claim: Anthropic, Mistral, and TeslaAI got back to me too late and I didn’t complete those processes. ReflectionAI, the one genuine rejection: they didn’t like me for the RS role but switched me to their Engineering track instead…
  

• stata-mpl - Give your matplotlib and seaborn charts the Stata 19 look
  Give your matplotlib and seaborn charts the look of Stata 19 (the stcolor scheme, Stata’s colorblind-friendly default). Calibrated against the official SVG files exported by Stata 18/19…
  

• Avian Visitors

  I mounted a tiny microphone on my apartment balcony to listen for any birds passing by and built a site to collage them as they’re heard…so I’ve thrown together this short writeup for any of you who want to monitor any avian visitors that may be passing by your own place. It’s short and sweet for now in an attempt to get something out quickly, but this work is part of a longer chain of bird-tangent projects i’ll write something up about soon!…
  

• Ask HN: What are tools you have made for yourself since the advent of AI?
  Ask HN: What are tools you have made for yourself since the advent of AI?…
  

• Why Academics Should Use AI for Writing: A Case Study
  I violently dislike the idea of AI taking over my writing. My writing is my own, and having it done by AI makes the final product lose its soul. Also, whenever I have used AI to write several paragraphs independently (which I admit to doing for bureaucratic tasks) I ended up rewriting most of it anyway. However, over the past year or so I have become increasingly impressed with what AI can do, and rather than talk about this in abstract terms I would like to present you with a concrete demonstration that shifted my opinion a great deal…

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Last Week's Newsletter's 3 Most Clicked Links

• How to Build a Simple, Bulletproof Data Pipeline

• supertree - Interactive Decision Tree Visualization

• Beyond the Semantic Layer: Building a Context Layer for the Agentic Era

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* Based on unique clicks.
** Please take a look at last week's issue #654 here.

Cutting Room Floor

• When is detecting AI-generated text worthwhile?

• Lines of Code Got a Better Publicist

• BayesMultiMode: Bayesian Mode Inference in R

• Cognitive Endurance as Human Capital

• DiffusionBlocks: Training Neural Networks One Block at a Time

• Data from 66,000+ practice sessions. How much does the typical musician actually practice? [Reddit]

• What is the most common reason data science projects fail to deliver business value? [Reddit]

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,500 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Issue #654
June 04, 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 to Build a Simple, Bulletproof Data Pipeline
  In most organizations, a lot of value can be delivered with a setup that avoids high costs and unnecessary complexity. The most common scenario is not real-time streaming or exotic architectures. It is daily extraction from one or more transactional systems backed by a relational database…In this article, I want to walk through a simple but realistic example and show how a few design decisions, even when they look basic, can make a meaningful difference in robustness, operability, and long-term maintainability…

• AI for Bio has a Fuzzy API problem
  “AI for bio” is getting hot again. Given the excitement in the current moment, I thought I’d share a bit about what actually makes biology uniquely hard as an application domain for machine learning. The reason is not simply that biology is complicated, though it obviously is. ML is good at many things that are complicated. The deeper reason is that drug discovery does not have the kind of clean feedback loops and clean interfaces that made modern ML so powerful elsewhere…

• Gaussian Point Splatting
  We propose Gaussian point splatting, a stochastic method to render Gaussian splats that scales extremely well to scenes with many Gaussians. Our core idea is to sample pixel-sized, opaque points from the Gaussians and to splat them to a framebuffer using 64-bit atomics. Through parallel programming primitives, we achieve an even distribution of the workload across millions of threads…

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

• State of London Report, 2026
  The State of London report brings together an array of data about how London is performing across its economy, society and environment. Published annually by the GLA’s City Intelligence unit, it provides an up‑to‑date, data‑led picture of life in the capital, drawing on a wide range of official and administrative sources. The report highlights trends and patterns and offers a shared evidence base to support debate and decision making…
  

• What is considered basic R? [Reddit]

  I have a job interview coming up and they want someone who knows basic R, I think I do have it, but what is your opinion on what it entails?…

• BRIDGE (Benchmark of Regional & International Data for Global Evaluation) is the first independent Global South ASR benchmark
  BRIDGE (Benchmark of Regional & International Data for Global Evaluation) is the first independent Global South ASR benchmark evaluating 15 global models across 22 languages on a first-of-its-kind 7 metric stack…
  

• supertree - Interactive Decision Tree Visualization
  supertree is a Python package designed to visualize decision trees in an interactive and user-friendly way within Jupyter Notebooks, Jupyter Lab, Google Colab, and any other notebooks that support HTML rendering. With this tool, you can not only display decision trees, but also interact with them directly within your notebook environment. Key features include:

  • ability to zoom and pan through large trees,

  • collapse and expand selected nodes,

  • explore the structure of the tree in an intuitive and visually appealing manner…
    

• A Primer in Post-Training Reasoning Data: What We Know About How It Works

  Work on post-training reasoning data has grown rapidly, yet this literature remains scattered across dataset papers, reinforcement-learning recipes, reward-model studies, benchmarks, and frontier system reports. This paper is the first primer to synthesize over 150 key public studies and system reports on post-training reasoning data. We organize the field around four questions: what data objects exist, what makes them useful, how they are constructed, and how they scale. Together, this organization provides an attribution framework for future reasoning-data releases and post-training recipes…
  

• Don’t know where your data is from? Bayesian modeling for unknown coordinates
  An especially strong motivating case for the usage of spatial probability models comes from the mining industry. During exploration for mineral resources, prospectors will take geologic samples by drilling holes and examining the resulting material for presence or concentration of valuable ores. These data typically show strong spatial correlation, but constructing a fully-detailed geophysical model is at times infeasible as we are able to observe very little of the underground conditions, though the advent of remote sensing techniques like ground-penetrating radar and gravimetry has dramatically improved our ability to characterize Earth’s subsurface. To address this challenge, we would like to construct a probability model which uses nearby data to predict a variable of interest at a new location…


• Why I think panic about local impacts of data centers is just a panic
  In the last year of following increased local resistance to data centers being built, I’ve listened to lots of recorded testimony at town halls, read through countless comments and articles where people argue for why data centers are so uniquely evil and shut down anyone defending them as shills for AI companies…whenever I look into where people are actually getting their ideas about the hundreds of other data centers being built, the source always leads back to some confused misreading of local reporting, a wild calculation error, a bad game of telephone, or a wildly misleading article…
  

• Beyond the Semantic Layer: Building a Context Layer for the Agentic Era
  A context layer puts your warehouse schema, joins, metric definitions, and business knowledge in one reviewable place so data agents query governed context instead of guessing field names. A look at how it works, and at ktx, the open-source context layer…
  

• Jensen-Shannon Divergence

  How different are two discrete or binned probability distributions on the same support?…
  

• Fluid Simulation for Dummies
  I wrote my Master’s thesis on high-performance real-time 3D fluid simulation and volumetric rendering. The basics of the fluid simulation that I used are straightforward, but I had a very difficult time understanding it. The available reference materials were all very good, but they were a bit too physics-y and math-y for me. Unable to find something geared towards somebody of my mindset, I’d like to write the page I wish I’d had a year ago. With that goal in mind, I’m going to show you how to do simple 3D fluid simulation, step-by-step, with as much emphasis on the actual programming as possible….
  

• A Hitchhiker’s Guide to ML Training Infrastructure

  Hardware has made a huge impact on the field of machine learning (ML). Many of the ideas we use today were published decades ago, but the cost to run them and the data necessary were too expensive, making them impractical. Recent advances, including the introduction of graphics processing units (GPUs), are making some of those ideas a reality. In this post we’ll look at some of the hardware factors that impact training artificial intelligence (AI) systems, and we’ll walk through an example ML workflow….
  

• Weaponized phrases in Data science Teams [Reddit]
  “No free cycles” / “Empty plates”…“We need to focus on the low-hanging fruit”…"Be a go-getter, don't get stuck"…"Let's optimize our sprint velocity"…“You’re making this more complicated than it is”…"We need to relentlessly prioritize"…"I need you to own this initiative"…"Let's take this offline" / "Parking lot this"…"We need to leverage AI to unlock enterprise value"…"We're like a family here"…
  

• Agricultural Field Boundaries, Mapped Globally for the First Time
  For the first time, every agricultural field on Earth has a boundary on the map. Taylor Geospatial funded and co-developed this work with Microsoft AI for Good Lab because we believe GeoAI should work everywhere, not just in the data-rich regions where labeled training data is abundant. Today, it’s publicly available for everyone to benefit from…

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Last Week's Newsletter's 3 Most Clicked Links

• What DS job market trends are you seeing? [Reddit]

• 6 LLM Prompting Techniques for Data Scientists and Engineers in 2026

• I am faking my way through a Data Analyst role with AI, how do I actually learn before I get caught? [Reddit]

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* Based on unique clicks.
** Please take a look at last week's issue #653 here.

Cutting Room Floor

• Expertise in the Age of AI

• We should be more tired than the model

• Domain Expertise Has Always Been the Real Moat

• SQLite is All You Need for Durable Workflows

• Implications of the Draft FDA Bayesian Guidance

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,500 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Issue #653
May 28, 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

• The quadratic sandwich
  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
  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?
  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…

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

• Friday Pins vs Sunday Pins or: How to Illustrate Something Completely Obvious
  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]

  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.

  1. The number of AI postings is going down. ML and DE skills are back in fashion.

  2. Salaries are down across the board.

  3. 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
  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
  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 practices for comparing and reporting the accuracy of my algorithm against manual markup?…
  

• Do Most TV Shows Stick the Landing?

  Four decades later, television has changed dramatically, reshaped by streaming and a clearer understanding of what makes for a satisfying conclusion. But has this institutional knowledge led to better endings? Have showrunners learned from the mistakes of St. Elsewhere, Game of Thrones, and other finale fiascos? So today, we’ll investigate whether omniscient showrunner Tommy Westphall has gotten any better at sticking the landing, how finale quality has changed in recent decades, and whether finality is simply a structural weakness of television itself…
  

• NYED Data Explorer Shows 15 Years of Charter School Success
  When I discovered 15 years of NYED assessment data, the interest to clean and free this data for others to discover in a Shiny app, was immediate. The opportunity to also feature Classical’s stand-out performance didn’t hurt my motivation, although this post and the app were built in my spare time, and do not represent the opinions of South Bronx Classical Charter Schools. Unlike many past Redwall posts, this one will not have code, and will be primarily to explain the data and show how to use the app…


• Luck vs. skill in poker
  The thread of our recent discussion of quantifying luck vs. skill in sports turned to poker, motivating the present post…Can good poker players really “read” my cards and figure out what’s in my hand?…
  

• Speeding up Stan model builds for R package developers
  My PhD student was interested in Bayesian methods and we put together an R package which included some Stan models. I was always frustrated by how slowly these compiled on our Windows machines…A few years later, when I got a MacBook Air I was shocked how much faster they compiled. On my Windows machine our mrbayes package takes 3 minutes 55 seconds to compile and install. On my M4 MacBook Air it takes 1 minute 16 seconds. The following tips show how to improve those timings…
  

• How unfair is the coin?

  In February 2024, Reverie Labs, the startup I co-founded in 2017, was acquired by Ginkgo Bioworks. I’m now on leave from Ginkgo and I’ve joined Y Combinator as a Visiting Partner, giving me the chance to work with the next generation of companies. Especially in this new role, I’ve been thinking a bit about what worked, what didn’t work, and what lessons I can take forward…We had quite the journey – 6+ years of building at the intersection of AI and drug discovery. We began as a machine learning driven software company selling SaaS tools and consulting services to pharma companies, and at acquisition we were a pharmaceutical company, developing our own in-house pipeline of drug assets and advancing them rapidly using our machine learning technology…
  

• Functions over Idioms - Writing R in Python with rfuns
  Sometimes a problem calls for a particular language to be used, and with that comes adjusting one’s brain to thinking in that language and using the appropriate idioms to leverage that language’s features…But what if I don’t want to?…The line between R and Python has been heavily blurred the last few years, particularly with {reticulate} (rstudio.github.io) enabling us to use Python within R code, RStudio rebranding as Posit (posit.co) and taking on a strong Python development effort, releasing Positron (posit.co) as a multi-language IDE, and Quarto (quarto.org) being a multi-language rethink of Rmarkdown…
  

• LLM Prompting Techniques for Data Scientists and Engineers in 2026

  Six techniques matched to six failure modes, including inconsistent output formats, shallow reasoning, instruction drift, and more…
  

• I am faking my way through a Data Analyst role with AI, how do I actually learn before I get caught? [Reddit]
  I graduated with a CS degree, but I spent my undergrad years grinding part-time jobs instead of actually studying. Now I am a Data Analyst at a small business, and the job is nothing like the theory I slept through in school. I am just winging it every day tbh. I rely heavily on openclaw for data scraping and acciowork to handle the processing and archiving. If these AI tools ever went down, I would be fired within an hour. I am terrified of being exposed as a fraud. Where do I even start fixing this? Should I grind python, or is mastering excel still the first step for survival?…
  

• 50 Hours to Draw Some Lines
  "What are you working on these days?"
  "Data visualizations." I told him.
  "Ah, you using algorithms, machine learning, cloud computing, things like that?"
  "No." I said. "I'm just trying to draw a line graph."….What do I mean by drawing data by hand? I made this data visualization (data viz) about a coffee maker computer by hand, using rulers, pencils, ink, and a lettering kit. Along with my flubs, flukes, and acclimation with tools - it took me 50 hours to make. It’s statistically accurate, carefully crafted, and like Hackaday said “right out of a 1970’s college textbook”. It’s how professionals might visualize data before computers could do it for them….

.

Last Week's Newsletter's 3 Most Clicked Links

• Are there any small, quick things I can do everyday to keep my skills sharp? [Reddit]

• After 5 years in data science, I’m starting to realize most “insights” we deliver are completely ignored. Is this normal? [Reddit]

• Is logistic regression regression?

.
* Based on unique clicks.
** Please take a look at last week's issue #652 here.

Cutting Room Floor

• Lahman Baseball Database, created by SABR member Sean Lahman, contains complete major league batting and pitching statistics back to 1871

• A Data-Driven Survey of MLB Franchise Management

• Behavior-Driven Development in R Shiny: Modeling User Behavior with When Steps

• I dont understand Standard Deviation [Reddit]

• Good practices in data scripts [Reddit]

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,500 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Read more

Issue #652
May 21, 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

• What’s going on in computational neuroscience nowadays? (part 1)
  A month ago I came back from Cosyne, the annual Computational and Systems Neuroscience conference…The days are a haze of tutorials, talks, poster sessions, and workshops, usually appended by dinners and drinks past midnight…I seem to have a hard time writing one-off pieces, so I’m leaning into that and writing this as a series. I’ll only be able to write a very narrow perspective of Cosyne, of course, but most of the talks are on YouTube on the official channel if you want to see for yourself. (That also means this will be more personal thoughts than report)…

• Is logistic regression regression?
  I came across a post recently by a machine learning engineer who made the bold claim that logistic regression is the worst name for an algorithm ever, or something along those lines…Many statisticians of the more old-school type seemed to disagree. This led me to think a bit more deeply about the subject. I’ve already written several posts on bad terminology in statistics (see confidence level, line of best fit, r squared) so I might have been expected to agree with the machine learning view, but in this case I agree with the statisticians, and I would like to explain why…

• What Every Experimenter Must Know About Randomization
  Randomized controlled experiments offer gold-standard insight into cause and effect. The knowledge that informs our most important decisions. Unfortunately, randomization in such experiments is often botched. Randomization errors silently invalidate the interpretation of experimental results, turning a fruitful quest for knowledge into a waste of time and money, or, worse, a wellspring of misinformation. Fortunately, these fatal errors are easy to spot and fix. So whether you’re a webmaster using A/B testing to increase engagement, a medical researcher evaluating vaccines, a factory manager exploring productivity improvements, or a scientist seeking the laws that govern nature or human affairs, read on…

What’s on your mind

This Week’s Poll:

.

Last Week’s Poll:

.

Data Science Articles & Videos

• Converting testthat Tests to testit
  Back in 2013, I wrote about testing R packages when I first released testit. Thirteen years later, I still believe that unit testing should be nothing more than “tell me if something unexpected happened.” Recently I converted a large testthat test suite to testit, and I thought I’d share a practical guide for anyone considering the same move…
  

• After 5 years in data science, I’m starting to realize most “insights” we deliver are completely ignored. Is this normal? [Reddit]

  I’ve been in data science roles (both analytics and ML) for about 5 years now across a couple of companies. Lately I’ve been feeling a bit burned out because I keep seeing the same pattern…We spend weeks cleaning data, building dashboards, running statistical analysis, or training models… and then the stakeholders either:

  • Say “thanks” and never use it

  • Cherry-pick the numbers that support their existing opinion

  • Or just completely ignore the findings and go with gut feel anyway

  The worst part is when leadership asks for a “data-driven decision” but they’ve already decided what they want to do…Am I alone in this? Or is this just the reality of data science in most companies?…

• Tagging my blog posts with BERTopic and LLMs
  I recently added tags to my blog using BERTopic and a mix of LLMs. You can see the tags in the sidebar to the right (or in the footer on mobile). I’ve done this before in 2023, with GGUF Mistral using llama-cpp, but never finished the project. Now, because the models have been getting so good, and my project was small, relatively well-defined, and easy to evaluate, the project took me about 6-10 hours over a month, using BERTopic, Claude Code, and Pi with Deepseek…
  

• What data science is actually about in the age of AI
  I reflect on the evolving role of data scientists in the age of AI and LLMs. I argue that our core mission remains rigorous measurement, not full-stack development. While AI tools make building easier, the real value comes from defining and evaluating what truly matters. I share why measurement should be led by those closest to the problem and how data scientists can best contribute. Are we losing sight of what makes data science essential in the rush to build with AI?…
  

• Transformer From Scratch

  I’ve wanted to dive deeper into the fundamentals of AI for a while now - it feels a little bit magical, and a little bit wrong, to operate alongside AI without a strong understanding of how the underlying mechanisms work. Naturally, I had to write a transformer, and Neel Nanda’s ”GPT-2 From Scratch” was my resource of choice…This post is meant to document my process of learning and to address some of the questions I was curious about when implementing the transformer for the first time. It includes an overview of transformer basics and some of my intuitions, followed by some of the points of interest (transformer secrets, if you will) and challenges I ran into…
  

• The last six months in LLMs in five minutes
  I presented this lightning talk at PyCon US 2026, attempting to summarize the last six months of developments in LLMs in five minutes….


• Five tips for managing your R-universe
  rOpenSci’s R-universe system is an open source platform allowing users to create their own CRAN-like universe of R packages…This post gives five tips I have developed to help manage my R-universe…
  

• Research-Driven Agents: What Happens When Your Agent Reads Before It Codes
  Coding agents working from code alone generate shallow hypotheses. Adding a research phase — arxiv papers, competing forks, other backends — produced 5 kernel fusions that made llama.cpp CPU inference 15% faster.
  

• Kalman and Extended Kalman Filters: Concept, Derivation and Properties

  This report presents and derives the Kalman filter and the Extended Kalman filter dynamics. The general filtering problem is formulated and it is shown that, under linearity and Gaussian conditions on the systems dynamics, the general filter particularizes to the Kalman filter. It is shown that the Kalman filter is a linear, discrete time, finite dimensional time-varying system that evaluates the state estimate that minimizes the mean-square error…
  

• How not to turn power on its head
  In giving some informal remarks about power at a seminar a couple of weeks ago, I proposed that the tendency to turn the notion of power on its head might be avoided by imagining we need to define a test’s error probabilities in terms of its power alone. We can refer to the power against the null hypothesis, rather than alluding to a type 1 error probability, for example…What do I mean by turning power on its head? I mean, at least here, supposing that a test provides poor evidence of discrepancies that the test has low power to detect…
  

• The Atlas-Learn Approach to the Manifold Hypothesis

  The 2025 paper by Robinett et al., ‘Atlas-based Manifold Representations for Interpretable Riemannian Machine Learning’, provides an algorithm for fitting a low dimensional manifold from a point cloud by means of a novel algorithm for approximating an atlas of charts. This post illustrates the Atlas-Learn method by reconstructing a sphere from a 3D point cloud of naive random samples and works through some checks on accuracy…
  

• Are there any small, quick things I can do everyday to keep my skills sharp? [Reddit]
  I’m sure everyone knows about the dilemma of AI at this point. We want to work faster but our skills are atrophying yada yada…as a junior data scientist, I feel like I barely had any skills to begin with. Now with my company forcing us to use AI, I feel like I’m not learning much. Now I’ve been doing leetcode, but I just don’t think it’s that applicable to my real job. I don’t have the bandwidth outside of work to do a project yet, since my company is working us to the bone. What are some quick habits/tools/websites/apps you recommend to keep your skills sharp?..
  

• The Measurement of Loudness
  I’m not a sound engineer of any sort, but I enjoy music and have been blessed with decent hearing acuity, so I tend to pay attention to noises going on around me. Now, I know what you’re thinking! Surely, a measurement nerd interested in sound would have bought a cheap SPL meter off the internet and this is what this post is about. And you’d be wrong! Hah! Because this post goes a bit further off the deep end because SPL and the dB(a) scales that we commonly associate with “sound volume” always confused me when I tried to understand them. Like with how measuring color is really difficult because it’s at the intersection of a physical measurement and human perception (see: How the heck does one measure color?), sound is just as messy because it’s again physical measurements (sound pressure) mediated by the human auditory system. To measure loudness, we’re going to have to go back a bit in history…

.

Last Week's Newsletter's 3 Most Clicked Links

• Softmax, can you really derive the Jacobian? And should you care?

• Teaching <Fundamentals of Machine Learning>

• Illusions of Understanding in the Sciences

.
* Based on unique clicks.
** Please take a look at last week's issue #651 here.

Cutting Room Floor

• the vexing Hausdorff measure

• Multivariate reconciliation for hierarchical time series

• Comments on: What Every Experimenter Must Know About Randomization

• Introducing Metrics SQL: A SQL-based semantic layer for humans and agents

• Collaborating between Bioconductor and R-universe on Development of Common Infrastructure

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,500 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Read more

Read more

Read more

Issue #651
May 14, 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

• Marco Polo: Finding a friend with only distance and motion.
  You walk into a cafe, looking for your friend. Seems like an easy task, until you see it’s so packed that you can’t see through the crowd at all, and everyone’s talking so loud that you can barely hear anything. The only things you know are your movements, and how far you are from your friend (through the special psychic bond you two share). How will you find each other?...I wanted to solve the exact same problem, but with devices instead of people (so no psychic connection for me), existing in a space of hundreds of other devices. Working the problem taught me a lot of really interesting science relating to robotics and state estimation, and I wrote this post so you can learn, too…

• Biology is a Burrito
  A bacterium’s genome, pulled into a straight thread, is nearly 1,000 times longer than the cell from which it came. If you placed one E. coli into a gallon-sized jug with some nutrients and waited a few hours, the genomes of its descendants, placed end-to-end, would reach to the moon and back...several times….The truth is that biochemistry textbooks often depict cells as spacious places, where molecules float in secluded harmony. “But a cell looks more like a burrito,” says Michael Elowitz, a biologist at Caltech. All the biochemicals are pushed together, bumping into each other…

• Softmax, can you really derive the Jacobian? And should you care?
  Multiclass output? Softmax. Normalising probabilities? Softmax. Attention weights? Softmax. Partition function? You guessed it, Softmax. This function comes up everywhere, but how often have you really thought about what’s going on inside?…What does softmax actually do to your distribution?…The softmax function is deceptively simple…

What’s on your mind

This Week’s Poll:

.

Last Week’s Poll:

.

Data Science Articles & Videos

• Illusions of Understanding in the Sciences
  The first part of this essay supports the case for the universality of partial and incomplete levels of understanding by showing the difficulty of reaching a deep level of understanding for even a simple analysis and model that most scientists use and believe they understand: linear regression. The second part highlights some implications of the existence of many levels of understanding and explanation, and their use by scientists for design, testing, analysis, and theory development. It discusses the way that deduction and induction depend on the levels of understanding and the implications of the illusion that a scientist’s understanding is deep…
  

• People Interested in Continual Learning Research [Reddit]

  Recently, I’ve become fascinated by Continual Learning, especially the idea of AI systems that can continuously adapt and improve from experience rather than staying static after training. I’m a student just starting my journey in CL research and would love to connect with people exploring similar ideas. Whether you’re a student, researcher, or just curious about the field, feel free to DM me. Would also love paper recommendations and interesting research directions…

• You Should Probably Map That: Introduction to Geospatial Analysis in R
  Anjile An, Weill Cornell Medical College…Data comes in many forms, but the spatial components can be overlooked. You’ll get some history of mapping and learn the basic building blocks of spatial data. The demo portion will go over how to use the trusty {ggplot2} as well as some new tools like {sf} and {tmap} to plot different types of spatial data…
  

• Requirements analysis: catching requirement bugs before they become code
  Every experienced engineer has a story where a feature shipped, worked on the happy path, and then quietly did the wrong thing on some edge case no one had thought about. Trace the bug back far enough and it rarely ends at the code; it ends at a sentence in a requirement document that meant one thing to the person who wrote it and something else to the person who implemented it…
  

• Teaching <Fundamentals of Machine Learning>

  This past spring, i taught <Fundamentals of Machine Learning> for computer science seniors (with some juniors as well as seniors from other majors, including data science and economics) at NYU. last time i taught this course, the course was titled <Introduction to Machine Learning>, it was pre-ChatGPT and it was pre-pandemic; in fact, i was teaching this course in the spring of 2020, and the whole university, city and world went into its first lock down mid-way. in other words, i taught this course in the old world, and i was asked to teach this course in this brave new world…
  

• Interactive Jensen–Shannon Divergence Visualisation
  An interactive visualisation of Jensen–Shannon divergence - the symmetric, always-finite cousin of KL. Shape two distributions and watch JSD, its ceiling of one bit, and the per-point contribution respond in real time…


• 70 years of love, empowerment, and freedom. Here’s a look at Eurovision by its lyrics.
  Famous for its upbeat rhythms, flamboyant performances, and political voting patterns, the competition has another dimension that moves with the zeitgeist: its song lyrics…
  

• What I’m Hearing About Cognitive Debt (So Far)
  A week ago, I wrote about how Generative and Agentic AI may be amplifying what I’ve been calling cognitive debt: the accumulated gap between a system’s evolving structure and a team’s shared understanding of how and why that system works and can be changed over time. The post sparked thoughtful discussion across different communities. Rather than respond thread by thread, I want to synthesize what I’m hearing and connect it to other reflections I’ve been reading. I will likely update this as the conversation evolves…
  

• Polynomial autoencoder

  The most direct way to compress an embedding (other than quantization) is to fit PCA on the corpus and keep the top-d eigenvectors. It works, but PCA is a linear projection, and neural-network embeddings on the sphere are structurally nonlinear — the well-known cone effect in transformers. Some of the variance lives in a nonlinear tail that a linear decoder can’t reach…This post is about a closed-form way to add a quadratic decoder on top of PCA, to capture part of that nonlinear tail. The encoder stays as plain PCA. The decoder is a degree-2 polynomial lift plus Ridge OLS (ordinary linear regression with L2 regularization), also closed-form. No SGD, no epochs, no hyperparameter search. One np.linalg.solve over corpus statistics…
  

• Visualize the Brrr - Learn GPU programming
  Who This Is For: This is for anyone interested in GPU programming or performance engineering, whether you’re writing CUDA, HIP, Triton, cuTile, Gluon, Helion, JAX, or nothing at all. If you’ve ever struggled to conceptualize parallelism, memory coalescing, or tiling, these visualizations are meant to make those ideas concrete. No prior GPU experience required.

  What You’ll Learn: The lessons walk through fundamental concepts of GPU programming, parallelism, memory hierarchy, and more. Each chapter pairs a short explanation with an interactive visualization you can poke at…
  

• PyTrendy

  PyTrendy is a robust solution for identifying and analyzing trends in time series. Unlike other trend detection packages, it is robust to noisy & flat segments, and handles for gradual & abrupt trend cases with a high precision. It aims to be the best package for trend detection in python…
  

• Standardization vs Log transform? [Reddit]
  I have been trying to understand the use cases of both of these and I am really confused. I know log transform fixes the features and makes their distribution normal and standardization on the other hand only fixes the scale of the feature by keeping the distribution the same. Are these things which I use one after the other ? Or just simply use one depending on the case (which I also don’t understand when) ?…
  

• AI agents can create convincing ecological models, but you still need to know what you’re doing
  Agentic AI tools like Claude Code can write and run code, fix its own errors, and produce a formatted report with figures. I wanted to know whether that translates into reliable ecological modelling, so we ran a test: three fisheries tasks, four AI models, ten independent runs each, scored against a rubric. The results are published in Fish and Fisheries. We found agents can be genuinely useful, but only if you know how to use them well and only if you know enough about the analysis to catch what they miss…

.

Last Week's Newsletter's 3 Most Clicked Links

• A decade of being an average Data Scientist! My personal experience [Reddit].

• Notes from inside China’s AI labs: Lessons from my trip to talk to most of the leading AI labs in China.

• DS market is kind of insane right now [Reddit]

.
* Based on unique clicks.
** Please take a look at last week's issue #650 here.

Cutting Room Floor

• Notes on Category Theory with examples from basic mathematics

• MySQL for Developers

• Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

• Design your plots (ggplot) for mobile

• Assessing Credit Score Prediction Reliability Using Bootstrap Resampling

• The Figure Problem in Scientific AI: Building a Multimodal Literature Agent for Biology, Powered by NVIDIA Nemotron 3 Nano Omni

• Publishing a Quarto Blog: What I Learned Moving from Netlify to GitHub Pages

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,500 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Read more

Read more

Issue #650
May 07, 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

• Notes from inside China’s AI labs
  Lessons from my trip to talk to most of the leading AI labs in China…

• Learning & Exploring Survival Analysis Part 1 - A Note To Myself
  A note to myself on survival analysis — KM curves, log-rank tests & Cox models 🧮 If I wrote it the way I understood it, maybe I’ll actually remember it 🤞…

• Two Cartography Expert Review MOVIE MAPS
  John Nelson and Peter Attwood review maps in films. What they get right, what they might get wrong, and what we think of them as cartography nerds. Includes thoughts on: Indiana Jones, The Lord of the Rings, Avatar, War Games, Prometheus, Harry Potter, The Muppets, Pirates of the Caribbean, Game of Thrones, Moonrise Kingdom, The Goonies, and The Emperor’s New Groove…

What’s on your mind

This Week’s Poll:

.

Last Week’s Poll:

.

Data Science Articles & Videos

• Bad Weather and the Subway
  I’ve been looking at hourly ridership data from the New York City Subway. Last time we learned that people go to work in the morning and come home in the evening, for example. (All together now: “Only in New York, baby!”) Today, we’ll learn that bad weather makes people stay at home. Except, sometimes it doesn’t…
  

• A decade of being an average Data Scientist! My personal experience. [Reddit]

  Hello! I know there’s people here with PhDs, working in FAANG, on top of the newest tech, and are absolutely brilliant Data Scientists. I’m not one of them…I just wanted to share my positive experience from someone who is painfully average lol!! I wanted to show people, especially new grads and/or people pivoting into the field, that you don’t have to be the smartest person in the room to get hired. You need to drill into the solid foundations and a have a drive to make change/bring value to a company…

• The World Inside Neural Networks
  How neural geometry will unlock understanding and control of AI…If we understand how a model carves up and represents the world conceptually (i.e., its ontology), this will unlock a far deeper understanding of both its algorithms that operate over that ontology and the intelligent behaviors produced by those algorithms. We thus need new methods to gain that understanding; this series of posts details our early efforts to develop them, building upon – and alongside – numerous related efforts from others…
  

• Quantum Machine Learning: The Pragmatic Guide for classical ML Engineers
  Part 1 of the “Quantum ML for Engineers” series: From Transformers and GPUs to QPUs and Hybrid Intelligence…
  

• Bringing OpenTelemetry to R in production
  Posit has instrumented Shiny, plumber2, mirai, httr2, ellmer, knitr, testthat, and DBI with OpenTelemetry, and created tools for you to instrument your own packages, bringing production-grade observability to R…
  

• Data 100: Principles and Techniques of Data Science UC Berkeley, Spring 2026
  This intermediate level class bridges between Data 8 and upper division computer science and statistics courses as well as methods courses in other fields. In this class, we explore key areas of data science including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making.​ Through a strong emphasis on data centric computing, quantitative critical thinking, and exploratory data analysis, this class covers key principles and techniques of data science…


• Inverse Probability Weighting: From Survey Sampling to Evidence Estimation
  We consider the class of inverse probability weight (IPW) estimators, including the popular Horvitz–Thompson and Hájek estimators used routinely in survey sampling, causal inference and for Bayesian computation. We focus on the ‘weak paradoxes’ for these estimators due to two counterexamples by Basu (1988) and Wasserman (2004) and investigate the two natural Bayesian answers to this problem: one based on binning and smoothing: a ‘Bayesian sieve’ and the other based on a conjugate hierarchical model that allows borrowing information via exchangeability…
  

• The Magic of In-Context Learning (ICL): When Your Model Already Knows Your Data
  As an experienced data scientist, you have seen thousands of datasets in your career. When confronted with new data, your natural neural network (a.k.a. brain) simply draws on this vast library of past mathematical shapes and immediately recognizes the pattern. But what if an artificial neural network could do exactly the same thing? What if it could predict your data without actually being trained on it?…Welcome to the mind-bending world of In-Context Learning (ICL) for tabular data, brought to R via the incredible new TabPFN package (on CRAN)…
  

• Three ways to differentiate ReLU

  When a function is not differentiable in the classical sense there are multiple ways to compute a generalized derivative. This post will look at three generalizations of the classical derivative, each applied to the ReLU (rectified linear unit) function. The ReLU function is a commonly used activation function for neural networks. It’s also called the ramp function for obvious reasons…
  

• You Don’t Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, classification and survival analysis
  Random Vector Functional Link (RVFL) networks offer a simple yet powerful alternative to traditional neural networks for tabular data. Instead of learning hidden layers through backpropagation, RVFL generates them randomly (or not, if using a deterministic sequence of quasi-random numbers) and focuses all learning effort on a final, regularized linear model…
  

• Comparing R’s {targets} and dbt for Data Engineering

  I’m getting more and more into data engineering these days and having used R for a long time, I’m seeing a lot of problems that look nail-shaped to my R-shaped hammer. The available tools to solve those problems exist for (presumably) very good reasons, so I wanted to take some time to dig into how to use them and compare their workflows to what I would otherwise naively do in R…
  

• DS market is kind of insane right now [Reddit]
  So here’s the story: another team in my company opened an associate-level DS role last week, we got 300+ applications, and somehow there were 30+ senior-level guys applying for it. Not fake senior either. Like actually senior all with 10+ yoe….Curious that are other people & teams seeing the same thing, or is this just a weird sample on our side?…
  

• Visualizing History: The Polish System
  For the Polish educator Antoni Jażwiński, history was best represented by an abstract grid — or at least it was for the purposes of remembering it. The so-called “Polish System” originated in the 1820s and was later brought to public attention in the 1830s and 1840s by General Józef Bem, a military engineer with a penchant for mnemonics…

.

Last Week's Newsletter's 3 Most Clicked Links

• Warning: Don’t get GPT-brained [Reddit]

• Principles for Autonomous System Design: OpenClaw Deep Dive

• How The Heck Does Shazam Work?

.
* Based on unique clicks.
** Please take a look at last week's issue #649 here.

Cutting Room Floor

• Planning for your midlife crisis (or Counterfactual Analysis with Bayesian Models: What Drives the Life Expectancy Gap?)

• My Thoughts on AI, Part 1: Fears, Opinions, and Mental Journey

• Reflections on AI-assisted Programming

• Time series forecasting with tabular foundation models

• Should You Trust the Netflix Top 10? A Statistical Analysis

.

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Hannah & Sebastian

Issue #649
April 30, 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

• The Difference Between “Replicable” and “Not replicable” is not Itself Scientifically Replicable
  Replication studies estimate the replicability rate of scientific results by aggregating binary verdicts of experiments. Exact replications are rarely attainable, so most replication sequences are non-exact. Experiments differ in ways that matter and do not share a single data-generating process. We formalize two statistical interpretations of non-exactness. In a shared latent rate (benchmark) model, experiments are exchangeable and depend on a common random replicability rate. In a conditionally independent rates (operational) model, each experiment has its own replicability rate drawn from a population distribution………The replication crisis, if there is one, cannot be established by the methods used to declare it….

• You can beat the binary search
  Binary search is a classic algorithm that efficiently locates a target value in a sorted array by repeatedly dividing the search interval in half…In C++, this is implemented by the std::binary_search function, which returns a boolean indicating whether the value is present…The popular Roaring Bitmap format uses arrays of 16-bit integers of size ranging from 1 to 4096. We sometimes have to check whether a value is present. We use a binary search…I wanted a faster approach. I had two insights…Virtually all processors today have data parallel instructions (sometimes called SIMD) that can check several values at once…The binary search checks one value at a time. However, recent processors can load and check more than one value at once…Thus, I created something I call the SIMD Quad algorithm. It is an efficient search algorithm for sorted arrays of 16-bit unsigned integers, combining a quaternary interpolation search with SIMD (Single Instruction, Multiple Data)…

• Principles for Autonomous System Design: OpenClaw Deep Dive
  In this talk, Alex Krentsel (UC Berkeley, NetSys Lab / Google Research) does a deep-dive into OpenClaw — a fully open-source autonomous AI agent system — and uses it as a lens to explore the emerging design principles behind truly autonomous agents. We’re in Phase 3 of the AI evolution: LLM + tool-use + dynamic tool discovery. The agents that exist today aren’t chatbots. They read your email, write code, schedule work, remember context across sessions, and spawn other agents. This talk breaks down exactly how that works….

What’s on your mind

This Week’s Poll:

.

Last Week’s Poll:

.

Data Science Articles & Videos

• Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement
  We propose a linear regression model for the estimation of human body measurements. The input to the model only consists of the information that a person can self-estimate, such as height and weight. We evaluate our model against the state-of-the-art approaches for body measurement from point clouds and images, demonstrate the comparable performance with the best methods, and even outperform several deep learning models on public datasets. The simplicity of the proposed regression model makes it perfectly suitable as a baseline in addition to the convenience for applications such as the virtual try-on. To improve the repeatability of the results of our baseline and the competing methods, we provide guidelines toward standardized body measurement estimation…
  

• Warning: Don’t get GPT-brained [Reddit]

  At my last role we had to move fast, so we relied on an LLM to help with a lot of the thinking and coding for us so we could focus on the business use case and managing meetings and stakeholders. The role was heavy on project management as well as development, research, and deployment so basically doing everything While I got good at scoping projects and managing them, my technical skills totally deteriorated in less than 1 year. It’s scary going back to problems I know I can solve and but have some brain fog when getting to the answer. If I could have gone slower, had more time to thinking about modeling/coding than I probably wouldn’t feel like this Don’t get GPT brained. You’ll have to crawl out of that pit eventually. Like technical debt but for your brain…

• There Will Be a Scientific Theory of Deep Learning
  In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properties and statistics of the training process, hidden representations, final weights, and performance of neural networks. We pull together major strands of ongoing research in deep learning theory and identify five growing bodies of work that point toward such a theory: (a) solvable idealized settings that provide intuition for learning dynamics in realistic systems; (b) tractable limits that reveal insights into fundamental learning phenomena; (c) simple mathematical laws that capture important macroscopic observables; (d) theories of hyperparameters that disentangle them from the rest of the training process, leaving simpler systems behind; and (e) universal behaviors shared across systems and settings which clarify which phenomena call for explanation…
  

• What do the fundamental constants of physics tell us about life?
  In the 1970s, the renowned physicist Victor Weisskopf famously developed a research program to qualitatively explain properties of matter in terms of the fundamental constants of physics. But there was one type of matter prominently missing from Weisskopf’s analysis: life. Here, we develop Weisskopf-style arguments demonstrating how the fundamental constants of physics can be used to understand the properties of living systems. By combining biophysical arguments and dimensional analysis, we show that vital properties of chemical self-replicators, such as growth yield, minimum doubling time, and minimum power consumption in dormancy, can be quantitatively estimated using fundamental physical constants. The calculations highlight how the laws of physics constrain chemistry-based life on Earth, and if it exists, elsewhere in our universe…
  

• Gaussian Process Regression (GPR) & Physics-driven Computing
  Gaussian Process Regression (GPR) is a non-parametric, Bayesian approach to regression. Unlike parametric models (like linear regression) where we find a distribution over parameters, a Gaussian Process defines a prior distribution over functions…
  

• Open Data Structures - An open content textbook
  Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs…Data structures presented in the book include stacks, queues, deques, and lists implemented as arrays and linked-lists; space-efficient implementations of lists; skip lists; hash tables and hash codes; binary search trees including treaps, scapegoat trees, and red-black trees; integer searching structures including binary tries, x-fast tries, and y-fast tries; heaps, including implicit binary heaps and randomized meldable heaps; graphs, including adjacency matrix and ajacency list representations; and B-trees…


• PostgreSQL is Not Slow. Your Queries Are.
  A field guide to the seven things that are actually making our database feel slow and how to stop blaming the wrong suspect…Culprit #1: The Missing Index…Culprit #2: The N+1 Query, Death by a Thousand Cuts…Culprit #3: Stale Statistics, The Planner Is Flying Blind…Culprit #4: The Query That Runs Fine, Alone…Culprit #5: EXPLAIN ANALYZE Exists, Use It…Culprit #6: Connection Exhaustion, PostgreSQL is Full…Culprit #7: Reporting Queries Running on Production…
  

• An interactive semantic map of the latest 10 million published papers
  I built a map to help navigate the complex scientific landscape through spatial exploration. How it works: Sourced the latest 10M papers from OpenAlex and generated embeddings using SPECTER 2 on titles and abstracts. Reduced dimensionality with UMAP, then applied Voronoi partitioning on density peaks to create distinct semantic neighborhoods. The floating topic labels are generated via custom labelling algorithms (definitely still a work in progress!). There is also support for both keyword and semantic queries, and there’s an analytics layer for ranking institutions, authors, and topics etc…
  

• How The Heck Does Shazam Work?

  How audio fingerprinting and a connect-the-dots trick lets Shazam identify a song in seconds…By throwing away almost everything and keeping only a handful of landmark peaks, a noisy 5-second clip from a coffee shop becomes a set of coordinates precise enough to pinpoint one song out of millions. Recognition, it turns out, is mostly an exercise in ignoring the right things…
  

• Generating Correlated Random Numbers in R Using Matrix Methods
  Generating random data with a specific correlation structure is a common need in statistical simulation. In this post, let’s walk through how to do it in R using matrix decomposition methods…
  

• Schotter Plots in R

  Translating things between languages reveals how each language approaches different design trade-offs, and I believe it’s a useful exercise. Having something to translate is the first step. I found a plot I wanted to generate (Georg Nees’ “Schotter” computer-generated art from 1968 which shows a grid of squares which get increasingly displaced in position and rotation), and some code that reproduced it, so off we go!…
  

• Why Most Time Series Models Fail Before They Start
  A practical look at stationarity and transformations with real CPI data in R…Many time series models fail before they even begin. Not because the software crashes. Not because the code is wrong. But because the data entering the model violate one of the most important assumptions in time series analysis: stationarity…The goal is simple: show why raw time series levels often mislead us, what stationarity really means, and why transformations such as differencing and log-differencing are not cosmetic tricks but conceptual necessities…
  

• Hourly Subway Station Flows
  Pie charts are bad, as any fule kno. We’re not as good at judging relative differences between angles and areas as we are at judging relative differences in lengths on a common baseline. This is especially true when we have more than two things to compare at the same time. So, as a rule, you shouldn’t use them. You should figure out some other way of viewing your data instead. On the other hand, I just made 424 animated pie charts because if you’re going to break a rule you should break it good and hard…

.

Last Week's Newsletter's 3 Most Clicked Links

• Your Name in Landsat 🛰️

• CEO cancels BI tooling, replaces it with AI, breaks everything [Reddit]

• Anyone else paranoid using AI for analysis? [Reddit]

.
* Based on unique clicks.
** Please take a look at last week's issue #648 here.

Cutting Room Floor

• How The Heck Does GPS Work?

• Does Postgres Scale?

• How do you test AI agents in production? The unpredictability is overwhelming. [Reddit]

• Do Loyalty Programs Actually Create Loyalty?

• logrittr: A Verbose Pipe Operator for Logging dplyr Pipelines

• A Data-Driven Survey of MLB Franchise Management - A search for factors leading to playoff success

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,500 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Issue #648
April 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.

Sponsor Message

Online Data Science Programs from Drexel University

Find your algorithm for success with an online data science degree from Drexel University. Gain essential skills in tool creation and development, data and text mining, trend identification, and data manipulation and summarization by using leading industry technology to apply to your career. Learn more.

.

* Want to sponsor the newsletter? Email us for details --> team@datascienceweekly.org

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

Editor's Picks

• Your Name in Landsat 🛰️
  Type your name and see it spelled out in stunning Landsat satellite imagery. Explore Earth from space, letter by letter, with NASA and USGS Landsat images…

• Trajectory shapes
  Why are we managing our coding agents based on vibes instead of their actual work habits?…I analyzed the latest SWE-Bench Pro trajectories I could find: runs from October 2025 for Sonnet 4.5 and GPT-5, 730 task trajectories per model. I deterministically classified each step into activities like understand, edit, verify, and cleanup using only tool calls, literal command/filename matches, and regex heuristics, and then computed their share over time. This “trajectory shape” chart I got as a result is very interesting!…If I were to condense the above to “vibes”, I might say “Claude starts editing early and figures it out in the loop. GPT reads first, then goes for the one-shot.” That is also close to what people on my timeline were saying when these runs were current. For example…

• Build yourself flowers
  This is an edited transcript of the keynote I gave at the Applied Machine Learning Conference in Charlottesville, VA in April 2026…I’m Vicki, and I build machine learning systems…I debated for a long time how to introduce myself. Am I a data scientist? Am I still a machine learning engineer? Am I an AI engineer now? I’m not really sure. I think, like a lot of people over the past six months in the industry, I’ve been having existential angst. So, I’ll go with “I build machine learning systems.”...The larger question behind my existential angst is, what is the state of machine learning engineering as an industry today? That is - is it still worth doing machine learning engineering?…And the second question that came to me was, not only is it still worth doing ML, but, in an era where we’re having LLMs generate a lot of code, when the most important thing is for us to ship quickly, to get to a prototype quickly, is it still worth doing machine learning well?…the same question of, in a world where it’s easy and fast to write code, why is technical excellence still important?…

What’s on your mind

This Week’s Poll:

.

Last Week’s Poll:

.

Data Science Articles & Videos

• Columnar Storage is Normalization
  Something I didn’t understand for a while is that the process of turning row-oriented data into column-oriented data isn’t a totally bespoke, foreign concept in the realm of databases. It’s still of the relational abstraction. Or can be. As an example, say we have this data…
  

• Anyone else paranoid using AI for analysis? [Reddit]

  With the LLM in the loop, I touch the data less, and I catch less.

  1. Do you also feel one step removed from the data compared to before these tools existed?

  2. What are you doing to safeguard and double-check AI-assisted analysis?

  3. Has AI-assisted analysis ever caused you to ship a wrong number to a stakeholder? What happened?…

• A Tutorial on Thompson Sampling
  Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance. The algorithm addresses a broad range of problems in a computationally efficient manner and is therefore enjoying wide use. This tutorial covers the algorithm and its application, illustrating concepts through a range of examples, including Bernoulli bandit problems, shortest path problems, product recommendation, assortment, active learning with neural networks, and reinforcement learning in Markov decision processes…
  

• On Rejection and Competition
  This blog post will cover two topics. First, how to deal with the emotional feeling of being rejected for a role. Second, a re-framing and discussion of how to feel about the person who did get the role…
  

• No raw data, no science: another possible source of the reproducibility crisis
  As an Editor-in-Chief of Molecular Brain, I have handled 180 manuscripts since early 2017 and have made 41 editorial decisions categorized as “Revise before review,” requesting that the authors provide raw data. Surprisingly, among those 41 manuscripts, 21 were withdrawn without providing raw data, indicating that requiring raw data drove away more than half of the manuscripts…
  

• Learning to Maximize Rewards via Reaching Goals
  Goal-conditioned reinforcement learning learns to reach goals instead of optimizing hand-crafted rewards. Despite its popularity, the community often categorizes goal-conditioned reinforcement learning as a special case of reinforcement learning. In this post, we aim to build a direct conversion from any reward-maximization reinforcement learning problem to a goal-conditioned reinforcement learning problem, and to draw connections with the stochastic shortest path framework. Our conversion provides a new perspective on the reinforcement learning problem: maximizing rewards is equivalent to reaching some goals….


• Marathon Man: how to pace a marathon
  Let’s take a look at a big dataset of marathon times – we’ll use the New York City Marathon from 2025 – to see if we can understand how to pace a marathon. There’s an available dataset of chip times (meaning we don’t have to worry about dodgy GPS data) and the course has similar first and second half profiles, allowing us to use these times to understand negative/even/positive splitting. Let’s dive in….
  

• Using R to Teach R: Lessons for Software Development
  If you have read our blogs previously then you will be aware that Jumping Rivers is a consultancy and training provider in all things data science. But did you know that we offer over 50 different courses spanning R, Python, Git, SQL and more?…In this blog we will provide a glimpse into our internal process and share how we have streamlined the task of maintaining so many courses. Along the way we will share some good practices applicable to any big coding project, including packaging of source code and automated CI/CD…
  

• Machine Learning Frameworks in R

  R’s ecosystem offers a rich selection of machine learning frameworks, each with distinct design philosophies and strengths. This post is a side-by-side comparison of five ML frameworks in R that provide unified interfaces over multiple algorithms, with runnable code examples on the same dataset so you can compare APIs directly. The focus is on packages that let you swap algorithms without rewriting your code…
  

• Creating a future-proof enterprise agentic platform architecture
  A difficult (though familiar) set of questions:

  • How do we capture short-term impact without creating waste or long-term technical debt?

  • What architecture best supports a mesh of AI agents and traditional, deterministic capabilities across workflows?

  • Which services and capabilities do we need to build now to support what we’ll need in two years?

  • Is it even worth building today when a viable market solution may exist in a couple of years?

  • How do we balance speed with control, and set standards for security, traceability, and observability in an agentic system that’s non-deterministic?

  There are no universal answers to these questions yet. But through our work with three financial institutions deploying agentic systems at scale, we have developed a set of practical approaches that define what matters most, where teams risk over-investment, and what tends to be harder than expected…
  

• The Broader Landscape of Robustness in Algorithmic Statistics

  The last decade has seen a number of advances in computationally efficient algorithms for statistical methods subject to robustness constraints. An estimator may be robust in a number of different ways: to contamination of the dataset, to heavy-tailed data, or in the sense that it preserves privacy of the dataset. We survey recent results in these areas with a focus on the problem of mean estimation, drawing technical and conceptual connections between the various forms of robustness, showing that the same underlying algorithmic ideas lead to computationally efficient estimators in all these settings…
  

• CEO cancels BI tooling, replaces it with AI, breaks everything [Reddit]
  This happen with a client a coupla months ago. they had their dashboards in metabase, he cancelled > handed the team claude > “dashboards are a waste and just go and ask ai”. as you can guess he then called me saying he thinks he broke sth….heard almost the same story from another data consultant last week. different company, same swap, same outcome is this becoming a pattern or if we just both got unlucky clients?…
  

• Six Characters
  What the PNR locator on your boarding pass actually contains, and why the fare calculation line on your e-ticket is written in a currency that does not exist…

.

Last Week's Newsletter's 3 Most Clicked Links

• Why Traditional Statistics Still Matters in the Age of AI

• What is a data analysis mistake you made early in your career that you will never make again? [Reddit]

• Getting Into AI Infra

.
* Based on unique clicks.
** Please take a look at last week's issue #647 here.

Cutting Room Floor

• Can you reverse engineer our (Jane Street) neural network?

• Engineering Architecture: A Syllabus?

• Test & Roll: Why Smaller A/B Tests Can Make More Money

• Apps and programming: two accidental tyrannies

• The Living Lattice - Explorables of the intelligence theorem.

• Agent Skills for Google products and technologies, including Google Cloud.

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,500 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Issue #647
April 16, 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.

Sponsor Message

Online Data Science Programs from Drexel University

Find your algorithm for success with an online data science degree from Drexel University. Gain essential skills in tool creation and development, data and text mining, trend identification, and data manipulation and summarization by using leading industry technology to apply to your career. Learn more.

.

* Want to sponsor the newsletter? Email us for details --> team@datascienceweekly.org

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

Editor's Picks

• Estimating π with a Coin
  We describe a simple Monte Carlo method for estimating π by tossing a coin. Although the underlying Catalan-number series identities appear implicitly in the probability theory literature, the interpretation of π/4 presented here seems to be new…

• UniGeoCLIP - Unified Geospatial Contrastive Learning
  Geospatial understanding requires reasoning across fundamentally different kinds of data -- a satellite view from above, a street photo at ground level, a 3D elevation map, a text description of a neighborhood, and a pair of GPS coordinates. These modalities are complementary: each one captures something the others miss…UniGeoCLIP is the first contrastive framework to jointly align all five modalities into a single unified embedding space, enabling seamless retrieval and reasoning across any combination of inputs, without relying on a privileged “pivot” modality…

• Getting Into AI Infra
  My next blog post will be about my 10-year Silicon Valley AI tour of duty — NVIDIA -> Tesla AI -> X -> DeepMind -> OpenAI…but I wanted to start with something more practical: how to get into AI infra. I joined NVIDIA in 2016, back when the Deep Learning Systems team still fit in one room. “AI infra” was not really a ‘thing’ yet. When clmt started the ‘AI Infra’ org I was the first engineer to join. This post is my version of how I made my way there, and a fun way to catch up…

What’s on your mind

This Week’s Poll:

.

Last Week’s Poll:

.

Data Science Articles & Videos

• Are predictive models enough?
  In one of the social media discussions about causal inference, the suggestion was made that predictive models are all you need: a good predictive model gives you all the conditional distributions you could want, and you don’t need any special causal inference stuff…I think there’s something to this point of view, but there are a few limitations…
  

• How many production ML/AI projects do you complete in a year? [Reddit]

  Wondering what it looks like at other companies. I usually deliver around 3 or 4 ML/AI projects each year. I’m also expected to do multiple analyses separate from this so I’m not only focused on ML/AI. We have a small team of 7 people and we rarely collaborate on projects. What is it like at your company?…

• On creating ‘new knobs of control’ in biology
  How can we regain more control over our poorly built physiology? Or, in other words, how we install more ‘knobs of control’? I had this question too! It turns out there’s a lot of new emerging therapeutic modalities that fit this criteria, and I decided to turn my research over them into an essay…
  

• A Better R Programming Experience Thanks to Tree-sitter
  R tooling around Tree-sitter is how you get

  • reformatting through Air and linting through Jarl;

  • auto-completion or help on hover in the Positron IDE;

  • better search for R on GitHub;

  • and more!

  In this post, we’ll explain what Tree-sitter is, and how tools built on Tree-sitter can benefit your R development workflow…
  

• Why Traditional Statistics Still Matters in the Age of AI
  In this episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to make the case for why rigorous statistical thinking remains indispensable in the age of AI, and what data scientists are giving up when they abandon it….In this episode, you’ll discover:

  1. Why throwing data at an LLM is no substitute for building a model that understands the problem

  2. How combining classical statistics and machine learning can produce better forecasting results than either approach alone

  3. What data scientists lose when they stop thinking probabilistically - and why it matters for decision making

  4. Where to start if you want to strengthen your statistical foundations…
     

• Raft is so fetch: The Raft Consensus Algorithm explained through “Mean Girls”
  Raft is a consensus algorithm used in distributed systems to ensure that data is replicated safely and consistently. That sentence alone can be confusing…In fact, I’ve seen conversations recently on social media in which actual technical leaders of infrastructure companies demonstrate a lack of understanding (!). Point being, you’re not alone. Get in, losers, we’re going back to (Hollywood) high school…


• Succinct Data Structures: Cramming 80,000 words into a Javascript file
  Let’s continue our short tour of data structures for storing words. Today, we will over-optimize John Resig’s Word Game. Along the way, we shall learn about a little-known branch of computer science, called succinct data structures….
  

• Components of A Coding Agent
  In this article, I want to cover the overall design of coding agents and agent harnesses: what they are, how they work, and how the different pieces fit together in practice. Readers of my Build a Large Language Model (From Scratch) and Build a Large Reasoning Model (From Scratch) books often ask about agents, so I thought it would be useful to write a reference I can point to…In this article, I lay out six of the main building blocks of a coding agent…
  

• State of AI: April 2026 newsletter

  Welcome to the latest issue of the State of AI, an editorialized newsletter that covers the key developments in AI policy, research, industry, and start-ups from February 1 to April 7, 2026…
  

• Regression should predict full distributions
  While approaches like linear regression can output full predictive distributions, these often come with (too) strong distributional assumptions. What if we always worked with machine learning models that produce the full predictive distribution? With classification, we are already at this point: Modern machine learning approaches output not just the majority class, but a probability for each class. Whether this probability is calibrated is another question. With regression, we are a bit stuck with a point-based mindset. However, this could change with tabular foundation models. At least in theory: While these models produce the full predictive distribution (or at least a discretized approximation over a fixed support), it’s not the default and the output is a bit hidden…
  

• Maze Classification

  Mazes in general (and hence algorithms to create Mazes) can be organized along seven different classifications. These are: Dimension, Hyperdimension, Topology, Tessellation, Routing, Texture, and Focus. A Maze can take one item from each of the classes in any combination…
  

• What is a data analysis mistake you made early in your career that you will never make again? [Reddit]
  For those who are working as data analysts or learning analytics, what’s one mistake you made early on that taught you a big lesson? Could be technical, communication, dashboards, SQL, Excel, anything. I think beginners like me could learn a lot from real experiences…
  

• What I Learned Building Attention Residuals from Scratch
  Naively reimplementing a paper in PyTorch changed how I think about how transformers route information, and about the gap between academic math and physical silicon…I wanted to understand how transformers actually route information between layers. Not at the level of “attention computes weighted averages,” but at the level of what physically happens to a tensor as it moves through the network. What gets preserved, what gets overwritten, and why….

.

Last Week's Newsletter's 3 Most Clicked Links

• Built a dashboard to analyze how AI skills are showing up in data science job postings (open source) [Reddit]

• The Git Commands I Run Before Reading Any Code

• Agentic Data Science Done Right: Why AI Coding Agents Make Bad Analytical Decisions - And How to Fix It

.
* Based on unique clicks.
** Please take a look at last week's issue #646 here.

Cutting Room Floor

• What’s you recommendation to get interview ready again the fastest? [Reddit]

• How to break free from LLM’s chains as a PhD student? [Reddit]

• How HTTPS Works

• legos vs 3d printers

• Guest Lecture by Eric Wallace of OpenAI on hardening LLMs

• Why is Everyone’s Robot Folding Clothes?

• Lyra 2.0: Explorable Generative 3D Worlds

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,500 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Issue #646
April 09, 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

• Fast and Gorgeous Erosion Filter
  This blog post and the companion video both explain an erosion technique I’ve worked on over the past eight months. The video has lots of elaborate animated visuals, and is more focused on my process of discovering, refining, and evolving the technique, while this post has a bit more implementation details on the final iteration….

• AI Assistance Reduces Persistence and Hurts Independent Performance
  Through a series of randomized controlled trials on human-AI interactions (N = 1,222), we provide causal evidence for two key consequences of AI assistance: reduced persistence and impairment of unassisted performance. Across a variety of tasks, including mathematical reasoning and reading comprehension, we find that although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up. Notably, these effects emerge after only brief interactions with AI (approximately 10 minutes). These findings are particularly concerning because persistence is foundational to skill acquisition and is one of the strongest predictors of long-term learning…

• Ask HN: Any interesting niche hobbies?
  I’m looking for something novel and interesting, that isn’t absolutely crowded that I could meaningfully contribute to…

What’s on your mind

This Week’s Poll:

.

Last Week’s Poll:

.

Data Science Articles & Videos

• What Category Theory Teaches Us About DataFrames
  Every dataframe library ships with hundreds of operations. pandas alone has over 200 methods on a DataFrame…Without a framework for telling them apart, you end up memorizing APIs instead of understanding structure…I ran into this question while building my own dataframe library. I needed to decide which operations were truly fundamental and which were just surface-level variations. That search led me to Petersohn et al.’s Towards Scalable Dataframe Systems…They analyzed 1 million Jupyter notebooks, cataloged how people use pandas, and proposed a dataframe algebra: a formal set of about 15 operators that can express what all 200+ pandas operations do…
  

• Built a dashboard to analyze how AI skills are showing up in data science job postings (open source) [Reddit]

  I’ve been scraping thousands of U.S. data science jobs for the past couple of months and writing about the findings in my newsletter…At some point, I figured the dashboard was more useful than anything I was writing, so I decided to open-source it…Here’s what it covers:

  • Top skills companies are actually hiring for, ranked by frequency

  • Skills broken down by category (ML/DL, GenAI, Cloud, MLOps, etc.)

  • What % of roles now require AI skills, broken down by seniority level

  • Salary premium for candidates with AI skills

  • An interactive explorer where you can browse individual postings with matched skills highlighted

  The skill extraction is built on around 230 curated keyword groups, so it’s pretty granular…

• Modern SQLite: Features You Didn’t Know It Had
  Working with JSON data…Full-text search with FTS5…Analytics with window functions and CTEs…Strict tables and better typing…Generated columns for derived data…Write-ahead logging and concurrency…
  

• Quantization from the ground up
  Qwen-3-Coder-Next is an 80-billion-parameter model, 159.4 GB in size. That’s roughly how much RAM you would need to run it, and that’s before thinking about long context windows. This is not considered a big model. Rumors have it that frontier models have over 1 trillion parameters, which would require at least 2TB of RAM. The last time I saw that much RAM in one machine was never. But what if I told you we can make LLMs 4x smaller and 2x faster, enough to run very capable models on your laptop, all while losing only 5-10% accuracy. That’s the magic of quantization…
  

• The Git Commands I Run Before Reading Any Code
  Five git commands that tell you where a codebase hurts before you open a single file. Churn hotspots, bus factor, bug clusters, and crisis patterns….The first thing I usually do when I pick up a new codebase isn’t opening the code. It’s opening a terminal and running a handful of git commands. Before I look at a single file, the commit history gives me a diagnostic picture of the project: who built it, where the problems cluster, whether the team is shipping with confidence or tiptoeing around land mines….
  

• Six (and a half) intuitions for KL divergence
  KL-divergence is a topic which crops up in a ton of different places in information theory and machine learning, so it’s important to understand well. Unfortunately, it has some properties which seem confusing at a first pass (e.g. it isn’t symmetric like we would expect from most distance measures, and it can be unbounded as we take the limit of probabilities going to zero). There are lots of different ways you can develop good intuitions for it that I’ve come across in the past. This post is my attempt to collate all these intuitions, and try and identify the underlying commonalities between them…


• Semantic Search Without Embeddings
  When I need to stay warm, I search for “long johns” or “long underwear”. But modern outdoors clothing stores label this a “base layer”. For a waterproof jacket I search for “slickers” or a “ski jacket”, not realizing what I should search for is a “shell.” Despite my outdated terminology, search still works. I’m somehow understood and shown the right content. We call this semantic search. When you hear that, you might think embeddings. Today I want to stretch your thinking beyond…In semantic search, content and query map to a shared representation. This space has a similarity function that scores similar items higher than less similar. Let’s walk through an example…
  

• Agentic Data Science Done Right: Why AI Coding Agents Make Bad Analytical Decisions - And How to Fix It
  AI coding agents write correct code. They make unreliable analytical decisions. That gap between syntactically valid code and statistically sound conclusions is where costly mistakes are made. Decision Lab is an open-source framework that closes this gap by encoding domain expertise, multi-path exploration, and Bayesian uncertainty quantification into every agent run…
  

• Eight years of wanting, three months of building with AI

  For eight years, I’ve wanted a high-quality set of devtools for working with SQLite. Given how important SQLite is to the industry, I’ve long been puzzled that no one has invested in building a really good developer experience for it…A couple of weeks ago, after ~250 hours of effort over three months on evenings, weekends, and vacation days, I finally released syntaqlite (GitHub), fulfilling this long-held wish. And I believe the main reason this happened was because of AI coding agents…there’s no shortage of posts claiming that AI one-shot their project or pushing back and declaring that AI is all slop. I’m going to take a very different approach and, instead, systematically break down my experience building syntaqlite with AI, both where it helped and where it was detrimental. I’ll do this while contextualizing the project and my background so you can independently assess how generalizable this experience was. And whenever I make a claim, I’ll try to back it up with evidence from my project journal, coding transcripts, or commit history…
  

• Land development math is alchemy: Why cheap land is cheap & how to turn it into gold
  Friends often send me links to beautiful parcels of land with prices that are too good to be true…When we look into the underlying development potential, it quickly explains the price: you’re not allowed to build much on it. The land isn’t “entitled.” Entitlement is the legal process by which raw land gets permission to be developed — in California, it’s long, expensive, uncertain, and controlled by local governments. Cheap land is typically cheap because the market has priced in how risky it is to get that permission. The discount isn’t a bargain; it reflects the work (and luck) required to unlock the value. But if you do succeed in entitling land for significant development, its value shoots up to reflect the new permitted supply (assuming the underlying market is strong). I call this “entitlement alchemy”. The dirt doesn’t move, but the piece of paper saying “you may build here” can double the value overnight. In supply-constrained markets, permission to build is a huge portion of the land’s value…
  

• stbl

  R is flexible about classes. Variables are not declared with explicit classes, and arguments of the “wrong” class don’t cause errors until they explicitly fail at some point in the call stack. It would be helpful to keep that flexibility from a user standpoint, but to error informatively and quickly if the inputs will not work for a computation. The purpose of stbl is to allow programmers to specify what they want, and to then see if what the user supplied can work for that purpose.

  This approach aligns with Postel’s Law:

  “Be conservative in what you send. Be liberal in what you accept from others.”

  stbl is liberal about what it accepts (by coercing when safe) and conservative about what it returns (by ensuring that inputs have the classes and other features that are expected)…
  

• Those of you with 10+ years in ML — what is the public completely wrong about? [Reddit]
  For those of you who’ve been in ML/AI research or applied ML for 10+ years — what’s the gap between what the public thinks AI is doing vs. what’s actually happening at the frontier? What are we collectively underestimating or overestimating?…
  

• Cogsci Book Summaries
  A reading list of 79 books about cognitive science and its application to the classroom…For each book there is a pdf of my summary…

.

Last Week's Newsletter's 3 Most Clicked Links

• The Revenge of the Data Scientist

• Learn Claude Code by doing, not reading

• 100 Economics Papers to Inspire Wonder

.
* Based on unique clicks.
** Please take a look at last week's issue #645 here.

Cutting Room Floor

• ML promises to be profoundly weird - The Future of Everything is Lies, I Guess

• A distributed systems reliability glossary

• GuppyLM A ~9M parameter LLM that talks like a small fish

• MIT16.485 - Visual Navigation for Autonomous Vehicles

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,000 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Issue #645
April 02, 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.

This Week in Data Science Weekly (Paid)

Each week, paid subscribers get 3 additional deep dives to help you. Here are the last three:

• Understanding core statistics

  • When Data Isn’t Normal

• Build with modern AI tools

  • What Makes an AI Agent “Autonomous”?

• Career Accelerator (get paid more and be better at your job)

  • The 10-Minute Sunday Habit That Makes Your Week Easier

If you’ve been reading for a while, this is where things go deeper.

👉 Get all 3 posts each week → Upgrade for $10/month

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

Editor's Picks

• The Revenge of the Data Scientist
  Training models was never most of the job. The bulk of the work is setting up experiments to test how well the AI generalizes to unseen data, debugging stochastic systems, and designing good metrics. Calling an LLM over an API does not make this work go away…I recently gave a talk titled “The Revenge of the Data Scientist” at PyAI Conf to make that case with examples rather than assertions alone. Below is an annotated version of that presentation…

• 100 Economics Papers to Inspire Wonder
  Making a list of a hundred papers in economics that fill me with wonder, joy, and excitement…

• James H. Simons, PhD: Using Mathematics to Make Money
  In September 2022, James Simons spoke with members of the Journal of Investment Consulting editorial board about how his experience as a mathematician prepared him for success in the financial world; the main ingredients of his quantitative investment firm’s success—building and continuously improving investment models through regular testing; hiring excellent scientists who have decided they want to make money rather than people with previous experience in finance; and fostering collaboration among the company’s workforce…

What’s on your mind

This Week’s Poll:

.

Last Week’s Poll:

.

Data Science Articles & Videos

• Seeing like a spreadsheet
  You cannot really understand the transformation of the American economy over the last few decades without understanding the spreadsheet. This is a story about how a piece of software transformed the way that American businesses understood themselves, and how they were understood by others; how it enabled the rise of financial engineering and the entire apparatus of Wall Street dealmaking; how it helped reshape the American corporation from an organization that built things into an organization that optimized numbers; and how it offers us a lesson, and a warning, about how artificial intelligence will transform economic life…

• Bayesian Statistics Future Relevance [Reddit]

  I have been interested in Bayesian statistics for a long time and would like to do some research in it on an applied project. However, I was wondering how relevant it is/is going to be in the future? Genuinely asking as I have no idea. I am interested in doing some work in advanced Bayesian hierarchical models. Would doing more stuff in ML/AI be more beneficial for trying to get a job in industry or is Bayesian work going to be sought after?…

• Machine Learning Visualized
  Book of Jupyter Notebooks that implement and mathematically derive machine learning algorithms from first-principles. The output of each notebook is a visualization of the machine learning algorithm throughout its training phase, ultimately converging at its optimal weights…
  

• AI badly needs a dose of skepticism - Some scientists are too eager to believe their own claims
  I explain some of my reasons for being deeply skeptical about AI models that claim to understand DNA, genes, and genomes…
  

• How to Build a Scalable Serverless Social Media Ingestion & Analytics Pipeline on AWS
  In this post, we explain how to build a scalable, cost-efficient, and serverless data pipeline on AWS to ingest, process, and visualize social media data…
  

• Regulating AI Agents
  The European Union’s AI Act - promulgated prior to the development and widespread use of AI agents, the EU AI Act faces significant obstacles in confronting the governance challenges arising from this transformative technology, such as performance failures in autonomous task execution, the risk of misuse of agents by malicious actors, and unequal access to the economic opportunities afforded by AI agents. We systematically analyze the EU AI Act’s response to these challenges, focusing on both the substantive provisions of the regulation and, crucially, the institutional frameworks that aim to support its implementation…


• Autograd and Mutation
  How does PyTorch autograd deal with mutation? In particular, what happens when a mutation occurs on a view, which aliases with some other tensor? In 2017, Sam Gross implemented support for in-place operations on views, but the details of which have never been described in plain English… until now…
  

• genStats: Standardizing Experimentation Analysis at Scale
  CarGurus runs hundreds of A/B tests annually to improve every aspect of our marketplace. Because the car-buying journey is complex and touches every part of our platform, we validate product changes through qualitative insights (consumer interviews) and quantitative experimentation (A/B tests)…genStats is an internal framework orchestrated in Python that standardizes A/B test analysis across CarGurus. It automates notebook generation, centralizes statistical logic, and packages domain-specific metrics into reusable “metric families,” addressing the limitations listed above. A Metric Family is a collection of standardized queries. Analysts contribute the queries for the metrics they specifically own. This approach codifies domain expertise, turning individual knowledge into a shared tool…
  

• Modern Probability Modeling - A Tools Approach

  These are notes for my class on probability models. In these notes, I walk through the concepts and computations that support modern probability modeling in political science using both maximum likelihood and Bayesian approaches…
  

• bananarama
  bananarama generates presentation images using Google Gemini. Define your images in a YAML configuration file with support for reference images and style defaults. Using gemini to generate slide images usually means a tedious loop of copying prompts into a web UI, downloading images, tweaking, and repeating. bananarama makes this process reproducible: your prompts live in version-controlled YAML, and regenerating every image is a single function call. No more copy-paste, no more losing track of which prompt produced which image. It also generates all images in parallel, making it wicked fast to generate full deck of images…
  

• Learn Claude Code by doing, not reading.

  11 interactive modules with terminal simulators, config builders, and quizzes. No setup required…
  

• Is Hip-Hop in Decline? A Statistical Analysis
  Today we’ll explore hip-hop’s meteoric rise and supposed fall, the genres gaining traction in recent years, and the forces that shape music popularity…
  

• Introduction to Online Control
  This text presents an introduction to an emerging paradigm in control of dynamical systems and differentiable reinforcement learning called online nonstochastic control. The new approach applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control…

.

Last Week's Newsletter's 3 Most Clicked Links

• Bayesian statistics for confused data scientists

• If DSPy is So Great, Why Isn’t Anyone Using It?

• So where are all the AI apps?

.
* Based on unique clicks.
** Please take a look at last week's issue #644 here.

Cutting Room Floor

• An NSFW filter for Marginalia Search

• Optimization Models in Engineering - a course reader

• ggpup: place two random dog images next to any ggplot object

.

Whenever you're ready, 3 ways we can help:

1. Go deeper each week (paid subscription)
   Get 3 additional posts per week designed to help you:

   • Statistics → understand the math behind ML

   • AI Agents → build with modern AI tools

   • Career → become more valuable at your job

   👉 Upgrade for $10/month — cancel anytime
   

2. Looking to get a job?
   A practical guide to landing your first (or next) data science role, based on thousands of reader questions.
   👉 Check out our “Get A Data Science Job” Course
   

3. Promote your organization/project/event to ~68,000 subscribers
   Sponsor this newsletter and reach a highly engaged data science audience (30–35% open rate).
   👉 Reply to this email to learn more

Thank you for joining us this week! :)

Stay Data Science-y!

All our best,
Hannah & Sebastian

Read more

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