Towards Data Science @TDataScience
The world's leading publication for data science and artificial intelligence professionals. Submit an Article ✍️ https://t.co/57pIMegK1o towardsdatascience.com We're Global 🌏 Joined October 2016-
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"Linear Discriminant Analysis (LDA) is a supervised learning technique used to surface the core components, or patterns, in the data." If you're taking your first steps in machine learning, don't miss @carolinabento's beginner-friendly, hands-on introduction to LDA. towardsdatascience.com/linear-discrim…
"A text watermark isn’t a visible stamp. It’s a pattern of choices only you know." Chien Vu Minh unpacks the math and recent research behind watermarking, and examines its practical applications for end users. towardsdatascience.com/text-watermark…
"If you've spent any real stretch of time around multi-agent systems in production, you've either already seen a version of this, or you're going to eventually." @BenjaminNweke11 explains what to do when your system fails even though evaluation guardrails say it's working fune. towardsdatascience.com/why-most-multi…
Thinking of giving GPT-6 Astra a try? Here's everything you need to know about getting started, based on @EivindKjos's hands-on tinkering. towardsdatascience.com/how-to-maximiz…
"The best place to start is testing your critical assumption about your solution." Thuwarakesh Murallie shares the lessons he's learned from building an agent-based app that worked great — but cost too much to keep online. towardsdatascience.com/i-vibe-coded-a…
A bilingual household fridge inventory built in about four hours with Codex: upload a grocery receipt, confirm the items, and an old iPad by the fridge shows what to eat first. By Amy Ma towardsdatascience.com/shipai/project…
Curious about world models, how they work, and how to build one? Don't miss @maurodp_'s beginner-friendly guide. towardsdatascience.com/a-beginners-gu…
In a recent deep dive, Ananya Bhattacharyya explained how model validation standards are changing for LLM-based systems: what breaks, what carries over, and how to test output quality. towardsdatascience.com/the-model-vali…
From averages and control variables to sample sizes, Sara A. Metwalli walks us through 10 statistical traps data scientists often overlook, but shouldn't. towardsdatascience.com/10-statistical…
When a single Python pipeline becomes too clunky, slow, or convoluted, Priyansh Bhardwaj suggests you switch to multiple MCP services that can be deployed independently. towardsdatascience.com/when-one-proce…
"Most introductions to neural networks stay in function space: what the network computes, how an activation function bends a line into a curve. This article stays in parameter space: what the set of good solutions actually looks like, and what that geometry costs you." Ananya Bhattacharyya shares a deep dive into permutation symmetry in deep learning. towardsdatascience.com/the-symmetry-t…
"When I browse the online market for suitable roles during those times, one of the most in-demand skills I keep seeing is experience with a tool called dbt." @taupirho presents a thorough guide to the popular open-source SQL transformation tool. towardsdatascience.com/getting-starte…
AI is becoming part of the everyday data science workflow, but knowing how to use it effectively takes more than prompting. Haden Pelletier highlights four Claude skills worth adding to your toolkit in 2026. towardsdatascience.com/4-claude-skill…
How do you go about catching silent failures in your agentic coding pipelines? @ReyaVir presents a tool she built to help builders verify their app aligns with their intent. towardsdatascience.com/coding-agents-…
"Almost nobody has a line in the budget for the day the model changes and the whole system has to be proven correct again." Pratik Rupareliya unpacks the challenges of the model "re-qualification tax" and ways to plan around it. towardsdatascience.com/we-pinned-our-…
What does it take to launch a data science career in the age of AI? Stephanie Kirmer shares nuanced insights and observations based on over a decade of experience in the field. towardsdatascience.com/starting-a-car…
In a multi-agent system, how do you ensure agents commit to a task and prevent it from falling through the cracks? @emmimalpa explains how she built a commitment layer to preserve intent and follow-through. towardsdatascience.com/multi-agent-co…
A model can still look healthy by its metrics even when the conditions that made it valid have started to change. How long should something keep working before we stop questioning why it works? #DataScience #ai #MLOps #ModelRisk #science @TDataScience towardsdatascience.com/who-questions-…
Complexity may not be the biggest obstacle for AI debugging. Nhu Hoang explores how missing context and information can create blind spots across coding agents and debugging workflows. towardsdatascience.com/bug-detection-…
More context is not always the answer to unreliable AI agents. Emmimal P Alexander looks at how typed context can preserve the role of each piece of information throughout the agent workflow. towardsdatascience.com/ai-agents-dont…
Bojan Tunguz @tunguz
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Eivind K @EivindKjos
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