Towards Data Science

The world's leading publication for data science and artificial intelligence professionals.

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[Towards Data Science @TDataScience]

"Many teams use interns or research spikes to explore ideas: is AI good enough now? Are these projects only about the final artifact?"

Don't miss @jacopotagliabue's new must-read on research projects in the age of AI. https://t.co/RdJD9ZeKiL

[Towards Data Science @TDataScience]

In a new article, Sam Black walks us through "a simple, fee-free method for cryptographically hashing a dataset of any size and storing its hash immutably on the Ethereum blockchain." https://t.co/RiT1bNOUnT

[Towards Data Science @TDataScience]

It's time to approach different coding assistant as complementary tools, not as adversaries — and @EivindKjos shows how to make the most of two of the most powerful ones (Claude Code and Codex) in his latest article.

https://t.co/BmLAnXg2mS

[Towards Data Science @TDataScience]

The role of a data scientist is changing faster than most people realize. Haden Pelletier highlights three Claude skills that are becoming essential in 2026.

https://t.co/X5KNVFMkrF

[Towards Data Science @TDataScience]

How do different tools perform on a set of complex OCR tasks? Ida Silfverskiöld created a comprehensive set of tests and reports on her findings, from the predictable to the counterintuitive. https://t.co/gxPWa7iV8X

[Towards Data Science @TDataScience]

"However, AI alone is not a danger. It is AI deployed inside an economy where workers have little leverage over how productivity gains are distributed."

Marco Baity-Jesi presents a nuanced argument in favor of fairness and equity in the transition towards an AI-powered economy. https://t.co/1oANNz2ZNC

[Towards Data Science @TDataScience]

For his debut TDS article, Anubhab Banerjee shares a comprehensive guide to optimizing LLM inference by eliminating padding overhead with hardware-aware sequence packing. https://t.co/hlISQpQJ91

[Towards Data Science @TDataScience]

What guardrails and limitations are absolutely essential when deploying AI agents in production? Sara Nobrega outlines the rules you must have in place to keep your agents safe and effective. https://t.co/Qm0HPAg5H3

[Towards Data Science @TDataScience]

"The team was lost, not stuck. RAG is not machine learning, and the ML toolkit solves the wrong problem."

Angela Shi unpacks the issues with tackling issues in your RAG system with a traditional machine learning approach. https://t.co/2aoK0lSTds

[Towards Data Science @TDataScience]

Do you know what the single most expensive misconception in enterprise RAG today is? According to Angela Shi, it's the notion that the traditional ML toolkit can apply to LLM-powered retrieval and generation. https://t.co/2aoK0lSTds

[Towards Data Science @TDataScience]

Claude Code or Codex?

For @EivindKjos, the real question is: why not Claude Code *and* Codex?

https://t.co/BmLAnXg2mS

[Towards Data Science @TDataScience]

Curious about the intersection of data science and blockchain technology? Read Sam Black's new writeup on the potential application of the Ethereum blockchain to dataset versioning, provenance, and integrity assurance. https://t.co/RiT1bNOUnT

[Towards Data Science @TDataScience]

What do we lose when we outsource research — and other similar, cognitively demanding tasks — to AI agents? @jacopotagliabue offers a nuanced and frank reflection on an emerging conundrum. https://t.co/RdJD9ZeKiL

[Towards Data Science @TDataScience]

Proof assistants are changing how programmers interact with mathematics. Ronen Lahat explores Lean through the perspective of software engineering and machine learning.

https://t.co/yHsbJsgslo

[Towards Data Science @TDataScience]

New Python tutorial alert! Mahnoor Javed uses old census data to extract meaningful insights with Pandas, Matplotlib, and Seaborn. https://t.co/NwP365x1D2

[Towards Data Science @TDataScience]

What happens next, now that the barriers to building with code have collapsed? Clara Chong shares incisive insights on the emerging bottlenecks for AI product development. https://t.co/qnTOjodwxL

[Towards Data Science @TDataScience]

Need to take your project from a locally hosted app to a publicly accessible website? No problem: here are three options to explore, courtesy of @taupirho. https://t.co/BF4Yc8FBnU

[Towards Data Science @TDataScience]

How should you go about matching a specific RAG technique to a particular use case? Angela Shi's comprehensive guide maps out the available options and explains how to choose among them. https://t.co/vm4aVkcC8t

[Towards Data Science @TDataScience]

Does the rise of agentic business intelligence tools threaten the future of data analysts? Hugo Lu digs into a thorny corner of the AI conversation. https://t.co/LMtseZUe1E

[Towards Data Science @TDataScience]

Bayesian inference meets a murder mystery in @Shubagana's latest article — don't miss this engaging foray into Netflix-inspired analysis. https://t.co/I6LkAw4coN