ABOUT THIS FEED
Towards Data Science (TDS) is one of the largest AI and data science communities on Medium, hosting contributions from thousands of practitioners, researchers, and enthusiasts worldwide. Its RSS feed captures a wide variety of articles, ranging from beginner-friendly tutorials to advanced explorations of machine learning theory and real-world applications. Since content comes from a diverse group of authors, readers can expect a broad range of perspectives, tools, and case studies. TDS is particularly strong for how-to guides, coding walkthroughs, and applied AI in areas like natural language processing, computer vision, and business analytics. It also includes thought pieces on ethics, careers, and future trends. While articles vary in depth, the community format ensures a steady stream of fresh ideas. The feed is ideal for self-learners and professionals alike, offering daily inspiration and practical learning opportunities.
- How to Effectively Review Claude Code Output
Get more out of your coding agents by making reviewing more efficient The post How to Effectively Review Claude Code Output appeared first on Towards Data Science.
- Self-Hosting Your First LLM
Privacy. Cost. Customization. Everything you need to know—step by step. The post Self-Hosting Your First LLM appeared first on Towards Data Science.
- Introducing Gemini Embeddings 2 Preview
One embedding model to rule them all The post Introducing Gemini Embeddings 2 Preview appeared first on Towards Data Science.
- How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment
Most neuro-symbolic systems inject rules written by humans. But what if a neural network could discover those rules itself? In this experiment, I extend a hybrid neural network with a differentiable rule-learning module that automatically extracts IF-THEN fraud rules during training. On the Kaggle Credit Card Fraud dataset (0.17% fraud rate), the model learned interpretable rules such as: The post How a Neural Network Learned Its Own Fraud Rules: A Neuro-Symbolic AI Experiment appeared first on Towards Data Science.
- Hallucinations in LLMs Are Not a Bug in the Data
It’s a feature of the architecture The post Hallucinations in LLMs Are Not a Bug in the Data appeared first on Towards Data Science.
- Follow AI Footpaths
Shadow AI and the desire paths of modern work The post Follow AI Footpaths appeared first on Towards Data Science.
- How to Build a Production-Ready Claude Code Skill
What I learned building and distributing my first Skill from scratch The post How to Build a Production-Ready Claude Code Skill appeared first on Towards Data Science.
- Bayesian Thinking for People Who Hated Statistics
You already think like a Bayesian. Your stats class just taught the formula before the intuition. Here's a 5-step framework to apply it at work. The post Bayesian Thinking for People Who Hated Statistics appeared first on Towards Data Science.
- The 2026 Data Mandate: Is Your Governance Architecture a Fortress or a Liability?
Is your data strategy 2026-ready? Get a deep dive into the mandatory shift toward human-in-the-loop oversight, active metadata, and the strategic advantages of European data sovereignty. The post The 2026 Data Mandate: Is Your Governance Architecture a Fortress or a Liability? appeared first on Towards Data Science.
- The Causal Inference Playbook: Advanced Methods Every Data Scientist Should Master
Master six advanced causal inference methods with Python: doubly robust estimation, instrumental variables, regression discontinuity, modern difference-in-differences, heterogeneous treatment effects and sensitivity analysis. Includes code and a practical decision framework. The post The Causal Inference Playbook: Advanced Methods Every Data Scientist Should Master appeared first on Towards Data Science.