Data Science Tutorials
Learn data science and AI with step-by-step tutorials on the DataCamp blog. Master Python, SQL, machine learning, and build your own AI agents.
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Recursive Self-Improvement in AI: How It Works and Why It Matters
Learn what recursive self-improvement means in AI, how AI systems can help develop better AI, how close today's models are to RSI, and why the concept matters for AI capabilities and safety.
Vinod Chugani
September 17, 2026
Vision-Language-Action Models Explained: How Robots Learn to See, Understand, and Act
Learn how vision-language-action (VLA) models work, how they differ from VLMs, and how to choose between OpenVLA, pi0, and SmolVLA in 2026.
Vaibhav Mehra
September 16, 2026
How to Clear Formatting in Excel: A Step-by-Step Guide
Learn how to clear formatting in Excel using the Clear Formats tool, shortcuts, and other methods while keeping your data intact.
Jachimma Christian
September 16, 2026
Exogenous vs. Endogenous Variables: Key Differences and Examples
Learn the difference between exogenous and endogenous variables, why endogeneity matters for causal inference and regression, and how to identify and address endogenous variables.
Vinod Chugani
September 16, 2026
GPT-Live-1 API Tutorial: Build a Full-Duplex Voice Assistant
Follow this GPT-Live-1 API tutorial to build a full-duplex voice learning assistant with browser WebRTC, backend delegation, web search, and confirmed actions.
Khalid Abdelaty
September 15, 2026
Spearman’s Correlation: How to Quantify Nonlinear Relationships
Discover how Spearman's Rank Correlation captures relationships that curve, plateau, or shift pace instead of following a straight line. Learn to calculate, interpret, and apply it in Python, R, and Excel.
Amberle McKee
September 15, 2026
What Are AI World Models? How They Work and 2026 Trends
Discover what AI world models are, how they differ from large language models, and how they help AI predict the future. Explore the latest 2026 industry trends.
Vaibhav Mehra
September 14, 2026
Causal Machine Learning: From Prediction to Cause and Effect
Causal machine learning combines ML with causal inference to move beyond prediction and estimate what happens when you intervene. This article covers the core concepts, methods, Python tools, and common mistakes.
Dario Radečić
September 14, 2026
State Space Models: How They Work and Where They're Used
Learn how state space models represent dynamic systems using hidden states and observations, including the state and observation equations, Kalman filtering, and time-series applications.
Vinod Chugani
September 14, 2026
Code Review With Claude Code: Catch Bugs Before They Reach Production
A practical guide to reviewing Python data science pull requests with Claude Code, GitHub, and ultrareview.
Tim Lu
September 14, 2026
LangChain4j Tool Memory Tutorial: Build a Java Agent With Oracle AI Database
Learn how to build a simple LangChain4j agent that can remember important notes across conversations.
Anders Swanson
September 13, 2026
LMCache Tutorial: Build a Scalable KV Cache Layer for LLM Inference
Learn how LMCache stores and reuses KV states across LLM requests, integrates with vLLM, and reduces repeated prefill computation for long-context inference.
Abid Ali Awan
September 13, 2026