Data Science Tutorials

Decision models score a fixed set of answers instead of writing text. See how they work, how Jev compares, and what independent tests found.

A hands-on RAG evaluation tutorial in Python. Build an evaluation dataset, score it with the 4 core Ragas metrics, wire a quality gate into CI, generate synthetic test questions, and track scores across commits so you can tell a retrieval failure from a generation failure before your users do.

Learn how to set up Paperclip on Windows with MiMo-V2.6-Pro and build an autonomous AI team that plans, delegates, and manages projects from a single dashboard.

Learn what quadrature is, how numerical integration approximates definite integrals, and how methods such as the trapezoidal rule, Simpson's rule, and Gaussian quadrature work.

Learn how to add on-demand persistent memory to chatbots and agents with MongoDB Atlas Ephemeral Clusters, MongoDB Vector Search, and Automated Embedding.

Learn how the Box-Cox transformation works, how to choose the lambda parameter, and when to use it to stabilize variance, reduce skewness, and improve statistical models.

Reverse engineer Chrome's Dinosaur Game with REA and Pi Coding Agent, uncover its hidden mechanics, and turn those insights into your own standalone Windows game without writing a single line of code.

Learn how to use OpenAI’s Decisions API to classify inputs, score outcomes, route requests, and make structured decisions from text and images.

Follow this OpenAI Agents API Computer Use tutorial to build a Python QA agent that tests a checkout in a hosted browser and retests a fix in the same session.

Run a massive MoE model locally by combining GPU VRAM, system RAM, SGLang, and KTransformers for heterogeneous CPU-GPU inference.

A hands-on Claude Cowork plugins tutorial: install Anthropic's Data plugin, connect a warehouse and run a slash command, customize the plugin for your own tables, and build a complete weekly-report plugin from scratch with every file shown.

Learn what mean reversion is, how to identify mean-reverting behavior in time-series data, and how the concept is used in statistics, trading, and quantitative finance.