What You'll Learn, Module by Module.

Write clean, working Python — data structures, functions, files, and debugging — while learning to code faster with modern AI dev tools: Cursor, Claude Code, GitHub Copilot, and OpenAI Codex. Engineering productivity from day one.

Work the way real teams do: branches, pull requests, code reviews, GitHub Actions for CI/CD pipelines, and an introduction to Agile delivery workflows — the engineering culture behind every production codebase.

Model, query, and manage data in relational and document stores — PostgreSQL for structured data, MongoDB for flexibility — plus an introduction to ETL concepts and data pipeline thinking that every data-driven product runs on.

Build and secure your first real backend — REST endpoints, request validation, authentication flows, and API testing with FastAPI. Introduction to microservices architecture patterns and how production services communicate.

Analyze real datasets and communicate insights through them — pandas, exploratory data analysis, interactive charting, BI dashboards, and an introduction to time-series data and forecasting concepts used across every industry.

Build your first AI-powered features — prompt engineering as an engineering discipline, hands-on work with the OpenAI and Claude SDKs, an introduction to RAG concepts, and a first look at agentic patterns with LangChain.

Turn your APIs and AI features into products people can use — React components, Next.js pages, Vercel deployment, cloud fundamentals, and a Docker introduction so you understand how modern teams ship and run production software.

Put it all together: contribute a real, reviewed feature to a live Trida Labs product — integrating backend, data, and AI — and present it at Demo Day. Your first line in a production portfolio.