What You'll Learn, Module by Module.

Stand up your first Python backend with FastAPI, test endpoints with Postman, and scaffold a quick UI with Next.js — all accelerated by 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 writing tests that protect the code you ship — the engineering culture behind every production codebase.

Package and ship your code the way production teams do — containerize services with Docker, deploy to the cloud on Render, Railway, and Vercel, and understand cloud fundamentals so every push goes live automatically.

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

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

Wire everything you've built into one complete, deployed product — your frontend consuming your backend APIs, live data flowing from you database through your APIs to the UI, and the whole thing running live. This is where the separate pieces become a real product.

Layer AI onto the full-stack product you've built — prompt engineering as an engineering discipline, hands-on work with OpenAI and Claude SDKs, vector stores with ChromaDB, RAG pipelines that retrieve real context before generating answers, and a first look at agentic AI: building small tool-calling agents that automate real tasks inside your app.

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