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SQL Basics
Zero to genuinely solid SQL: every query pattern you must own before any data interview, drilled with runnable, interview-style questions.
Engineering setup for AI & Data Products
Build the foundational skills required to set up AI and data products properly, including terminal usage, Python project anatomy, Python package boundaries, virtual environments, dependency management with uv, configuration hygiene, Git and GitHub workflows, secrets management, repository visibility, licensing, testing, a minimal FastAPI starter app, Docker-based local services, and a guided starter-repo project.
Python Foundations
From zero to confident everyday Python, taught through real situations and the bugs they cause: values, collections, functions, the mutation model, errors, and files, all practiced by writing and running code in the browser.
Idiomatic Python and OOP
Go from working Python to fluent, well-structured Python: the idioms professionals reach for, the standard-library toolkit, iterators and generators, decorators, and object-oriented design built on Python's data model.
Machine Learning Foundations
An intuition-first foundation in classical machine learning for anyone moving into LLM and agent work: enough real understanding to discuss the concepts with confidence and to frame working ML projects from a domain you already know.
DSA for Agentic AI Engineers
Python-only data structures and algorithms practice for AI and data product engineers: problem statement, runnable solution, explanation, and visible public tests for every problem.