The Python Roadmap

What a program is, what Python is, how to run your first lines here and on your own computer, and how to read the errors every beginner meets.

Numbers, text and true/false values, the names that hold them, how Python turns one kind of value into another, and how to show results clearly.

Make programs decide with if and match, repeat with while and for, and learn the loop tools every Python programmer uses: range, break, enumerate, zip and :=.

Slice and search text, use every string method that matters, format output, understand Unicode and bytes, write regular expressions and clean real text.

Lists, tuples, dictionaries and sets: how each works inside, how to copy them safely, the collections toolbox, and how to choose the right one.

Name a group of steps and reuse it: parameters, defaults, *args and **kwargs, scope, closures, recursion, type hints, and how to design a good function.

Split code across files, import it, organise it into packages, tour the standard library, and learn the tools and layout of a real, shippable Python project.

Handle errors on purpose, read and write files safely, speak CSV, JSON, TOML and YAML, log like a professional, and debug with real tools.

Classes, objects, inheritance, the data model, dataclasses, enums and protocols: how to model real things in code and design classes that stay easy to change.

How for really works, generators that produce values one at a time, itertools and functools, decorators, and context managers you write yourself.

Label what your values are, let a checker catch mistakes before you run, read hints at run time, and write Python the way current versions intend.

Write tests with assert, pytest, mocks and doctest, measure coverage, lint and format with Ruff, and let pre-commit and CI check every change for you.

Threads, processes and asyncio: what the GIL really does, how to run hundreds of LLM calls at once, and how to choose the right tool without guessing.

What an object really is, how memory is freed, what bytecode and attribute lookup do, how descriptors and metaclasses work, and how to measure and speed up code.

Arrays, vectors, broadcasting, tables and charts: the two libraries under almost all data science and machine learning, and the maths behind embeddings.

Call web APIs safely, validate data with Pydantic, build a FastAPI service, use databases, write command-line tools, automate work and ship it in a container.

How Python powers AI work: the library map, calling models, tokens, streaming, structured output, RAG, agents, evals, ML basics, PyTorch and AI assistants.

Answer the Python questions interviewers really ask, with a runnable proof for each, then build and ship a complete document question-answering tool.