Log inStart free
← Back to catalog

Python for AI from scratch

Learn Python and libraries for AI and data work.

Updated: 21 July 2026Editor: Skilvy editorial

22 lessons · 9 h read · 2 free · 4 AI-graded projects

About this course

A course for people who want to write code for working with data and AI tools starting from zero programming experience. It treats Python not as a language in general but specifically as a data tool — from types, mutability, and tracebacks to pandas, numpy, and calling models through an external API.

Lessons move from environment and types — 'venv, versions, and reproducibility,' 'Reading a traceback: what to do first' — through numpy and pandas — 'DataFrame and Series: what pandas is built from,' 'Grouping and aggregation,' 'Joining tables and what happens to keys' — to project structure, tests for analytical code, and working with external APIs. Every block ends with its own working tool: a data loader, a reproducible report, a small project, a finished utility for an external service.

The course suits beginners with no programming experience who want to apply code to their own real data work, not pass an exam on language syntax. No technical background is required, but you'll need your own working data — assignments run on a real CSV or Excel file, not textbook examples.

What you'll learn

  • Set up an environment and pin dependencies in requirements.txt
  • Read a traceback and find the actual root cause
  • Work with numpy arrays and vectorization instead of loops
  • Build a DataFrame, filter, group, and join tables
  • Structure code into src/scripts/tests instead of one file
  • Write the minimum set of tests that actually pays off
  • Log instead of using print and handle errors properly
  • Call an external API with retries and a --dry-run flag

Tools you'll work with

PythonpandasnumpyJupytervenv

What you'll end up with

  • A loader for your own working data (load.py)
  • A reproducible report on your tables from a single script
  • A small project with tests and a src/scripts/tests structure
  • A ready-to-hand-off tool that calls an external service, plus a certificate on paid plans

This course doesn't teach algorithms and data structures for interviews — it's practical Python for working with data, not competitive programming.

Course program

Module 1 · The Python people actually write for data
1How data Python differs from textbook PythonFree2The environment: venv, versions and reproducibilityFree3Types, mutability and where things usually break4Tracebacks: how to read them and what to do first
Module 2 · Data in memoryproject
5List, dict, set: which one and when6numpy: arrays and vectorization7Slices, copies and views8Files, encodings and paths
Module 3 · Tablesproject
9DataFrame and Series: what pandas is made of10Selection and filtering without confusion11Grouping and aggregation12Joining tables and the fate of the keys13Types, missing values and memory
Module 4 · Code that survives a weekproject
14Functions, arguments and side effects15Project structure and your own modules16Tests for analytical code: the minimum that pays off17Logs instead of print, and error handling
Module 5 · Working with models and the outside worldproject
18Notebook versus script: what belongs where19APIs and retries20Parallelism in plain words21Performance: measure first, optimise second22Secrets, environments and handing code over
$69
one-time purchase · lifetime access
Or included in Ultra
Start freeGet a subscription
Practical projects
0 / 4 passed
Certificate progress →

What's included

  • Lifetime access
  • Tasks and quizzes
  • Certificate — on a paid plan
  • Updates forever