Python
Fundamentals, structured programming, object-oriented programming, testing, typing, concurrency, performance, backend and packaging.
Free courses
Python, Data and APIs
Practical content to learn programming, data analysis and API development.
Study at your own pace with runnable examples, exercises, projects and complete GitHub repositories.
100% free.
What you get
Executed code
Every output in the course was generated by running the code.
Exercises with tests
Solutions end with assert so you can validate your answers.
Integration projects
Real projects with tests in every course.
Up-to-date content
Recent versions and important library changes.
Courses in the learning path
Start with Python, then move on to data and APIs. If you already know the fundamentals, choose the course that fits your goal.
All courses include beginner through advanced content.
NumPy
Arrays, vectorization, broadcasting, linear algebra, randomness, memory, performance, numerical testing and a neural network from scratch.
Pandas
Load, inspect, clean, filter, group, join, reshape and summarize data, with dates, performance and data testing.
FastAPI
Build APIs with Pydantic, dependencies, middleware, SQLAlchemy, JWT, async, testing, cache, rate limiting and deployment.
Learning path overview
| # | Course | Chapters | What you can do by the end | Projects |
|---|---|---|---|---|
| 1 | Python | 65 | Program in Python from beginner to advanced: code structure, testing, performance and packaging. | Course integration projects |
| 2 | NumPy | 43 | Think in arrays: vectorization, broadcasting, linear algebra, performance and randomness. | Students (14 tests)Images from scratch (16 tests)Neural network from scratch (12 tests) |
| 3 | Pandas | 38 | Load, clean, join, reshape and summarize data, and prove the results are correct. | Employees (18 tests)Sales (16 tests)Service pipeline (16 tests) |
| 4 | FastAPI | 35 | Build an API with a database, authentication, testing and basic security protections. | Blog API with PostgreSQL (22 tests) |
How to study
A simple step-by-step guide to make the most of the learning path.
- 1
Read the chapter
Follow the course chapters and read the explanation alongside the code examples.
- 2
Run the code
Use the file indicated in the example and compare the result with the output shown in the chapter.
- 3
Experiment with changes
Modify the example, predict the result and run it again to check.
- 4
Solve the exercise
Try solving it before opening “View solution”. Run the assert checks to verify your answer.
- 5
Build the project
Follow the course project's prerequisites, apply what you learned and run the indicated tests.
Starting from scratch
Choose the path for your goal and prepare your study environment.
Which course fits your goal
- Learn Python
Prepare your environmentFolders and versions to follow the examples on your computer.
Suggested folder structure
trilha-cursos/
README.md
python-cursos/
numpy-cursos/
pandas-cursos/
fastapi-cursos/Verified versions
| Component | Version |
|---|---|
| Python | 3.12.3 |
| NumPy | 2.4.4 |
| Pandas | 3.0.2 |
| FastAPI | 0.142.2 |
| Pydantic | 2.13.5 |
| SQLAlchemy | 2.1.3 |
| PostgreSQL | 16.15 |
Know what to expect
Learn what was tested and the limits of the material before moving on.
Course quality and limitationsReview the tests performed and what falls outside the scope of the learning path.
What was verified
- Chapter files executed in a clean environment
- Projects with automated tests
- FastAPI tested with SQLite and PostgreSQL
- Paths referenced in the courses checked
- Dependencies pinned in uv.lock
Current limitations
- Docker, Docker Compose and deployments were not tested end to end
- This is not a complete Machine Learning program
- Data visualization is not covered in depth
- The datasets are synthetic
- Performance may vary between machines
