FreeCampus Python

FreeCampus Python

Six connected Python courses from first principles to software engineering, scientific computing, statistics, and machine learning.

Six connected Python courses

Build the Python path that matches your goal

Start from zero, then follow a software-development, scientific-computing, or statistics pathway. Every lesson uses focused explanations, runnable examples, quizzes, debugging practice, and small projects.

Start with Python Foundations Explore all courses

6 focused courses

3 learning pathways

Free in Colab

hello_python.py Ready 
# Change the name, then run it
learner = "Ada"
print(f"Hello, {learner}!")
Output Hello, Ada!
1 · Predict 2 · Run 3 · Explain 4 · Modify

Choose a course

Python Foundations

Beginner · No prerequisite

Learn core syntax, collections, functions, and debugging from zero programming experience, then optionally deepen files, tooling, OOP, and Python patterns.

Intermediate Python: Applied Problem Solving

TBD · Requires Foundations

Apply foundational tools to original challenges that emphasize parsing, modeling, testing, refactoring, and explaining unfamiliar problems.

Advanced Python: Complex Problem Solving

TBD · Requires Intermediate

Solve multi-stage, constrained problems involving search, graphs, simulations, performance, architecture, and engineering trade-offs.

Scientific Computing with Python

In development · Requires Foundations

Work with arrays, simulations, plots, symbolic mathematics, and scientific algorithms.

Statistics with Python

TBD · Requires Foundations

Describe variation, reason from samples, quantify uncertainty, and communicate what statistical evidence can and cannot support.

Data Science and Machine Learning

In development · Requires Scientific Computing and Statistics

Move from a data question to a responsibly evaluated model and communicate the evidence.

Compare every course

Choose a learning path

Python Foundations is the common entry point. Scientific Computing and Statistics are parallel prerequisites for Data Science and Machine Learning and may be completed in either order.

flowchart LR
  F[Python Foundations]
  I[Intermediate Python]
  A[Advanced Python:<br/>Complex Problem Solving]
  S[Scientific Computing]
  T[Statistics with Python]
  M[Data Science and Machine Learning]
  F --> I --> A
  F --> S
  F --> T
  S --> M
  T --> M

New to programming?

Begin with Meet Python Through a Tiny Program and run your first examples in a browser.

Already know some Python?

Use the readiness guide to choose a course without repeating skills you can already explain and debug.

Teaching these courses?

Use learner profiles, instructor notes, unit challenges, and visible project rubrics for self-paced or classroom learning.

How every course works

  1. Understand why the idea matters.
  2. Predict what a small example will do.
  3. Run it in Colab, Jupyter, VS Code, or the terminal.
  4. Explain the result in everyday language.
  5. Modify one small thing and predict again.
  6. Debug using evidence instead of guessing.
NoteProgress and certificates

Lesson progress is stored in your browser and organized by course. Python Foundations now has published local completion requirements and a project rubric. Verifiable certificates remain planned because they also require identity, assessment evidence, and durable learner records. Browser completion is not presented as a verified credential.

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