FreeCampus Python

Get Started Overview

Prepare a dependable Python learning lab, practise from observable evidence, and choose browser or local tools without assuming previous programming experience.
python-foundations get-started overview
Open in Colab
  • Level: Python Foundations · Unit 0
  • Estimated time: 13–20 hours including the challenge
  • Unit outcome: Run and explain a small Python program, keep trustworthy evidence in a clean notebook, choose a suitable practice environment, diagnose common first-run problems, and use AI assistance without surrendering prediction or verification.
  • Practice in: Google Colab or Jupyter, then a local editor and terminal when your device permits

Open this lesson in Google Colab

1. Start with a working place to learn

You do not need programming experience before this unit. You do not need to memorize Python words, choose a career specialty, or install every tool you have heard about. You need one place where you can run a small program, observe what happens, preserve useful notes, and try again without hiding the evidence.

The fastest route is a browser notebook such as Google Colab. It lets you begin without installing Python. A local workspace becomes important when you need to work with saved files, project folders, terminals, editors, and isolated environments. This unit teaches both routes. If a school or work computer blocks installation, you will record that constraint, continue in Colab, and identify what access you will need before later project units.

Notice that both routes lead to the same learning loop and a clean rerun.

flowchart LR
  A[Choose a practice route] --> B[Browser notebook]
  A --> C[Local workspace]
  B --> D[Predict]
  C --> D
  D --> E[Run]
  E --> F[Compare evidence]
  F --> G[Explain and change one thing]
  G --> H[Clean rerun]

You will run Python in the first lesson. Formal details about names, values, expressions, functions, and errors come later. When one of those shapes appears here, the lesson explains only what you need for the current task and points forward to the unit that develops it.

2. Follow the five-step sequence

Step Lesson What you will practise or create
1 Meet Python Through a Tiny Program Run first_run.py, connect each source line to its output, change one message, and repair a missing delimiter.
2 Learn with Predictions and Evidence Build learning-log.md and use observations rather than guesses to decide the next change.
3 Use AI While Keeping the Thinking Yours Review a supplied AI answer, verify it locally, protect private information, and retain your own conclusion.
4 Work Reliably in Colab and Jupyter Create first_notebook.ipynb, expose hidden state, and prove a clean top-to-bottom run.
5 Build a Dependable Local Python Workspace Run the saved script from a known folder, align the editor and interpreter, verify .venv, and finish launch-card.md.
6 Unit Challenge Complete three scenario rounds and confirm that your learning lab is ready for Unit 1.

Plan 13–20 hours of active work for this unit. Each lesson’s final lab is a natural stopping point. Save your work and its observed result before leaving, then rerun the nearest check when you return.

Each lesson asks you to predict, run, compare, explain, modify, and repair, but those words will not appear as one generic practice section copied five times. You will use them on different evidence: source code, an experiment record, an AI claim, notebook history, and local paths.

3. Prepare without buying anything

Bring:

  • a modern web browser;
  • a place to save a notebook or download files;
  • permission to create a small folder if you will practise locally; and
  • a plain-text note for commands, outputs, and questions.

The course does not require a paid editor, notebook service, or AI subscription. You can complete the AI lesson using the supplied responses. Do not paste passwords, API keys, private repository code, personal records, or assessment answers into an external service.

ImportantChoose one route before adding tools

Begin in Colab if installation would delay your first run. Begin locally if Python is already available and you can create files. Do not install Python, Conda, Anaconda, multiple editors, and several notebook systems at once. One working route creates better evidence than five half-configured routes.

4. Know what success looks like

You are ready for Unit 1 when you can:

  1. point to the source line responsible for each line of a tiny program’s output;
  2. keep prediction, observation, explanation, and next change separate;
  3. treat an AI response as a claim to verify rather than permission to stop thinking;
  4. restart a notebook and run every cell from the top without relying on hidden state;
  5. state which Python command and environment you are using; and
  6. preserve the exact command, output, and relevant file when you need help.

Progress in this site is local and self-reported. It is not a verified exam, identity check, or certificate record. A Foundations final project and certificate are planned but are not available yet; they will be announced later.

Begin with Meet Python Through a Tiny Program

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