Introduction to Computer Science with Python

This is a computer science course for the Swiftener Computer Science editor. You already know Python from Introduction to Python. Here you use it to study algorithms and data structures: search, sort, stacks, queues, trees, graphs.

Language syntax belongs in Introduction to Python. Big-O, heaps, and Dijkstra belong in Introduction to Data Structures and Algorithm Analysis with Python. Modules, tests, and project layout belong in Introduction to Software Engineering with Python. This course stays on first algorithms and how computation works.

You do not install Python. Code and uploaded files stay in your browser.

How to take the course

1. Keep this tutorial tab and the Computer Science workbench side by side.
2. Wait until the workbench status says Python is ready.
3. Copy an exercise, paste it as the whole editor, run it, then read the printed counts and results. Each shaded block is a complete script — it does not rely on leftover variables.
4. When a chapter asks for a file, download it from the buttons above, then click Add files in the workbench.

The workbench Snippets menu is a cheat sheet. Prefer the exercises here — they are written for this editor.

What this editor can and cannot do

• Can: standard library, print, lists and dicts, recursion, writes to /uploads, downloads.
• Cannot: matplotlib plots, NumPy arrays, git, or fetching URLs. Do not import numpy.
• Always print(...) a result. A bare value at the end of a cell is not displayed the way Jupyter would.
• Prefer writing linear_search yourself. in and list.sort hide the steps this course is counting.

Sample files

Download any of these from the banner, then attach them with Add files:

• cities.txt — Nairobi through Eldoret, one name per line
• unsorted.txt — mixed prices to sort
• graph.json — undirected city links
• inventory.csv — product rows for the practice chapter

Chapter list

Work in order the first time. After that, jump around from the sidebar.