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Algorithms

Seven lessons on the classic algorithms: how to measure speed with Big O, binary search, simple and fast sorts, recursion, searching a graph and dynamic programming.

  1. How fast is it? Big O

    Counting steps instead of seconds, and what O(1), O(log n), O(n) and O(n²) mean.

    6 questions

  2. Searching

    Linear search checks everything; binary search halves a sorted list each step.

    5 questions

  3. Simple sorting

    Bubble, selection and insertion sort: easy to follow, fine for small lists, slow for big ones.

    5 questions

  4. Fast sorting

    Merge sort and quicksort split the problem in half, which gets sorting down to O(n log n).

    5 questions

  5. Recursion

    Functions that call themselves, the base case that stops them, and the call stack underneath.

    5 questions

  6. Searching a graph

    Breadth-first and depth-first search, and why BFS finds the route with the fewest steps.

    5 questions

  7. Dynamic programming

    Remembering answers to small problems so you never work them out twice.

    5 questions
More info

How it works

Each lesson is a short read with examples, then a few practice questions. Some ask what a piece of code prints, some have you pick an answer, and some have you tap steps or lines into the right order. You can try again as often as you like; after two misses there’s a button to show the answer.

What do I need to know first?

Programming basics, and ideally the data structures course, since stacks, queues and graphs come up here.

The other computer science courses

Where is my progress saved? In this browser, like the rest of the site. Nothing to sign up for.

“Python” is a registered trademark of the Python Software Foundation. This course isn’t made by or affiliated with it.