AmouAI Hub/Courses /Data Structures & Algorithms
Thirty chapters, beginner to advanced. Every idea arrives as an analogy before a definition, every structure is animated so you watch it move rather than imagine it, and every chapter ends in exercises that grade themselves in your browser. Arrays through segment trees; linear search through A*.
Nothing to install. Python runs inside the page, and your code never leaves your machine.
Not a diagram you look at — an animation you drive. Press play, drag the scrubber, step backwards. Every visualisation in the course narrates what just happened and counts the operations as they occur. This one is from Chapter 12.
There are more than sixty of these, one or more in every chapter.
Five outcomes. Every chapter in the syllabus below maps to at least one, and the exercises assess them directly.
The same rhythm every time, so it becomes a habit rather than a decision. Budget about ninety minutes if you do the exercises properly — and you should.
Every structure opens with something you already understand — a coat check, a triage desk, a treasure hunt — before a single line of code appears.
Drive the structure yourself. Step forward and back, change the input, watch the operation counters climb.
Read the real Python, line by line, with the cost of each operation named and justified.
Warm-up, core and challenge problems, graded instantly with feedback that says what is wrong, not just that something is.
Seven parts, thirty chapters, in order. Each one assumes the ones before it. Open any part to see its chapters.
None of these are required — the chapters stand alone. They are named so that when you want the formal proof of something this course only shows you, you know where to look.
Loops, functions, if, and lists. If you can write a function that
loops over a list and returns a number, you are ready. You do not need classes,
decorators, generators or anything else — the chapters that need a class teach it on the spot.
If that description does not fit you yet, the first ten days of Programming Fundamentals will get you there.
Partly. Chapter 30 is explicitly about interview patterns, and the material covers everything a standard technical interview draws on. But the aim is understanding rather than pattern-matching: if you can explain why a heap is an array and why Dijkstra breaks on negative edges, the interview questions stop being memory tests.
Because the point is the idea, and Python has the least syntax between you and
it. Where Python's convenience hides something important — that a list is really a resizable
array, that dict is really a hash table — the chapter opens the box and shows you.
Everything here transfers directly. A heap is a heap in any language.
If you want the course to work, yes. Reading about a red-black tree produces a comfortable feeling of understanding that evaporates the moment you try to write one. The exercises are graded in your browser and tell you specifically what is wrong, so the loop is fast.
In your browser's local storage, per chapter — your code and which exercises you have passed. Nothing is uploaded anywhere, which also means clearing site data clears your progress, and it does not follow you to another device.