🧭 What is this course?
An algorithm is a step-by-step recipe for solving a problem. A data structure is a way of organizing information so your algorithms can use it quickly. Together, they are the heart of computer science — and the most common topic in coding interviews at companies like Google and Amazon.
You'll learn not just how to write code, but how to make it fast and elegant. Each lesson builds on the last, with a 📖 Description and a 🎮 Practice tab where you run real Python.
👉 Recommended: finish Python Basics first — you'll need functions, loops, lists, and dictionaries.
🏋️ Every lesson finishes with an Extra Practice section of six more coding drills, and the Practice Arena has boss battles waiting for you.
Big-O & Complexity
Measure how fast (or slow) an algorithm is as the data grows. The vocabulary of efficiency.
Start here ✨ 2Recursion
Functions that call themselves to break big problems into smaller copies of themselves.
Thinking 3Searching
Linear vs. binary search — and why halving the problem is a superpower.
Algorithms 4Sorting
Bubble, selection, and merge sort. See how clever ideas crush slow ones.
Algorithms 5Stacks & Queues
Last-in-first-out and first-in-first-out — the structures behind undo buttons and printers.
Data Structures 6Linked Lists
Build a chain of nodes from scratch and learn how pointers connect data.
Data Structures 7Hash Tables & Sets
The magic behind instant lookups. Why dictionaries are so fast.
Data Structures 8Trees & BSTs
Branching structures, traversals, and binary search trees for fast organized data.
Data Structures 🏆Problem-Solving Patterns
Two pointers, sliding window, and more — toolkits to crack interview challenges.
Capstone