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🌱 Foundations — Searching
O(n) · O(log n)
Start here. Simple logic that teaches you how algorithms work — what a "step" is, and why speed matters. Linear Search is brute force; Binary Search shows the power of sorted data.
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📊 Easy Sorting — O(n²)
Comparisons & Swaps — perfect for small data
All three run in O(n²) — slow for large data, but incredibly visual. They teach you the core mechanics of sorting: comparing, swapping, and building a sorted region.
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🚀 Advanced Sorting — Divide & Conquer
O(n log n) — the algorithms used in real systems
These algorithms split the problem in half each time, giving O(n log n) speed. Understanding them unlocks a key CS concept: recursion and divide-and-conquer thinking.
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🗂️ Data Structures — How Data Lives
O(1) push/pop — the building blocks of everything
Algorithms don't exist in isolation — they run on data structures. Stack, Queue, and Linked List are the three most fundamental. Every system you'll ever build uses these.
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🌳🕸️ Trees & Graphs — The Powerful Ones
O(log n) · O(V+E) — used in maps, compilers, AI