Data Structures & Algorithms

What O(1), O(n), and O(log n) really mean — and why getting them right is half of CS.

Contiguous memory, indices, and the patterns that solve 80% of array problems: two pointers and sliding window.

How hash tables work — and why they turn O(n²) problems into O(n) ones.

Pointer manipulation, dummy nodes, fast/slow pointers — and when a linked list is actually the right answer.

LIFO, FIFO, and a clever variant that solves the "next greater element" family of problems.

Recursion as a tool, traversals (preorder/inorder/postorder), and the BST invariant.

The most universal data structure in CS. Friend networks, road maps, dependency graphs — all the same algorithms.

Memoization, tabulation, and a framework for recognizing and solving DP problems.

When local choices reach a global optimum (greedy) — and what to do when they don't (backtracking).

The toolkit that separates intermediate from expert: priority queues, prefix trees, and disjoint-set union.