Data Structures & Algorithms

The two structures you'll use most — mastering them makes everything else easier.

Many powerful algorithms (trees, divide-and-conquer, DP) are built on recursion.

They teach pointer-based memory management and underpin stacks, queues, and more.

Simple yet powerful — they appear in parsing, traversal, and async processing.

The fastest way to look up and insert at average O(1) — essential for efficient problem-solving.

Sorting underpins many problems, and knowing how it works lets you pick the right tool.

Binary search cuts search time from O(n) to O(log n) — the key to many clever solutions.

Trees model hierarchical data, and BSTs give O(log n) search and insert when balanced.

A heap gives you the min or max element instantly — the basis of top-K and scheduling problems.

Networks, maps, and relationships are all graphs — mastering them unlocks a huge problem class.

From Google Maps to network routing — path algorithms solve real-world problems.

The hardest and most powerful topic — it turns exponential problems polynomial by reusing results.

Two complementary paradigms: greedy for fast optimal choices, backtracking for smart exhaustive search.

Specialized tools that turn hard problems easy — vital for competitive programming and tough interviews.