🧱 DATA STRUCTURES – "How data is organized"
Core Goal: Efficiently store, access, and manipulate data.
| Category | Concepts You Must Master | Real-Life Analogy |
|---|---|---|
| Array | Indexing, Iteration, Static size | Shelf with fixed compartments |
| Linked List | Nodes, Pointers, Dynamic size | Train cars linked together |
| Stack | LIFO, Push/Pop | Plates stacked in a cafeteria |
| Queue | FIFO, Enqueue/Dequeue | Waiting line at a ticket counter |
| Hash Map / Hash Table | Key-value pairs, Hash functions | Dictionary |
| Tree | Hierarchy, Traversals (DFS, BFS) | Family tree |
| Binary Search Tree (BST) | Ordered nodes, Search in O(log n) | Sorted filing system |
| Heap (Min/Max) | Priority Queue, Top-K | Task manager prioritizing processes |
| Trie | Prefix tree, String search | Phonebook with prefix-based lookup |
| Graph | Nodes + Edges, Directed/Undirected | Road map or social network |
⚙️ ALGORITHMS – "How problems are solved using data"
Core Goal: Apply logic to manipulate data structures efficiently.
| Technique | Concepts You Must Master | Example Problem Types |
|---|---|---|
| Recursion | Base case, Call stack | Factorial, Tower of Hanoi |
| Backtracking | Try all possibilities, undo steps | Sudoku, N-Queens |
| Greedy | Best choice at each step | Activity selection, Huffman coding |
| Divide & Conquer | Break into subproblems | Merge sort, Quick sort |
| Binary Search | Sorted input, Halve search space | Find element, Rotated array |
| Dynamic Programming | Optimal substructure, Memoization | Knapsack, Fibonacci, LIS |
| Graph Algorithms | DFS, BFS, Dijkstra, Union-Find | Shortest path, Cycle detection |
| Sorting | Bubble, Merge, Quick, Heap sort | Arrange items efficiently |
| Sliding Window | Fixed/variable range over array | Max sum subarray, Anagram check |
| Two Pointers | Start/end movement | Sorted array problems, Palindrome check |
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