DSA Syllabus
Data Structures and Algorithms (DSA) Syllabus
This syllabus covers fundamental and advanced concepts in Data Structures and Algorithms (DSA), commonly used in coding interviews, competitive programming, and software development.
1. Introduction to DSA
Importance of Data Structures and Algorithms
Time Complexity and Space Complexity (Big-O Notation)
Asymptotic Analysis
2. Basic Data Structures
2.1 Arrays and Strings
Introduction to Arrays
Array Operations (Insertion, Deletion, Searching)
Multi-dimensional Arrays
String Manipulation and Pattern Matching
Sliding Window and Two-Pointer Technique
2.2 Linked List
Singly Linked List (Insertion, Deletion, Traversal)
Doubly Linked List
Circular Linked List
Detecting and Removing Loops in Linked Lists
2.3 Stack
Stack Implementation (Array and Linked List)
Applications: Valid Parentheses, Postfix Expression Evaluation
Stack using STL/Collections
2.4 Queue and Deque
Queue Implementation (Array and Linked List)
Circular Queue
Priority Queue
Double-Ended Queue (Deque)
3. Recursion and Backtracking
Basics of Recursion (Factorial, Fibonacci)
Backtracking (N-Queens Problem, Rat in a Maze, Sudoku Solver)
4. Searching and Sorting Algorithms
4.1 Searching Algorithms
Linear Search
Binary Search (Iterative and Recursive)
Ternary Search
Applications of Binary Search (Search in Rotated Sorted Array, Median of Two Sorted Arrays)
4.2 Sorting Algorithms
Bubble Sort, Selection Sort, Insertion Sort
Merge Sort, Quick Sort, Heap Sort
Counting Sort, Radix Sort, Bucket Sort
5. Hashing and Hash Tables
Hash Functions and Hash Tables
Collision Handling (Chaining, Open Addressing)
Applications of Hashing (Two Sum Problem, Anagram Check)
6. Trees and Graphs
6.1 Trees
Binary Trees (Traversal: Inorder, Preorder, Postorder)
Binary Search Tree (BST) and its Operations
AVL Trees, Red-Black Trees
Trie (Prefix Tree) and its Applications
6.2 Graphs
Graph Representations (Adjacency Matrix, Adjacency List)
Graph Traversal (DFS, BFS)
Shortest Path Algorithms (Dijkstra’s, Floyd-Warshall, Bellman-Ford)
Minimum Spanning Tree (Prim’s, Kruskal’s)
Topological Sorting
7. Dynamic Programming (DP) and Greedy Algorithms
7.1 Dynamic Programming
Principles of Dynamic Programming
Memoization vs. Tabulation
Classical DP Problems (Knapsack Problem, Fibonacci, Longest Common Subsequence, Coin Change)
7.2 Greedy Algorithms
Greedy Algorithm Approach
Activity Selection Problem
Huffman Coding
Fractional Knapsack
8. Advanced Topics
Bit Manipulation
Segment Trees and Fenwick Tree (Binary Indexed Tree)
Disjoint Set Union (DSU) and Kruskal’s Algorithm
KMP Algorithm for Pattern Matching
9. Competitive Programming Practice
Solving Problems on Platforms like LeetCode, CodeForces, and HackerRank
Time and Space Optimization Techniques
Participating in Online Coding Contests
