.png "Data Structures and Algorithms (DSA) course training classes")
• DSA stands for Data Structures and Algorithms.
• It is a combination of two important concepts in computer science:
– Data Structures: The way data is organized and stored.
– Algorithms: Step-by-step instructions to solve problems.
• DSA helps in writing efficient and optimized code.
• It is the foundation of programming, competitive coding, and technical interviews.
• DSA improves your problem-solving and logical thinking skills.
• It helps in writing fast and memory-efficient programs.
• Most technical job interviews in IT companies are based on DSA concepts.
• Understanding DSA gives you confidence in coding challenges and projects.
• It is required in real-world applications like search engines, social media, and mobile apps.
Lesson 1: Introduction
● Introduction
● What is DSA?
● Importance in programming and problem solving
● Complexity Analysis - Time and Space
Lesson 2: Arrays & Strings
● 1D and 2D Arrays
● Basic problems and optimizations
● String operations and pattern matching (KMP, Z-Algorithm)
Lesson 3: Linked Lists
● Singly Linked List
● Doubly Linked List
● Circular Linked List
● Linked List operations and problems
Lesson 4: Stacks and Queues
● Stack operations and problems (Infix, Prefix, Postfix)
● Queue, Circular Queue, Deque
● Implementation using arrays and linked lists
Lesson 5: Trees
● Binary Trees, BST
● Tree traversals (Inorder, Preorder, Postorder)
● Heap, Trie, Segment Tree
● Balanced Trees: AVL, Red-Black Tree
Lesson 6: Graphs
● Graph representations (Adjacency Matrix/List)
● DFS, BFS, Dijkstra, Floyd-Warshall
● Minimum Spanning Tree: Kruskal, Prim
● Topological Sort, Cycle Detection
Lesson 7: Recursion & Backtracking
● Basic recursion problems
● N-Queens, Sudoku Solver
● Backtracking templates
Lesson 8: Greedy Algorithms
● Activity Selection, Huffman Encoding
● Greedy vs Dynamic Programming
Lesson 9: Dynamic Programming
● Memoization vs Tabulation
● Classic problems: Knapsack, LCS, LIS, Matrix Chain Multiplication
Lesson 10: Searching & Sorting
● Binary Search and variations
● Merge Sort, Quick Sort, Count Sort, Heap Sort
● Time complexity comparisons
Lesson 11: Hashing & Bit Manipulation
● Hash Table, Hash Map, Collision handling
● Bitwise operations and tricks
Lesson 12: Interview Pattern Problems
● Sliding Window, Two Pointer, Fast-Slow Pointers
● Prefix Sum, Binary Search on Answer
● Important Leetcode/Codeforces Patterns
Lesson 1: Introduction
● Introduction
● What is DSA?
● Importance in programming and problem solving
● Complexity Analysis - Time and Space
Lesson 2: Arrays & Strings
● 1D and 2D Arrays
● Basic problems and optimizations
● String operations and pattern matching (KMP, Z-Algorithm)
Lesson 3: Linked Lists
● Singly Linked List
● Doubly Linked List
● Circular Linked List
● Linked List operations and problems
Lesson 4: Stacks and Queues
● Stack operations and problems (Infix, Prefix, Postfix)
● Queue, Circular Queue, Deque
● Implementation using arrays and linked lists
Lesson 5: Trees
● Binary Trees, BST
● Tree traversals (Inorder, Preorder, Postorder)
● Heap, Trie, Segment Tree
● Balanced Trees: AVL, Red-Black Tree
Lesson 6: Graphs
● Graph representations (Adjacency Matrix/List)
● DFS, BFS, Dijkstra, Floyd-Warshall
● Minimum Spanning Tree: Kruskal, Prim
● Topological Sort, Cycle Detection
Lesson 7: Recursion & Backtracking
● Basic recursion problems
● N-Queens, Sudoku Solver
● Backtracking templates
Lesson 8: Greedy Algorithms
● Activity Selection, Huffman Encoding
● Greedy vs Dynamic Programming
Lesson 9: Dynamic Programming
● Memoization vs Tabulation
● Classic problems: Knapsack, LCS, LIS, Matrix Chain Multiplication
Lesson 10: Searching & Sorting
● Binary Search and variations
● Merge Sort, Quick Sort, Count Sort, Heap Sort
● Time complexity comparisons
Lesson 11: Hashing & Bit Manipulation
● Hash Table, Hash Map, Collision handling
● Bitwise operations and tricks
Lesson 12: Interview Pattern Problems
● Sliding Window, Two Pointer, Fast-Slow Pointers
● Prefix Sum, Binary Search on Answer
● Important Leetcode/Codeforces Patterns
Lesson 13: Advanced Trees and Graphs
● Lowest Common Ancestor (LCA) – Binary Lifting
● Euler Tour Technique
● Disjoint Set Union (DSU) / Union-Find with Path Compression
● Heavy-Light Decomposition
● Centroid Decomposition
Lesson 14: Advanced Dynamic Programming
● Digit DP
● Bitmask DP
● Tree DP
● DP on Graphs
● Knuth Optimization, Convex Hull Trick
Lesson 15: Segment Tree & Variants
● Segment Tree with Lazy Propagation
● Persistent Segment Tree
● Fenwick Tree (Binary Indexed Tree)
● 2D Segment Tree
Lesson 16: String Algorithms
● Suffix Array and LCP Array
● Rabin-Karp, KMP, Z-Algorithm
● Aho-Corasick Automaton
● Manacher’s Algorithm
● Trie + Bit Trie
Lesson 17: Number Theory
● Sieve of Eratosthenes
● Modular Exponentiation
● GCD, LCM, Extended Euclidean Algorithm
● Modular Inverse, Fermat's Theorem
● Chinese Remainder Theorem
Lesson 18: Advanced Graph Algorithms
● Articulation Points and Bridges
● Strongly Connected Components (Kosaraju, Tarjan)
● 2-SAT Problem
● Bellman-Ford Algorithm
● Johnson’s Algorithm
Lesson 19: Geometry & Miscellaneous
● Convex Hull (Graham Scan, Andrew's Algorithm)
● Line Sweep Algorithms
● Segment Intersection
● Rotating Calipers
● Game Theory (Nim Game, Grundy Numbers)