Module #1 Introduction to Data Structures and Algorithms Overview of the importance of data structures and algorithms, course objectives, and prerequisites.
Module #2 Big-O Notation and Time Complexity Introduction to Big-O notation, time complexity, and how to analyze the efficiency of algorithms.
Module #3 Arrays and Dynamic Memory Allocation Introduction to arrays, dynamic memory allocation, and basic operations (insertion, deletion, searching).
Module #4 Linked Lists Introduction to linked lists, implementation, and operations (insertion, deletion, searching).
Module #5 Stacks and Queues Introduction to stacks and queues, implementation, and operations (push, pop, enqueue, dequeue).
Module #6 Trees (Basic Concepts) Introduction to trees, basic concepts (nodes, edges, height, traversals), and types (binary, AVL, BST).
Module #7 Binary Search Trees (BST) Implementation and operations of BSTs (insertion, deletion, searching), and balancing techniques.
Module #8 Heaps and Priority Queues Introduction to heaps and priority queues, implementation, and operations (insertion, deletion, extract-max/min).
Module #9 Graphs (Basic Concepts) Introduction to graphs, basic concepts (vertices, edges, adjacency matrix, adjacency list), and types (weighted, unweighted).
Module #10 Graph Traversal (BFS, DFS) Introduction to graph traversal algorithms (BFS, DFS), implementation, and applications.
Module #11 Graph Shortest Paths (Dijkstras, Bellman-Ford) Introduction to graph shortest paths algorithms (Dijkstras, Bellman-Ford), implementation, and applications.
Module #12 Hash Tables Introduction to hash tables, implementation, and operations (insertion, deletion, searching), collision resolution techniques.
Module #13 Sorting Algorithms (Bubble, Selection, Insertion) Introduction to basic sorting algorithms (bubble, selection, insertion), implementation, and time complexity analysis.
Module #14 Sorting Algorithms (Merge, Quick, Heap) Introduction to advanced sorting algorithms (merge, quick, heap), implementation, and time complexity analysis.
Module #15 Searching Algorithms (Linear, Binary) Introduction to searching algorithms (linear, binary), implementation, and time complexity analysis.
Module #16 Greedy Algorithms Introduction to greedy algorithms, implementation, and applications (Huffman coding, activity selection problem).
Module #17 Dynamic Programming Introduction to dynamic programming, implementation, and applications (Fibonacci series, longest common subsequence).
Module #18 Backtracking Algorithms Introduction to backtracking algorithms, implementation, and applications (n-queens problem, sudoku).
Module #19 Divide and Conquer Algorithms Introduction to divide and conquer algorithms, implementation, and applications (merge sort, fast Fourier transform).
Module #20 String Matching Algorithms Introduction to string matching algorithms (Rabin-Karp, Knuth-Morris-Pratt), implementation, and applications.
Module #21 Bit Manipulation and Operations Introduction to bit manipulation and operations, implementation, and applications (bitwise operations, bit vectors).
Module #22 Algorithm Design Techniques Introduction to algorithm design techniques (brute force, recursive, iterative), and problem-solving strategies.
Module #23 Asymptotic Notation and Complexity Analysis In-depth analysis of asymptotic notation, time and space complexity, and trade-offs.
Module #24 Problem-Solving Strategies and Practice Practice solving problems on platforms like LeetCode, HackerRank, or CodeForces, and strategies for improving problem-solving skills.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Data Structures and Algorithms career
