Data Structures and Algorithms
( 25 Modules )

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

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Data Structures and Algorithms ( 25 Modules )

Data Structures and Algorithms
( 25 Modules )