1. Introduction to Data Structures (1 hour)
    • Definition, Importance, and Real-world Examples
    • Time and Space Complexity Basics
  2. Arrays and Strings (2 hours)
    • Array Operations: Traversal, Insertion, Deletion
    • Multidimensional Arrays
    • String Manipulations and Common Problems (e.g., Palindrome)
  3. Linked Lists (3 hours)
    • Singly and Doubly Linked Lists
    • Operations: Insertion, Deletion, Searching
    • Circular Linked List Introduction
  1. Stacks (2 hours)
    • Stack Implementation Using Arrays and Linked Lists
    • Applications: Undo Mechanism, Expression Evaluation
  2. Queues (2 hours)
    • Queue Types: Circular, Priority Queues
    • Operations: Enqueue, Dequeue, Peek
    • Use Cases: Scheduling, Caching
  3. Recursion and Its Applications (2 hours)
    • Basic Recursive Techniques
    • Recursive Problem Examples (Factorial, Fibonacci)
  1. Binary Trees (3 hours)
    • Basic Terminologies: Node, Root, Leaf
    • Traversals: Preorder, Inorder, Postorder
    • Applications in Parsing and Expression Trees
  2. Binary Search Trees (3 hours)
    • Operations: Insertion, Deletion, Searching
    • Use Cases: Databases, Searching Algorithms
  3. Advanced Trees (3 hours)
    • AVL Trees and Balancing Techniques
    • B-Trees and Their Applications
    • Segment Trees
  1. Graphs (6 hours)
    • Graph Representation: Adjacency Matrix, List
    • Traversals: Breadth-First Search (BFS), Depth-First Search (DFS)
    • Graph Applications: Shortest Path, Network Flow
  2. Hashing (3 hours)
    • Hash Functions and Collision Resolution
    • Applications in Caching, Indexing
    • Implementation of Hash Maps
  1. Sorting and Searching Algorithms (3 hours)
    • Sorting Techniques: Merge Sort, Quick Sort, Bubble Sort
    • Searching Techniques: Linear, Binary Search

Real-world Use Cases in Data Retrieval