MCA 1st Semester DATA STRUCTURE AND ALGORITHMS WITH ‘C’ Paper Summary || PPU | Patna | MCA | Syllabus || Allrounder Sita Ram Sahu || Subscribe

 Paper 2: DATA STRUCTURE AND ALGORITHMS WITH ‘C’ from the MCA syllabus:


Unit-I: Basics of Data Structures and Algorithms

  1. Structures, Unions, and File Input/Output:

    • Structures: User-defined data type to group variables (e.g., struct in C).
    • Unions: Similar to structures but share memory.
    • File I/O: Functions like fopen, fclose, fprintf, fscanf, etc.
  2. Pointers and Dynamic Memory Allocation:

    • Pointers: Variables that store memory addresses.
    • Dynamic Memory Allocation: Functions like malloc, calloc, realloc, and free.
  3. Algorithm Analysis and Complexity:

    • Time Complexity: Measures how runtime grows with input size (e.g., O(1), O(n), O(n²)).
    • Space Complexity: Memory usage by an algorithm.
  4. Recursion:

    • Function calls itself to solve smaller instances of the problem.
    • Types: Linear Recursion (direct calls), Binary Recursion (multiple calls).
  5. Searching Techniques:

    • Linear Search: Sequentially checks each element (O(n)).
    • Binary Search: Divides sorted array into halves (O(log n)).

Unit-II: Linked Lists

  1. Introduction to Linked Lists:

    • Single Linked List: Nodes connected linearly with a pointer to the next node.
    • Circular Linked List: Last node points back to the first node.
    • Double Linked List: Each node has pointers to both the next and previous nodes.
  2. Operations on Linked Lists:

    • Insertion: Adding elements to the list.
    • Deletion: Removing elements from the list.
    • Traversal: Accessing all nodes in sequence.
  3. Advantages and Disadvantages:

    • Advantages: Dynamic memory allocation, efficient insertions/deletions.
    • Disadvantages: Overhead of pointers, sequential access only.

Unit-III: Stacks and Queues

  1. Stacks:

    • LIFO (Last-In-First-Out): Data added last is accessed first.
    • Operations:
      • Push: Add data.
      • Pop: Remove data.
      • Peek: View the top element.
    • Applications:
      • Reverse strings.
      • Evaluate postfix expressions.
      • Convert infix to postfix notation.
  2. Queues:

    • FIFO (First-In-First-Out): Data added first is accessed first.
    • Types:
      • Simple Queue: Regular insertion and deletion.
      • Circular Queue: Wraps around to use unused space.
      • Priority Queue: Elements prioritized during insertion.
    • Applications:
      • Round-robin scheduling.
      • Handling requests in real-time systems.
  3. Implementation:

    • Using arrays or linked lists.

Unit-IV: Sorting Techniques

  1. Basic Concepts:

    • Sorting arranges data in ascending or descending order.
  2. Sorting Algorithms:

    • Insertion Sort: Builds sorted array one item at a time (O(n²)).
    • Selection Sort: Repeatedly selects the smallest/largest item (O(n²)).
    • Bubble Sort: Repeatedly swaps adjacent elements if out of order (O(n²)).
    • Quick Sort: Divides array using a pivot, sorts partitions (O(n log n)).
    • Merge Sort: Divides array into halves, merges sorted halves (O(n log n)).
    • Heap Sort: Builds a max/min heap and extracts elements (O(n log n)).
    • Radix Sort: Non-comparative, sorts digits position by position.

Unit-V: Trees and Graphs

  1. Trees:

    • Hierarchical data structure with nodes connected by edges.
    • Types:
      • Binary Tree: Each node has at most two children.
      • Binary Search Tree (BST): Left child < parent < right child.
      • AVL Tree: Self-balancing binary search tree.
      • B+ Tree: Used for databases, balances multiple keys.
    • Traversals:
      • Inorder: Left → Root → Right.
      • Preorder: Root → Left → Right.
      • Postorder: Left → Right → Root.
  2. Graphs:

    • Collection of vertices (nodes) and edges (connections).
    • Representation:
      • Adjacency matrix.
      • Adjacency list.
    • Graph Traversal:
      • DFS (Depth First Search): Explores as far as possible along branches.
      • BFS (Breadth First Search): Explores level by level.
    • Applications:
      • Shortest path (e.g., Dijkstra’s algorithm).
      • Minimum spanning tree (e.g., Prim’s and Kruskal’s algorithms).

Key Features of the Subject

  • Provides foundational knowledge of algorithms, data structures, and their efficiency.
  • Covers practical implementations in C programming.
  • Includes advanced topics like AVL Trees, B+ Trees, and graph algorithms.
  • Practical focus on solving problems like searching, sorting, and dynamic memory allocation.

Would you like explanations for any specific algorithm, code implementation, or practical examples?

No comments:

Post a Comment