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:

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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)).

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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.

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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.

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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.

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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).

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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.

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