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Data Structure and Algorithm

BCA course syllabus for data structures and algorithm at Sikkim Manipal University Distance Education. Request students to fill the enquiry form for a call back counselling.

BCA course syllabus for data structures and algorithm at Sikkim Manipal University Distance Education. Request students to fill the enquiry form for a call back counselling.

Data Structure and Algorithm BCA course syllabus for data structures and algorithm at Sikkim Manipal University Distance Education. Request students to fill the enquiry form for a call back counselling. When: — Where: Category:

BCA - Data Structure and Algorithm

Course Code: BCA212

Course Title: Data Structure and Algorithm (4 Credits)

 

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Course Contents

Unit – 1: Data Structures Basics: Structure and Problem Solving, Data structures, Data structure Operations, Algorithm: complexity, Time- space tradeoff.

Unit – 2:  Linked List: Introduction, Linked lists, Representation of linked lists in Memory, Traversing a linked list, Searching a linked list, Memory allocation and Garbage collection, insertion into linked list, Deletion from a linked list, Types of linked list.

Unit – 3: Stack and Queue: Introduction, Array Representation of Stack, Linked List Representation of stack, Application of stack, Queue, Array Representation of Queue, Linked List Representation of Queue.

Unit – 4: Trees: Definitions and Concepts, Operations on Binary Trees, Representation of binary tree, Conversion of General Trees to Binary Trees, Sequential and Other Representations of Trees, Tree Traversal. 

Unit –5: Graphs: Matrix Representation of Graphs, List Structures, Other Representations of Graphs, Breadth First Search, Depth First Search, Spanning Trees.

Unit - 6: Directed Graphs Types of Directed Graphs; Binary Relation As a Digraph; Euler’s Digraphs; Matrix Representation of Digraphs.

Unit -7 Applications of Graphs: Topological Sorting, Shortest-Path Algorithms – Unweighted Shortest Paths – Dijkstra’s Algorithm, Minimum spanning tree- Prim’s Algorithm, Introduction to NP-Completeness.

Unit – 8: Searching Techniques: Sequential Searching, Binary Searching, Search Trees, Hash- Table Methods.

Unit 9 : Elementary Algorithms: Notation for Expressing Algorithms; Role and Notation for Comments; Example of an Algorithm; Problems and Instances; Characteristics of an Algorithm; Building Blocks of Algorithms; Procedure and Recursion – Procedure, Recursion; Outline of Algorithms; Specification Methods for Algorithms.

Unit 10: Mathematical Functions and Notations Functions and Notations; Modular Arithmetic / Mod Function; Mathematical Expectation in Average Case Analysis; Efficiency of an Algorithm; Well Known Asymptotic Functions and Notations; Analysis of Algorithms – Simple Examples; Well Known Sorting Algorithms – Insertion sort, Bubble sort, Selection sort, Shell sort, Heap sort.

Unit 11: Divide and Conquer Divide and Conquer Strategy; Binary Search; Max. And Min.; Merge sort; Quick sort.

Unit 12: Greedy Method Greedy Method Strategy; Optimistic Storage on Tapes; Knapsack Problem; Job Sequencing with Deadlines; Optimal Merge Pattern; Single Source Shortlist Paths.

Unit 13: Dynamic Programming Dynamic Programming Strategy; Multistage Graphs; All Pair Shortest Paths; Travelling Salesman Problems.

Unit 14: Complexity of Algorithms: Notations for the Growth Rates of Functions; Classification of Problems; Reduction, NP-Complete and NP-Hard Problems, Establishing NP-Completeness of Problems.

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