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BCS 042 Introduction to Algorithm Design | Quick...
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| Category | : BACHELOR‘S DEGREE PROGRAMMES |
| Sub Category | : Bachelor of Computer Applications (BCA) |
| Products Code | : BCA-S4-3.06 |
| HSN Code | : 490110 |
| Language | : English |
| Author | : BMA PUBLICATION PVT LTD |
| Publisher | : BMAP EDUSERVICES PVT LTD |
| University | : IGNOU (Indira Gandhi National Open University) |
| Pages | : 300 |
| Weight | : 199 GM |
| Dimensions | : 21.0 x 29.7 cm (A4 Size Pages) |
BCS 042 Introduction to Algorithm Design is a foundational course designed for Bachelor of Computer Applications (BCA) students, providing a comprehensive introduction to the principles and techniques of algorithm design and analysis. This course aims to equip students with the essential knowledge and skills necessary for designing efficient and effective algorithms to solve computational problems.
The course begins by covering algorithmic strategies. Students learn about various algorithm design techniques, including brute force, divide and conquer, dynamic programming, and greedy algorithms. They understand how to apply each strategy to solve different types of problems and develop algorithmic solutions that optimize time and space complexity.
Algorithm efficiency is a central focus of the course. Students understand the importance of algorithm efficiency and learn how to analyze algorithms using asymptotic notation, commonly known as big O notation. They explore the concepts of best-case, worst-case, and average-case analysis and learn how to evaluate algorithm performance based on input size.
The course covers fundamental searching and sorting algorithms. Students gain proficiency in searching algorithms such as linear search and binary search, as well as sorting algorithms including bubble sort, insertion sort, merge sort, and quick sort. They understand the principles behind each algorithm, analyze their time and space complexity, and implement them to solve computational problems.
Graph algorithms are another essential aspect of the course. Students explore a variety of graph algorithms used to solve graph-based problems. They learn about traversal algorithms such as depth-first search (DFS) and breadth-first search (BFS), as well as shortest path algorithms such as Dijkstra's algorithm and minimum spanning tree algorithms such as Prim's and Kruskal's algorithms.
Throughout the course, students engage in practical exercises and algorithmic problem-solving tasks to reinforce their learning. They work on coding assignments, implement algorithms in programming languages such as C, C++, or Python, and analyze algorithm performance using empirical analysis and theoretical methods.
In addition to its educational value, this study guide serves as a valuable resource for students preparing for exams. Covering the entire syllabus comprehensively and spanning approximately 300-350 pages, it provides in-depth coverage of all algorithm design and analysis topics, ensuring thorough preparation for exams.
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