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This Course is designed to provide students, who have already completed Advanced Programming and Discrete Structures, with a comprehensive understanding of fundamental concepts in computer science. The course begins with a focus on time and space complexity analysis, enabling students to grasp the theoretical underpinnings of algorithmic efficiency. Emphasis is placed on understanding how to assess the efficiency of algorithms in terms of their execution time and memory requirements, thus enabling students to make informed decisions about algorithm selection for various problem-solving scenarios.
Moving forward, the course delves into various essential data structures, including linked lists, stacks, queues, and heaps. Students gain a profound understanding of how these structures operate, as well as their advantages and limitations in different contexts. Additionally, the course explores advanced topics such as tree and graph traversal, covering both breadth-first and depth-first search algorithms, allowing students to comprehend the nuances of navigating complex data structures. This segment of the course provides a solid foundation for understanding more intricate concepts, such as binary search trees and balanced trees, fostering a deeper understanding of data organization and management.
Moreover, the course delves into essential sorting algorithms, equipping students with the skills to compare and contrast various sorting techniques based on their efficiency and applicability in different scenarios. The students also learn about the principles of hashing and its practical applications in data retrieval and storage. The course culminates with a comprehensive study of shortest path algorithms, focusing on Dijkstra's and Bellman-Ford algorithms, enabling students to grasp the intricacies of pathfinding in a variety of network structures. By the end of the course, students are well-prepared to tackle real-world problems that require a solid understanding of data structure and algorithmic principles.