Quick Sort is an efficient sorting algorithm based on the divide-and-conquer principle. This module covers its implementation, performance analysis, and real-world applications.
This introductory lecture sets the stage for understanding data structures and algorithms. It covers essential definitions and the significance of these concepts in programming.
In this module, students will explore stacks, a fundamental data structure that operates on a Last In First Out (LIFO) principle. Key operations such as push, pop, and peek will be discussed.
This module covers queues and linked lists, essential data structures for managing collections of data. Students will learn about the FIFO principle of queues and the dynamic nature of linked lists.
Dictionaries are key-value pairs that enable efficient data retrieval. This module covers their implementation and use cases, highlighting hash tables as a common underlying structure.
This module introduces hashing, a technique that maps data to fixed-size values. Students will learn about hash functions, collision resolution, and their applications in data storage.
Trees are hierarchical data structures that organize data in a parent-child relationship. This module introduces binary trees, their properties, and applications in data organization.
This module focuses on tree traversal techniques such as in-order, pre-order, and post-order. Students will learn how to navigate trees effectively to access data.
Ordered dictionaries maintain the order of elements based on insertion. This module discusses their implementation and advantages over traditional dictionaries.
Deletion is crucial for data management. This module covers deletion techniques in various data structures, ensuring students understand the implications of removing data.
Quick Sort is an efficient sorting algorithm based on the divide-and-conquer principle. This module covers its implementation, performance analysis, and real-world applications.
AVL Trees are self-balancing binary search trees that maintain sorted data. This module covers their properties, rotations, and how they ensure optimal search times.
This module continues the discussion of AVL Trees, diving deeper into their balancing techniques and practical applications in ensuring efficient data retrieval.
This module revisits trees, expanding on different types such as binary trees and their applications in various algorithms and data management techniques.
Red-Black Trees are another form of self-balancing binary search trees. This module covers their properties, advantages, and how they maintain balance during insertions and deletions.
This module focuses on insertion operations in Red-Black Trees, detailing the necessary rotations and color changes to maintain tree balance.
In this module, students will explore disk-based data structures designed for efficient storage and retrieval of large data sets, optimizing performance for database applications.
This case study examines various algorithms for searching patterns within data. Students will apply data structures to solve real-world problems related to pattern recognition.
Tries are specialized tree structures used for storing associative data structures. This module covers their construction, applications in searching, and efficiency advantages.
Data compression techniques are vital in reducing data size for storage and transmission. This module covers various algorithms and their effectiveness in different scenarios.
Priority queues are essential for scheduling processes and tasks in computing. This module explores their implementation, operations, and applications in real-world scenarios.
Binary Heaps are a type of complete binary tree that supports efficient priority queue operations. This module covers their structure, properties, and applications in algorithms.
This module discusses the importance of sorting in computer science. Students will learn about different sorting algorithms and their impact on algorithm efficiency and data handling.
This module provides an overview of advanced sorting techniques, comparing various algorithms for effectiveness and efficiency in different contexts.
Graphs are versatile data structures used to represent relationships between entities. This module covers their representation, traversal methods, and applications in real-world scenarios.
This module focuses on data structures specifically designed for graphs, including adjacency lists and matrices, and their advantages in graph representation.
This module examines two applications of Breadth First Search (BFS), demonstrating its effectiveness in finding the shortest paths and connected components in graphs.
Depth First Search (DFS) is another fundamental graph traversal algorithm. This module covers its implementation, advantages, and scenarios where it is most effective.
This module explores various applications of DFS, including pathfinding and cycle detection in graphs, showcasing its versatility in solving complex problems.
This module covers DFS in directed graphs, emphasizing the differences in traversal compared to undirected graphs and highlighting specific challenges.
In this module, students will discover applications of DFS in directed graphs, focusing on topological sorting and strongly connected components.
This module delves into Minimum Spanning Trees (MST), covering their definition, significance, and algorithms used to find them, such as Kruskal's and Prim's.
The Union-Find algorithm is essential for managing disjoint sets. This module covers its implementation and applications in network connectivity and clustering.
Prim's Algorithm is a popular method for finding Minimum Spanning Trees. This module dives into its step-by-step process and practical applications in network design.
This module introduces Single Source Shortest Paths, a critical concept in graph theory. Students will learn various algorithms to efficiently find paths from a single source.
This module covers the correctness of Dijkstra's Algorithm, discussing its implementation, proof of correctness, and applications in routing and navigation systems.
This module revisits Single Source Shortest Paths, emphasizing different algorithms and their comparative efficiencies in various graph structures.