Priority queues are advanced data structures that allow for efficient retrieval of the highest priority element. This module discusses their implementation and applications in various algorithms.
This module introduces the fundamental concepts of data structures and algorithms, providing a solid foundation for the course. Understanding these basics is crucial for tackling more complex topics later on.
In this module, students will delve into stacks, a fundamental data structure. Stacks operate on a Last In, First Out (LIFO) principle, making them essential in various applications like expression evaluation and backtracking algorithms.
This module examines queues and linked lists, two essential data structures. Queues follow a First In, First Out (FIFO) model, while linked lists offer dynamic memory allocation, allowing efficient data manipulation.
Dictionaries, also known as hash maps, are explored in this module. They provide efficient data retrieval through key-value pairs, making them indispensable in scenarios requiring fast lookups and data association.
This module covers hashing techniques, crucial for implementing dictionaries and optimizing data retrieval. Students will learn about hash functions, collisions, and strategies for handling them effectively.
Trees are fundamental in computer science, and this module introduces various types, including binary trees and their applications. Trees facilitate efficient searching, sorting, and hierarchical data organization.
This module focuses on tree walks and traversals, including pre-order, in-order, and post-order. Understanding these techniques is vital for various algorithms and data manipulation tasks.
Ordered dictionaries extend the concept of traditional dictionaries, allowing for data retrieval in a sorted manner. This module introduces their implementation and benefits in programming.
This module covers the deletion of nodes from various data structures, an essential operation for maintaining data integrity and efficiency. Different strategies for deletion in trees and lists will be discussed.
Quick Sort is one of the most efficient sorting algorithms. This module explores its principles, implementation, and the divide-and-conquer strategy that makes it effective for large datasets.
This module delves into AVL trees, a type of self-balancing binary search tree. Students will learn about their properties and how balancing ensures efficient operations.
Continuing from the previous module, this segment further explores AVL trees and their balancing techniques, emphasizing the importance of maintaining tree height for optimal performance.
This module revisits various tree types, emphasizing their roles in different applications and scenarios. Understanding the versatility of trees is crucial for effective algorithm implementation.
Red-Black trees are another form of self-balancing binary search trees. This module introduces their properties, structure, and advantages over other tree types.
This module focuses on the insertion process in Red-Black trees. Students will learn the balancing operations necessary to maintain the tree's properties during insertion.
This module discusses disk-based data structures, which are crucial for managing data that exceeds memory capacity. Students will explore techniques that optimize disk I/O operations.
This case study examines searching for patterns using various data structures. Students will learn how different algorithms can be applied to solve real-world problems effectively.
Tries are specialized tree structures used for efficient retrieval of keys in a dataset. This module explores their structure and applications in tasks such as autocomplete.
This module focuses on data compression techniques. Students will explore various algorithms that reduce data size while maintaining integrity, essential for efficient storage and transmission.
Priority queues are advanced data structures that allow for efficient retrieval of the highest priority element. This module discusses their implementation and applications in various algorithms.
This module examines binary heaps, a common implementation of priority queues. Students will learn about their properties and how they facilitate efficient data operations.
This module discusses the reasons behind sorting data, emphasizing its significance in various applications. Students will learn about different sorting strategies and their efficiency.
Continuing the discussion on sorting, this module covers advanced sorting techniques and their applications. Students will analyze the performance of each method in different scenarios.
This module introduces graphs as essential data structures for representing relationships. Students will learn about different types of graphs and their applications in real-world scenarios.
In this module, various data structures for representing graphs will be discussed. Students will learn about adjacency lists, matrices, and their pros and cons in different contexts.
This module explores applications of breadth-first search (BFS), a fundamental algorithm for traversing or searching tree or graph structures. Students will analyze its efficiency and use cases.
Depth-first search (DFS) is another critical algorithm for exploring graphs. This module introduces its principles, implementation, and how it contrasts with BFS in various scenarios.
This module covers applications of depth-first search in solving real-world problems. Students will learn how DFS can be effectively applied to various algorithmic challenges.
This module focuses on DFS in directed graphs, highlighting its unique properties and challenges compared to undirected graphs. Students will learn about its implementation in this context.
This module discusses the applications of DFS in directed graphs, emphasizing its utility in various algorithmic problems and scenarios where directed relationships are present.
This module introduces minimum spanning trees (MST), essential for connecting nodes in a graph with minimal total edge weight. Students will explore various algorithms used to determine an MST.
This module examines the Union-Find algorithm, a crucial technique in network connectivity and component analysis. Students will learn how to implement and optimize this algorithm for efficiency.
This module covers Prim's algorithm for finding minimum spanning trees, providing step-by-step guidance on its implementation and analysis of its performance in various scenarios.
Single source shortest path algorithms play a vital role in graph theory. This module explores various methods for calculating the shortest path from a single starting node.
This module discusses the correctness of Dijkstra's algorithm, a fundamental method for finding shortest paths in graphs. Students will analyze its principles and practical applications.
This module revisits the concept of single source shortest paths, emphasizing advanced techniques and optimizations that enhance performance in various contexts and applications.