Advanced Data Structures

This module covers various forms of balanced binary trees and heaps, which are crucial for maintaining performance in dynamic datasets. Students explore Threaded Binary Trees (TBT) and their traversal mechanisms, followed by an in-depth study of Max Heaps and Min Heaps for priority-based processing. The concept of AVL Trees, a type of height-balanced tree, is discussed along with its insertion, deletion, and search operations. The module also introduces weight-balanced trees, highlighting their importance in optimizing search and update operations in data-intensive applications.

Focusing on graph data structures, this module introduces students to core graph concepts, including storage representations using adjacency lists and matrices. It covers key traversal techniques like Depth First Search (DFS) and Breadth First Search (BFS), enabling students to navigate and explore complex graph structures. Essential algorithms such as Prim’s and Kruskal’s for finding Minimum Spanning Trees are also discussed, along with Topological Sorting and Dijkstra’s Algorithm for finding the shortest path in weighted graphs. This module is vital for understanding real-world problems like network routing and dependency resolution.

This module delves into advanced tree structures and indexing techniques that are widely used in databases and file systems. Students learn about the purpose and methods of indexing, and explore various multiway tree structures such as B-Trees and B+ Trees, which enable efficient data insertion, deletion, and retrieval. The module also introduces self-balancing trees like Red-Black Trees, AA Trees, and Splay Trees, explaining their structure and how they maintain balance for optimal performance. This module enhances students' ability to design data systems that are fast, scalable, and efficient.