Advanced Programming | 26. Advanced Data Structures (e.g., Trees, Graphs) by Abraham | Learn Smarter
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26. Advanced Data Structures (e.g., Trees, Graphs)

Advanced data structures, including trees and graphs, are essential for efficient data manipulation as programs scale in complexity. This chapter delves into a variety of structures such as binary trees, binary search trees, heaps, tries, and graphs, exploring their properties, operations, and real-world applications. Understanding these structures enhances problem-solving capabilities in complex software development environments.

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Sections

  • 26

    Advanced Data Structures (E.g., Trees, Graphs)

    Advanced data structures like trees, heaps, tries, and graphs are essential for efficient data manipulation and storage in complex programs.

    Learning Practice

  • 26.1

    Trees

    This section introduces trees as hierarchical data structures used for efficient data manipulation and storage.

    Learning Practice

  • 26.1.1

    Overview Of Trees

    Trees are hierarchical data structures consisting of nodes connected by edges, with one root node and multiple levels of children.

    Learning Practice

  • 26.1.2

    Binary Trees

    Binary trees are hierarchical data structures where each node has at most two children, with specific traversal methods for navigating the tree.

    Learning Practice

  • 26.1.3

    Binary Search Trees (Bsts)

    Binary Search Trees (BSTs) are a type of data structure that maintains sorted order, facilitating efficient insertion, deletion, and searching operations.

    Learning Practice

  • 26.1.4

    Balanced Trees

    Balanced trees maintain a balanced structure to optimize search, insertion, and deletion operations.

    Learning Practice

  • 26.1.5

    Heaps

    Heaps are complete binary trees utilized for implementing priority queues, supporting efficient insertion and extraction operations.

    Learning Practice

  • 26.1.6

    Tries (Prefix Trees)

    Tries are tree-based data structures primarily used for efficiently storing and retrieving strings, especially for applications like autocomplete and spell checking.

    Learning Practice

  • 26.2

    Graphs

    Graphs are complex data structures composed of vertices and edges, used to model relationships in non-linear data.

    Learning Practice

  • 26.2.1

    Introduction To Graphs

    Graphs are complex data structures consisting of vertices and edges that can be directed, undirected, weighted, or unweighted.

    Learning Practice

  • 26.2.2

    Representation Of Graphs

    This section covers the two primary ways to represent graphs: adjacency matrices and adjacency lists, detailing their structures and space complexities.

    Learning Practice

  • 26.2.3

    Graph Traversal

    Graph traversal methods, including BFS and DFS, are crucial for exploring graph structures efficiently.

    Learning Practice

  • 26.2.4

    Applications Of Graphs

    This section covers the diverse applications of graph theory, including algorithms for shortest paths, cycle detection, and network flow.

    Learning Practice

  • 26.2.5

    Dijkstra's Algorithm (Shortest Path)

    Dijkstra's Algorithm efficiently finds the shortest paths from a source node to all other nodes in a weighted graph with non-negative weights.

    Learning Practice

  • 26.2.6

    Minimum Spanning Tree

    This section discusses Minimum Spanning Trees (MST) and introduces Prim's and Kruskal's algorithms for their calculation.

    Learning Practice

  • 26.3

    Comparative Analysis Of Data Structures

    This section compares various advanced data structures, focusing on their use cases, average time complexities, and space complexities.

    Learning Practice

  • 26.4

    Real-World Applications

    This section highlights the practical applications of advanced data structures like trees, heaps, tries, and graphs in various domains.

    Learning Practice

  • 26.5

    Summary

    Advanced data structures such as trees and graphs provide essential tools for efficient data manipulation in complex software applications.

    Learning Practice

References

Chapter_26_Advan.pdf

Class Notes

Memorization

What we have learnt

  • Trees provide a hierarchica...
  • Graphs serve as a crucial f...
  • Data structures like heaps ...

Final Test

Revision Tests