Gain insights into array-based and linked list-based data structures, explore advanced structures like skiplists and hashing, and discover template-based collections for efficient data storage and retrieval.

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Data structures and algorithms are essential in computer science since they play a crucial role in efficient information retrieval and processing, dealing with files, storing contacts on phones, social networks and web searches.

In this course, you’ll learn about the array-based implementation of various linear data structures, stack, and queues. You’ll also learn about linked list-based implementation. Next, you’ll explore advanced data structures like skiplists and hashing. You’ll learn how to implement a variety of trees and graphs, and data structures related to bits of an integer. Toward the end of the course, you’ll learn the implementation of structures based on external storage.

After completing this course, you’ll be able to create reusable programs with template-based collections that can efficiently analyze how to optimize the storage and retrieval of very large amounts of data. Overall, this course will enhance your productivity and performance as a software developer.

Data Structures with Generic Types in Python

# Additional notes
The running times of the depth-first-search and breadth-first-search algorithms are somewhat overstated by the following theorems we explained before:
1. When given as input a `Graph`, `g`, that is implemented using the `AdjacencyLists` data structure, the `bfs(g, r)` algorithm runs in $O(n + m)$ time.
2. When given as input a `Graph`, `g`, that is implemented using the `AdjacencyLists` data structure, the `dfs(g,r)` and `dfs2(g,r)` algorithms each run in $O(n + m)$ time.

Define $n_r$ as the number of vertices, $i$, of $G$, for which there exists a path from $r$ to $i$. Define $m_r$ as the number of edges that have these vertices as their sources. Then the following theorem is a more precise statement of the running times of the breadth-first-search and depth-first-search algorithms.

**Theorem:** When given as input a `Graph`, `g`, that is implemented using the `AdjacencyLists` data structure, the `bfs(g,r)`, `dfs(g,r)` and `dfs2(g,r)`
algorithms each run in $O(n_r + m_r)$ time.


Breadth-first-search seems to have been discovered independently by #key# Moore: E. F. Moore. The shortest path through a maze. In Proceedings of the International Symposium on the Theory of Switching, pages 285–292, 1959. #key# and #key# Lee: C. Y. Lee. An algorithm for path connection and its applications. IRE Transaction on Electronic Computers, EC-10(3):346–365, 1961. #key# in the contexts of maze exploration and circuit routing, respectively.



Overview

Array-Based Lists

Linked Lists

Skiplists

Hash Tables

Binary Trees

Random Binary Search Trees

Scapegoat Trees

Red-Black Trees

Heaps

Sorting Algorithms

Graphs

Data Structures for Integers

External Memory Searching

Wrap Up

Discussion on Graphs

Additional notes