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
Both singly-linked and doubly-linked lists are established techniques, having been used in programs for over $40$ years. They are discussed, for example, by #key# Knuth: D. Knuth. Fundamental Algorithms, volume 1 of The Art of Computer Programming. Addison-Wesley, third edition, 1997. #key#. Even the `SEList` data structure seems to be a well-known data structures exercise. The `SEList` is sometimes referred to as an #key# unrolled linked list: Z. Shao, J. H. Reppy, and A. W. Appel. Unrolling lists. In Proceed- ings of the 1994 ACM conference LISP and Functional Programming (LFP’94), pages 185–195, New York, 1994. ACM. #key#.

Another way to save space in a doubly-linked list is to use so-called XOR-lists. In an XOR-list, each node, $u$, contains only one pointer, called `u.nextprev`, that holds the bitwise exclusive-or of `u.prev` and `u.next`. The list itself needs to store two pointers, one to the `dummy` node and one to `dummy.next` (the first node, or `dummy` if the list is empty). This technique uses the fact that, if we have pointers to `u` and `u.prev`, then we can extract `u.next` using the formula

$$
\text{u.next} = \text{u.prev} \ \^{} \ \text{u.nextprev} 
$$

(Here $^$ computes the bitwise exclusive-or of its two arguments.) This technique complicates the code a little and is not possible in some languages, like Java and Python, that have garbage collection but gives a doubly-linked list implementation that requires only one pointer per node. See #key# Sinha’s magazine article: P. Sinha. A memory-efficient doubly linked list. Linux Journal, 129, 2005. URL: http://www.linuxjournal.com/article/6828 [cited 2013-06-05]. #key# for a detailed discussion of XOR-lists.

Discover more aspects regarding linked lists.

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 Linked Lists

Additional notes