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 implicit representation of a complete binary tree as an array, or list, seems to have been first proposed by #key# Eytzinger: M. Eytzinger. Thesaurus principum hac aetate in Europa viventium (Cologne). 1590. In commentaries, ‘Eytzinger’ may appear in variant forms, including: Aitsingeri, Aitsingero, Aitsingerum, Eyzingern. #key#. He used this representation in books containing pedigree family trees of noble families. The BinaryHeap data structure described here was first introduced by #key# Williams: J. Williams. Algorithm 232: Heapsort. Communications of the ACM, 7(6):347–348, 1964. #key#.

The randomized `MeldableHeap` data structure described here appears to have first been proposed by #key# Gambin and Malinowski: A. Gambin and A. Malinowski. Randomized meldable priority queues. In SOFSEM98: Theory and Practice of Informatics, pages 344– 349. Springer, 1998. #key#. Other #key# meldable heap implementations: C. Crane. Linear lists and priority queues as balanced binary trees. Technical Report STAN-CS-72-259, Computer Science Department, Stanford University, 1972. #key# exist, including #key# leftist heaps: D. Knuth. Sorting and Searching, volume 3 of The Art of Computer Programming. Addison-Wesley, second edition, 1997. #key#, #key# binomial heaps: J. Vuillemin. A data structure for manipulating priority queues. Communications of the ACM, 21(4):309–315, 1978. #key#, #key# Fibonacci heaps: M. Fredman and R. Tarjan. Fibonacci heaps and their uses in improved network optimization algorithms. Journal of the ACM, 34(3):596–615, 1987. #key#, #key# pairing heaps: M. Fredman, R. Sedgewick, D. Sleator, and R. Tarjan. The pairing heap: A new form of self-adjusting heap. Algorithmica, 1(1):111– 129, 1986. #key#, and #key# skew heaps: D. Sleator and R. Tarjan. Self-adjusting binary trees. In Proceedings of the 15th Annual ACM Symposium on Theory of Computing, 25–27 April, 1983, Boston, Massachusetts, USA, pages 235–245. ACM, ACM, 1983. #key#, although none of these are as simple as the `MeldableHeap` structure.



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 Heaps

Additional notes