Gain insights into array and linked list-based implementations, delve into advanced structures like skiplists and trees, and explore template-based collections for efficient data optimization 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 C++

# Additional notes
The `List`, `USet`, and `SSet` interfaces described before are influenced by the [Java Collections Framework](http://download.oracle.com/javase/1.5.0/docs/guide/collections/). These are essentially simplified versions of the `List`, `Set`, `Map`, `SortedSet`, and `SortedMap` interfaces found in the Java Collections Framework. The accompanying source code includes wrapper classes for making `USet` and `SSet` implementations into `Set`, `Map`, `SortedSet`, and `SortedMap` implementations.

For a superb (and free) treatment of the mathematics discussed in this chapter, including asymptotic notation, logarithms, factorials, Stirling’s approximation, basic probability, and lots more, see the #key# textbook by Leyman, Leighton, and Meyer: E. Lehman, F. T. Leighton, and A. R. Meyer. Mathematics for Computer Science. 2011. #key#. For a gentle calculus text that includes formal definitions of exponentials and logarithms, see the (freely available) #key# classic text by Thompson: S. P. Thompson. Calculus Made Easy. MacMillan, Toronto, 1914. Project Gutenberg EBook 33283. URL: http://www.gutenberg. org/ebooks/33283 [cited 2012-06-14]. #key#.


For more information on basic probability, especially as it relates to computer science, see the #key# textbook by Ross: S. M. Ross. Probability Models for Computer Science. Academic Press, Inc., Orlando, FL, USA, 2001. #key#. Another good reference, which covers both asymptotic notation and probability, is the #key# textbook by Graham, Knuth, and Patashnik: R. L. Graham, D. E. Knuth, and O. Patashnik. Concrete Mathematics. Addison-Wesley, 2nd edition, 1994. #key#.




Discover more aspects regarding different interfaces.


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

Additional notes