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Everyday data structures

You're reading from   Everyday data structures A practical guide to learning data structures simply and easily

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787121041
Length 344 pages
Edition 1st Edition
Languages
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Author (1):
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William Smith William Smith
Author Profile Icon William Smith
William Smith
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Toc

Table of Contents (14) Chapters Close

Preface 1. Data Types: Foundational Structures FREE CHAPTER 2. Arrays: Foundational Collections 3. Lists: Linear Collections 4. Stacks: LIFO Collections 5. Queues: FIFO Collections 6. Dictionaries: Keyed Collections 7. Sets: No Duplicates 8. Structs: Complex Types 9. Trees: Non-Linear Structures 10. Heaps: Ordered Trees 11. Graphs: Values with Relationships 12. Sorting: Bringing Order Out Of Chaos 13. Searching: Finding What You Need

Advanced topics

Now that we have seen how arrays are used in common practice, let's examine a few advanced topics relating to arrays: search patterns and variations on the basic types of objects that can be stored in an array.

Linear search

When learning about data structures, it is impossible to avoid discussing the subjects of searching and sorting. Without the ability to search a data structure, the data would be fairly useless to us. Without the ability to sort the data set for use in a particular application, the data would be extremely tedious to manage.

The steps or process we follow to perform a search or a sort of a particular data structure are called an algorithm. The performance or the complexity of an algorithm in computer science is measured using the big O notation, which is derived from the function f(n) = O (g(n)), read as f of n equals big oh of g of n. In the simplest terms, big O is the terminology we use to describe the worst case for how long an algorithm takes...

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