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Everyday data structures

Everyday data structures

By : Smith
Buy this Book
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Everyday data structures

Everyday data structures

By: Smith
Buy this Book

Overview of this book

Explore a new world of data structures and their applications easily with this data structures book. Written by software expert William Smith, you?ll learn how to master basic and advanced data structure concepts. ? Fully understand data structures using Java, C and other common languages ? Work through practical examples and learn real-world applications ? Get to grips with data structure problem solving using case studies
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Data Types: Foundational Structures
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1. Data Types: Foundational Structures
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Numeric data types
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Boolean data type
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Strings
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Summary
2
2. Arrays: Foundational Collections
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2. Arrays: Foundational Collections
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Mutable versus immutable arrays
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Advanced topics
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Summary
3
3. Lists: Linear Collections
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3. Lists: Linear Collections
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List implementations
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Instantiating lists
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Revisiting users logged in to a service
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Doubly linked list
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Searching
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A few pointers
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Summary
4
4. Stacks: LIFO Collections
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4. Stacks: LIFO Collections
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Initializing stacks
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Case study: motion planning algorithm
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Advanced topics - stack implementations
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Summary
5
5. Queues: FIFO Collections
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5. Queues: FIFO Collections
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Initializing queues
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Case study: customer service
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Advanced topics
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Summary
6
6. Dictionaries: Keyed Collections
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6. Dictionaries: Keyed Collections
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Initializing dictionaries
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Case study: arcade ticket totals
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Advanced topics
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Summary
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7. Sets: No Duplicates
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7. Sets: No Duplicates
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Set theory
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Initializing sets
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Example: revisiting users logged in to a service
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Case-study: music playlists
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Advanced topics
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Summary
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8. Structs: Complex Types
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8. Structs: Complex Types
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The essentials
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Enums
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Summary
9
9. Trees: Non-Linear Structures
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9. Trees: Non-Linear Structures
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Tree data structure versus tree data type
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Tree terminology
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Common operations
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Instantiating trees
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Tree structures
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Recursion
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Traversal
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Summary
10
10. Heaps: Ordered Trees
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10. Heaps: Ordered Trees
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Heap implementations
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Heap operations
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Instantiating heaps
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Min heap structure
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Common applications
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Summary
11
11. Graphs: Values with Relationships
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11. Graphs: Values with Relationships
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Visual graph concepts
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Graph operations
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Graph implementations
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The graph data structure
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Summary
12
12. Sorting: Bringing Order Out Of Chaos
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12. Sorting: Bringing Order Out Of Chaos
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Selection sort
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Insertion sort
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Bubble sort
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Quick sort
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Merge sort
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Bucket sort
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Summary
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13. Searching: Finding What You Need
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13. Searching: Finding What You Need
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Linear search
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Binary search
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Jump search
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Summary

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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Tech Concepts
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Programming languages
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