A data structure is a way of organizing data so that it can be accessed and/or modified efficiently. Learning about data structures and algorithms gives you a better insight on how to solve common programming problems. Most of the problems faced everyday by programmers have been solved, tried and tested. Knowing how these solutions work, ensures that the right tool is chosen when faced with these problems.
This book teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You’ll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications.
If you want to better understand common data structures and algorithms by following code examples in Java and improve your application efficiency, then this is the book for you. It helps to have basic knowledge of Java, mathematics and object-oriented programming techniques.
Chapter 1, Algorithms and Complexities, covers how to define an algorithm, measure algorithmic complexity, and identify algorithms with different complexities. It also covers how to assess various examples with different runtime complexities
Chapter 2, Sorting Algorithms and Fundamental Data Structures, explores bubble, quick, and merge sort. We will also introduce data structures and study various implementations and use cases of linked lists, queues, and stacks. We will also see how some data structures can be used as building blocks to build more complex ones.
Chapter 3, Hash Tables and Binary Search Trees, talks about data structures for implementing the data dictionary operation. In addition, binary trees also give us the ability to perform various range queries. We will also see examples of both data structures, and implementations of these operations.
Chapter 4, Algorithm Design Paradigms, discusses three different algorithm design paradigms along with example problems, and discusses how to identify whether problems may be solvable by one of the given paradigms.
Chapter 5, String Matching Algorithms, introduces the string matching problem. This chapter also introduces you to the string matching algorithms, starting from the naive search algorithm and improving it by using the rules introduced by Boyer and Moore. We'll also explore some other string matching algorithms without going into too much detail about them.
Chapter 6, Graphs, Prime Numbers, and Complexity Classes, introduces graphs, formalizing what they are and showing two different ways to represent them in computer programs. Later, we'll take a look at ways of traversing graphs, using them as building blocks for building more complex algorithms. We'll also look at two different algorithms for finding shortest paths in a graph.
For successful completion of this book, you will require a computer system with at least an i3 processor, 4 GB RAM, 10 GB hard disk and an internet connection. Along with this you would require the following software:
- Java SE Development Kit, JDK 8 (or a later version)
- Git client
- Java IDE (IntelliJ or Eclipse)
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There are a number of text conventions used throughout this book.
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quickSort(array, start, end) if(start < end) p = partition(array, start, end) quickSort(array, start, p - 1) quickSort(array, p + 1, end)
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Activity: These are scenario-based activities that will let you practically apply what you've learned over the course of a complete section. They are typically in the context of a real-world problem or situation.
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