Description

Book Synopsis

Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview.

Classic Computer Science Problems in Java will teach you techniques to solve common-but-tricky programming issues. You’ll explore foundational coding methods, fundamental algorithms, and artificial intelligence topics, all through code-centric Java tutorials and computer science exercises. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems!

Key Features

· Recursion, memorization, bit manipulation

· Search algorithms

· Constraint-satisfaction problems

· Graph algorithms

· K-means clustering

For intermediate Java programmers.

About the technology

In any computer science classroom you’ll find a set of tried-and-true algorithms, techniques, and coding exercises. These techniques have stood the test of time as some of the best ways to solve problems when writing code, and expanding your Java skill set with these classic computer science methods will make you a better Java programmer.

David Kopec is an assistant professor of computer science and innovation at Champlain College in Burlington, Vermont. He is the author of Dart for Absolute Beginners (Apress, 2014), Classic Computer Science Problems in Swift (Manning, 2018), and Classic Computer Science Problems in Python (Manning, 2019).

Classic Computer Science Problems in Java

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    £37.99

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    RRP £39.99 – you save £2.00 (5%)

    Order before 4pm today for delivery by Mon 29 Jun 2026.

    A Paperback / softback by David Kopec

    7 in stock

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      View other formats and editions of Classic Computer Science Problems in Java by David Kopec

      Publisher: Manning Publications
      Publication Date: 12/03/2021
      ISBN13: 9781617297601, 978-1617297601
      ISBN10: 1617297607

      Description

      Book Synopsis

      Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview.

      Classic Computer Science Problems in Java will teach you techniques to solve common-but-tricky programming issues. You’ll explore foundational coding methods, fundamental algorithms, and artificial intelligence topics, all through code-centric Java tutorials and computer science exercises. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems!

      Key Features

      · Recursion, memorization, bit manipulation

      · Search algorithms

      · Constraint-satisfaction problems

      · Graph algorithms

      · K-means clustering

      For intermediate Java programmers.

      About the technology

      In any computer science classroom you’ll find a set of tried-and-true algorithms, techniques, and coding exercises. These techniques have stood the test of time as some of the best ways to solve problems when writing code, and expanding your Java skill set with these classic computer science methods will make you a better Java programmer.

      David Kopec is an assistant professor of computer science and innovation at Champlain College in Burlington, Vermont. He is the author of Dart for Absolute Beginners (Apress, 2014), Classic Computer Science Problems in Swift (Manning, 2018), and Classic Computer Science Problems in Python (Manning, 2019).

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