Computer science (Pearson Education)

Today, learning to program and understanding the basics of computation isn't just indispensable for every science and engineering student: it's crucial for everyone who wants to understand the world they live in. In Computer Science: An Interdisciplinary Approach, pioneering Princeton computer science professors Robert Sedgewick and Kevin Wayne introduce core Java programming techniques in a scientific context, while also demystifying computation and illuminating its intellectual underpinnings. Writing for students and professionals of all types and backgrounds, Sedgewick and Wayne draw on all they've learned in teaching hundreds of thousands of beginners worldwide, both in person and online. The companion text to their eagerly-anticipated Coursera Computer Science MOOC, this book's intelligent, broad-based approach draws on applications from science, mathematics, engineering, and commercial computing. Throughout, they engage students aby teaching how to solve interesting and significant problems - not toy problems. Coverage includes:Elements of programming: built-in data types, conditionals, loops, arrays, I/O, and moreFunctions and modules: static methods, libraries, clients, and recursionObject-oriented programming: understanding, creating, and designing data typesAlgorithms and data structures: performance, sorting, searching, stacks, queues, and symbol tablesComputing machines: data representations, instruction set architecture, programming, simulations, and moreBuilding a computer: gates, circuits, components, and CPUsTheory of computation: formal languages, abstract machines, computability, universality, and intractabilityEach chapter contains questions and answers, exercises, creative exercises, and a compelling, classroom-tested case study - all reflecting Sedgewick and Wayne's 20+ years of experience teaching introductory computer science at Princeton. The book is complemented by extensive resources on a comprehensive website, including hundreds of Java programs and real-world data sets.