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Professor Moffat has been a member of the academic staff at the University of Melbourne since 1987. This book has evolved out of his 20 years' teaching experience with first year students. The readable style is punctuated by more than 100 working programs and each chapter includes detailed case study, key points and exercises.
This edition of Data Abstraction and Problem Solving with Java: Walls and Mirrors employs the analogies of Walls (data abstraction) and Mirrors (recursion) to teach Java programming design solutions, in a way that beginning students find accessible. The book has a student-friendly pedagogical approach that carefully accounts for the strengths and weaknesses of the Java language. With this book, students will gain a solid foundation in data abstraction, object-oriented programming, and other problem-solving techniques. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.
"Focusing on data abstraction and data structures, the second edition of this very successful book continues to emphasize the needs of both the instructor and the student. The book illustrates the role of classes and abstract data types (ADTs) in the problem-solving process as the foundation for an object-oriented approach. Throughout the next, the distinction between specification and implementation is continually stressed. The text covers major applications of ADTs, such as searching a flight map and performing an event-driven simulation. It also offers early, extensive coverage of recursion and uses this technique in many examples and exercises. Overall, the lucid writing style, widespread use of examples, and flexible coverage of material have helped make this a leading book in the field." --Book Jacket.
In the tradition of Pascal and Turbo Pascal, authors Nell Dale and Chip Weems have teamed up with Mark Headington to offer Programming and Problem Solving with C++ for students in the CS1/C101 course. Written in the same style as the successful Pascal books, this text provides an accessible introduction to programming using C++ for beginning students. The first half of the text gives students a solid foundation in top-down programming techniques. The second half builds on this foundation and explains ADTs, the C++ class, encapsulation, information hiding, and object-oriented software development.
This text is intended for use in the second programming course Programming is a matter of learning by doing. Eric Roberts' Programming Abstractions in C++ gives students opportunities to practice and learn with engaging graphical assignments. A client-first approach to data structures helps students absorb, and then apply the material. Teaching and Learning Experience This program presents a better teaching and learning experience--for you and your students. It will help: Improve Student Comprehension with a Client-first Approach to Data Structures: To aid in student understanding, this book presents the full set of collection classes early. Defer the Presentation of C++ Features that Require a Detailed Understanding of the Underlying Machine: Introducing collection classes early enables students to master other equally important topics without having to struggle with low-level details at the same time. Engage Students with Exciting Graphical Assignments: An open-source library supports graphics and interactivity in a simple, pedagogically appropriate way. Support Instructors and Students: The companion website provides source code, sample run PDFs, answers to review questions, and more.
"Welcome to the third edition of my C++ text. The highly successful first edition was one of the first textbooks available for teaching C++ in the first programming course. The text was introduced at the 1994 ACM Conference in Phoenix when many were arguing the virtues of teaching C++ and OOP versus Pascal and structured programming in the first programming course. I argued at the time, and still argue, that students need to be taught problem solving early-on using both the structured and object-oriented paradigms and, because of its hybrid nature, C++ is the only language suited to learning both of these paradigms. Since then, many institutions have made the switch from Pascal to C++ for just this reason, as well as the intense industry support for C++ language. As a result, this third edition continues to provide an introduction to both structured and object-oriented problem solving techniques using the C++ language. Of course, many improvements have been made based on using the text in numerous classrooms all over the world since 1994. As with earlier editions, the text starts from the beginning, assuming no previous knowledge of C, or any other programming language. This text is appropriate for any introductory programming (CS1 course using the C++ language as well as experienced programmers wanting an introduction to structured and object-oriented problem solving techniques using the C++ language"-- Book Preface.
Koffman and Wolfgang introduce data structures in the context of C++ programming. They embed the design and implementation of data structures into the practice of sound software design principles that are introduced early and reinforced by 20 case studies. Data structures are introduced in the C++ STL format whenever possible. Each new data structure is introduced by describing its interface in the STL. Next, one or two simpler applications are discussed then the data structure is implemented following the interface previously introduced. Finally, additional advanced applications are covered in the case studies, and the cases use the STL. In the implementation of each data structure, the authors encourage students to perform a thorough analysis of the design approach and expected performance before actually undertaking detailed design and implementation. Students gain an understanding of why different data structures are needed, the applications they are suited for, and the advantages and disadvantages of their possible implementations. Case studies follow a five-step process (problem specification, analysis, design, implementation, and testing) that has been adapted to object-oriented programming. Students are encouraged to think critically about the five-step process and use it in their problem solutions. Several problems have extensive discussions of testing and include methods that automate the testing process. Some cases are revisited in later chapters and new solutions are provided that use different data structures. The text assumes a first course in programming and is designed for Data Structures or the second course in programming, especially those courses that include coverage of OO design and algorithms. A C++ primer is provided for students who have taken a course in another programming language or for those who need a review in C++. Finally, more advanced coverage of C++ is found in an appendix. Course Hierarchy: Course is the second course in the CS curriculum Required of CS majors Course names include Data Structures and Data Structures & Algorithms
An entertaining and captivating way to learn the fundamentals of using algorithms to solve problems The algorithmic approach to solving problems in computer technology is an essential tool. With this unique book, algorithm guru Roland Backhouse shares his four decades of experience to teach the fundamental principles of using algorithms to solve problems. Using fun and well-known puzzles to gradually introduce different aspects of algorithms in mathematics and computing. Backhouse presents you with a readable, entertaining, and energetic book that will motivate and challenge you to open your mind to the algorithmic nature of problem solving. Provides a novel approach to the mathematics of problem solving focusing on the algorithmic nature of problem solving Uses popular and entertaining puzzles to teach you different aspects of using algorithms to solve mathematical and computing challenges Features a theory section that supports each of the puzzles presented throughout the book Assumes only an elementary understanding of mathematics Let Roland Backhouse and his four decades of experience show you how you can solve challenging problems with algorithms!
Thes book has three key features : fundamental data structures and algorithms; algorithm analysis in terms of Big-O running time in introducied early and applied throught; pytohn is used to facilitates the success in using and mastering data strucutes and algorithms.
The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.