Solve real world problems with Java using multiple classes. Learn how to create programming solutions that scale using Java interfaces. Recognize that software engineering is more than writing code - it also involves logical thinking and design. By the end of this course you will have written a program that analyzes and sorts earthquake data, and developed a predictive text generator. After completing this course, you will be able to: 1. Use sorting appropriately in solving problems; 2. Develop classes that implement the Comparable interface; 3. Use timing data to analyze empirical performance; 4. Break problems into multiple classes, each with their own methods; 5. Determine if a class from the Java API can be used in solving a particular problem; 6. Implement programming solutions using multiple approaches and recognize tradeoffs; 7. Use object-oriented concepts including interfaces and abstract classes when developing programs; 8. Appropriately hide implementation decisions so they are not visible in public methods; and 9. Recognize the limitations of algorithms and Java programs in solving problems. 10. Recognize standard Java classes and idioms including exception-handling, static methods, java.net, and java.io packages.
Summary
Получаемые навыки
Programming Principles, Interfaces, Software Design, Java Programming
Рецензии
SB
Aug 13, 2016
This course was another of many well-taught courses of Duke University. The GPS library use to find locations of earthquakes particularly of very interesting. Thank you Duke University
LY
Sep 28, 2016
devoted professors, appropriate topics which can lead to the correct understanding of each Java object, so we can realize OO programming ideas, not only how to type codes.
In this module, you will explore some of the underlying concepts of predictive text. The first lesson will introduce random character generation and then how to train the character selection based on an input text. The second lesson will extend this concept to complete words. By the end of this module, you will be able to: (1) base random text generation on the frequency of characters in a training text, (2) collect a set of characters that occur in a text after randomly chosen initial character(s) to create a semi-random text, (3) extend the predictive text generation to use whole words, and (4) implement your own .equals method to compare complex data types.
Преподаватели
Robert Duvall
Lecturer
Owen Astrachan
Professor of the Practice
Andrew D. Hilton
Associate Professor of the Practice
Susan H. Rodger
Professor of the Practice
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