Discovering Computer Science

Interdisciplinary Problems, Principles, and Python Programming
1. Auflage 2015
ISBN: 978-1-4822-5414-3
Verlag: Apple Academic Press Inc.

Buch, Englisch, 750 Seiten, Format (B × H): 176 mm x 246 mm, Gewicht: 1866 g

Reihe: Chapman & Hall/CRC Textbooks in Computing

Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming introduces computational problem solving as a vehicle of discovery in a wide variety of disciplines. With a principles-oriented introduction to computational thinking, the text provides a broader and deeper introduction to computer science than typical introductory programming books.Organized around interdisciplinary problem domains, rather than programming language features, each chapter guides students through increasingly sophisticated algorithmic and programming techniques. The author uses a spiral approach to introduce Python language features in increasingly complex contexts as the book progresses.The text places programming in the context of fundamental computer science principles, such as abstraction, efficiency, and algorithmic techniques, and offers overviews of fundamental topics that are traditionally put off until later courses.The book includes thirty well-developed independent projects that encourage students to explore questions across disciplinary boundaries. Each is motivated by a problem that students can investigate by developing algorithms and implementing them as Python programs.The book's accompanying website — http://discoverCS.denison.edu — includes sample code and data files, pointers for further exploration, errata, and links to Python language references.Containing over 600 homework exercises and over 300 integrated reflection questions, this textbook is appropriate for a first computer science course for computer science majors, an introductory scientific computing course or, at a slower pace, any introductory computer science course.

Zielgruppe

Students in CS1 courses; science students from other majors who are interested in learning CS concepts.

Autoren/Hrsg.

Havill, Jessen

Inhaltsverzeichnis

What is Computation? Problems and Abstraction Algorithms and Programs Efficient AlgorithmsComputers Are DumbSummary Further Discovery Elementary ComputationsWelcome to the Circus ArithmeticWhat’s In a Name? Using FunctionsBinary ArithmeticSummary Further Discovery Visualizing Abstraction Data Abstraction Visualization with TurtlesFunctional AbstractionProgramming in StyleA Return to FunctionsScope and NamespacesSummary Further Discovery Growth and DecayDiscrete ModelsVisualizing Population Changes Conditional IterationContinuous ModelsNumerical AnalysisSumming UpFurther Discovery ProjectsForks in the Road Random WalksPseudorandom Number GeneratorsSimulating Probability DistributionsBack to BooleansA Guessing Game Summary Further Discovery ProjectsText, Documents, and DNA Counting words Text DocumentsEncoding StringsLineartime AlgorithmsAnalyzing TextComparing Texts GenomicsSummaryFurther Discovery ProjectsDesigning Programs How to Solve ItDesign by ContractTestingSummary Further Discovery Data Analysis Summarizing Data Creating and Modifying ListsFrequencies, Modes, and HistogramsReading Tabular Data Designing Efficient AlgorithmsLinear Regression Data ClusteringSummaryFurther Discovery ProjectsFlatlandTwo-Dimensional Data The Game of LifeDigital ImagesSummaryFurther DiscoveryProjectsSelf-Similarity and Recursion FractalsRecursion and IterationThe Mythical Tower of HanoiRecursive Linear SearchDivide and ConquerLindenmayer SystemsSummary Further Discovery ProjectsOrganizing Data Binary SearchSelection SortInsertion SortEfficient SortingTractable and Intractable AlgorithmsSummaryFurther Discovery ProjectsNetworks Modeling with GraphsShortest Paths It’s A Small WorldRandom Graphs Summary Further DiscoveryProjectsAbstract Data Types Designing ClassesOperators and Special Methods Modules A Flocking Simulation A Stack ADT A Dictionary ADTSummary Further DiscoveryProjectsAppendix A: Installing Python An Integrated Distribution Manual Installation Appendix B: Python Library ReferenceMath Module Turtle Methods Screen Methods Matplotlib.Pyplot Module Random Module String MethodsList Methods Image Module Special Methods Bibliography Index

Über Autor(innen)

Jessen Havill is a professor of computer science and the Benjamin Barney Chair of Mathematics at Denison University, where he has been on the faculty since 1998. Dr. Havill teaches courses across the computer science curriculum, as well as an interdisciplinary elective in computational biology. He was awarded the college's highest teaching honor, the Charles A. Brickman Teaching Excellence Award, in 2013.Dr. Havill is also an active researcher, with a primary interest in the development and analysis of online algorithms. In addition, he has collaborated with colleagues in biology and geosciences to develop computational tools to support research and teaching in those fields. Dr. Havill earned his bachelor's degree from Bucknell University and his Ph.D. in computer science from The College of William and Mary.