Author: Steven L. Mandell
Publisher:
ISBN: 9780314006981
Category : BASIC (Computer program language)
Languages : en
Pages : 612

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Book Description

Author:
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 780

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Book Description

Author:
Publisher:
ISBN: 9780835242721
Category : Periodicals
Languages : en
Pages : 1528

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Book Description

Author: Maria Litvin
Publisher:
ISBN: 9780965485357
Category : Computer software
Languages : en
Pages : 0

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Book Description
Review and test preparation book for Advanved Placement examinations in computer science

Author: Steven S Skiena
Publisher: Springer Science & Business Media
ISBN: 1848000707
Category : Computers
Languages : en
Pages : 742

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Book Description
This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW "war stories" relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java

Author: Norman Herr
Publisher: John Wiley & Sons
ISBN: 0787972983
Category : Education
Languages : en
Pages : 614

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Book Description
The Sourcebook for Teaching Science is a unique, comprehensive resource designed to give middle and high school science teachers a wealth of information that will enhance any science curriculum. Filled with innovative tools, dynamic activities, and practical lesson plans that are grounded in theory, research, and national standards, the book offers both new and experienced science teachers powerful strategies and original ideas that will enhance the teaching of physics, chemistry, biology, and the earth and space sciences.

Author: Julianne Zedalis
Publisher:
ISBN: 9781947172401
Category : Biology
Languages : en
Pages : 1923

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Book Description
Biology for AP® courses covers the scope and sequence requirements of a typical two-semester Advanced Placement® biology course. The text provides comprehensive coverage of foundational research and core biology concepts through an evolutionary lens. Biology for AP® Courses was designed to meet and exceed the requirements of the College Board’s AP® Biology framework while allowing significant flexibility for instructors. Each section of the book includes an introduction based on the AP® curriculum and includes rich features that engage students in scientific practice and AP® test preparation; it also highlights careers and research opportunities in biological sciences.

Author: Steven S. Skiena
Publisher: Springer
ISBN: 3319554441
Category : Computers
Languages : en
Pages : 456

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Book Description
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

Author: Noam Nisan
Publisher:
ISBN: 0262640686
Category : Computers
Languages : en
Pages : 343

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Book Description
This title gives students an integrated and rigorous picture of applied computer science, as it comes to play in the construction of a simple yet powerful computer system.

Author: National Research Council
Publisher: National Academies Press
ISBN: 0309037409
Category : Education
Languages : en
Pages : 231

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Book Description
This book presents a carefully developed monitoring system to track the progress of mathematics and science education, particularly the effects of ongoing efforts to improve students' scientific knowledge and mathematics competency. It describes an improved series of indicators to assess student learning, curriculum quality, teaching effectiveness, student behavior, and financial and leadership support for mathematics and science education. Of special interest is a critical review of current testing methods and their use in probing higher-order skills and evaluating educational quality.