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Introduction to Computers and Application Software provides a complete survey of the computer technologies necessary for achieving basic technology literacy and sets you on the path to career success. This helpful resource covers: - Computer Hardware - The Microsoft Windows 7 Operating System - The Internet - Microsoft Word 2010 - Microsoft Excel 2010 - Microsoft PowerPoint 2010
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Introduction to Computing is a comprehensive text designed for the CS0 (Intro to CS) course at the college level. It may also be used as a primary text for the Advanced Placement Computer Science course at the high school level.
A must-have introduction that bridges the gap between music and computing The rise in number of composer-programmers has given cause for an essential resource that addresses the gap between music and computing and looks at the many different software packages that deal with music technology. This up-to-date book fulfills that demand and deals with both the practical use of technology in music as well as the principles behind the discipline. Aimed at musicians exploring computers and technologists engaged with music, this unique guide merges the two worlds so that both musicians and computer scientists can benefit. Defines computer music and offers a solid introduction to representing music on a computer Examines computer music software, the musical instrument digital interface, virtual studios, file formats, and more Shares recording tips and tricks as well as exercises at the end of each section to enhance your learning experience Reviews sound analysis, processing, synthesis, networks, composition, and modeling Assuming little to no prior experience in computer programming, this engaging book is an ideal starting point for discovering the beauty that can be created when technology and music unite.
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.