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OverviewFull Product DetailsAuthor: Professor Steven S. SkienaPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: 1st ed. 2017 Weight: 1.168kg ISBN: 9783319554433ISBN 10: 3319554433 Pages: 445 Publication Date: 29 August 2017 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand ![]() We will order this item for you from a manufactured on demand supplier. Table of ContentsWhat is Data Science?.- Mathematical Preliminaries.- Data Munging.- Scores and Rankings.- Statistical Analysis.- Visualizing Data.- Mathematical Models.- Linear Algebra.- Linear and Logistic Regression.- Distance and Network Methods.- Machine Learning.- Big Data: Achieving Scale.ReviewsThe 14 chapters of this book have been carefully devised to provide a comprehensive introduction to data science as an academic discipline. The special feature of this text is that it does so by focusing on the skills and principles needed to design systems for collecting, analyzing, and interpreting data. (P. Navrat, Computing Reviews, February, 23, 2018) “The book is more than a typical manual. In fact, the author himself designates it as a textbook for an introductory course on data science. The chapters are richly equipped with exercises. The topics are always explained starting with a proper motivation and continuing with practical examples. This is perhaps the most outstanding feature of the book. It can serve as a regular textbook for an academic course. In fact, I should like to recommend it exactly for this purpose. On the other hand, it provides a wealth of material for people from industry, such as software engineers, and can serve as a manual for them to accomplish data science tasks. It should be noted that the book is not just a text, but a much more complex product, including a full set of lecture slides available online as well as a solutions wiki.” (P. Navrat, Computing Reviews, February, 23, 2018) The book is more than a typical manual. In fact, the author himself designates it as a textbook for an introductory course on data science. The chapters are richly equipped with exercises. The topics are always explained starting with a proper motivation and continuing with practical examples. This is perhaps the most outstanding feature of the book. It can serve as a regular textbook for an academic course. In fact, I should like to recommend it exactly for this purpose. On the other hand, it provides a wealth of material for people from industry, such as software engineers, and can serve as a manual for them to accomplish data science tasks. It should be noted that the book is not just a text, but a much more complex product, including a full set of lecture slides available online as well as a solutions wiki. (P. Navrat, Computing Reviews, February, 23, 2018) Author InformationDr. Steven S. Skiena is Distinguished Teaching Professor of Computer Science at Stony Brook University, with research interests in data science, natural language processing, and algorithms. He was awarded the IEEE Computer Science and Engineering Undergraduate Teaching Award “for outstanding contributions to undergraduate education ...and for influential textbooks and software.” Dr. Skiena is the author of six books, including the popular Springer titles The Algorithm Design Manual and Programming Challenges: The Programming Contest Training Manual. Tab Content 6Author Website:Countries AvailableAll regions |