Authors:
- Unites theory, algorithm design, and practical data analysis for simplicity and clarity of content
- Contains more than twenty detailed and carefully crafted Python tutorials
- Each chapter includes exercises of varying levels of difficulty
- Uses publicly available data sets throughout the book
- Includes supplementary material: sn.pub/extras
- Request lecturer material: sn.pub/lecturer-material
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Table of contents (12 chapters)
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Front Matter
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Data Reduction
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Front Matter
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Extracting Information from Data
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Front Matter
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Predictive Analytics
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Front Matter
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Back Matter
About this book
This book has three parts:
(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, themathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.
(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.
(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.
This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
Reviews
“This 430-page book contains an excellent collection of information on the subject of practical algorithms used in data science. The discussion of each algorithm starts with some basic concepts, followed by a tutorial with real datasets and detailed code examples in Python or R. Each chapter has a set of exercise problems so readers can practice the concepts learned in the chapter. … a good reference for practitioners, or a good textbook for graduate or upper-class undergraduate students.” (Xiannong Meng, Computing Reviews, September, 2017)
“This textbook on practical data analytics unites fundamental principles, algorithms, and data. … this book is devoted to upper-division undergraduate and graduate students in mathematics, statistics, and computer science. It is intended for a one- or two-semester course in data analytics and reflects the authors’ research experience in data science concepts and the teaching skills in various areas. … The text is eminently suitable for self-study and an exceptional resource for practitioners.” (Krzysztof J. Szajowski, zbMATH 1367.62005, 2017)
Authors and Affiliations
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University of Montana, Missoula, USA
Brian Steele
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School of Business Administration, University of Montana, Missoula, USA
John Chandler
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SoftMath Consultants, LLC, Missoula, USA
Swarna Reddy
About the authors
John Chandler has worked at the forefront of marketing and data analysis since 1999. He has worked with Fortune 100 advertisers and scores of agencies, measuring the effectiveness of advertising and improving performance. Dr. Chandler joined the faculty at the University of Montana School of Business Administration as a Clinical Professor of Marketing in 2015 and teaches classes in advanced marketing analytics and data science. He is one of the founders and Chief Data Scientist for Ars Quanta, a Seattle-based data science consultancy.
Dr. Swarna Reddy is the founder, CEO, and a Senior Data Scientist for SoftMath Consultants, LLC and serves as a faculty affiliate with the Department of Mathematical Sciences at the University of Montana. Her area of expertise is computational mathematics and operations research. She is a published researcher and has developed computational solutions across a wide variety of areas spanning bioinformatics, cybersecurity, and business analytics.
Bibliographic Information
Book Title: Algorithms for Data Science
Authors: Brian Steele, John Chandler, Swarna Reddy
DOI: https://doi.org/10.1007/978-3-319-45797-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-45795-6Published: 27 December 2016
Softcover ISBN: 978-3-319-83373-6Published: 07 July 2018
eBook ISBN: 978-3-319-45797-0Published: 25 December 2016
Edition Number: 1
Number of Pages: XXIII, 430
Number of Illustrations: 18 b/w illustrations, 30 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Statistics and Computing/Statistics Programs, Mathematics of Computing, Health Informatics
Industry Sectors: Aerospace, Biotechnology, Consumer Packaged Goods, Electronics, Energy, Utilities & Environment, Engineering, Finance, Business & Banking, IT & Software, Law, Materials & Steel, Oil, Gas & Geosciences, Pharma, Telecommunications