Overview
- Focuses on how to use predictive analytic techniques to analyze historical data
- An applied approach and focus on solving business problems using predictive analytics
- Uses examples in SAS Enterprise Miner, one of world’s leading analytics software tools
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
- Predicative analytics
- SAS Enterprise Miner
- Neural Networks
- Machine Learning
- Supervised learning unsupervised learning
- Data mining
- Business analytics
- Decision trees
- Complex analytics model
- Using analytics models
- Building analytics models
- Real-life business analytics examples
- Applied data analytics textbook
About this book
The new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life examples of how business analytics have been used in various aspects of organizations to solve issues or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.
Authors and Affiliations
About the authors
Richard V. McCarthy (DBA, Nova Southeastern University, MBA, Western New England College) is a professor of Computer Information Systems at the School of Business, Quinnipiac University. Prior to this, Dr. McCarthy was an associate professor of management information systems at Central Connecticut State University. He has twenty years of experience within the insurance industry and has held a Charter Property Casualty Underwriter (CPCU) designation since 1991. He has authored numerous research articles and contributed to several textbooks. He has served as the associate dean of the School of Business, the MBA director, and the director of the Master of Science in Business Analytics program. In 2019, he was awarded the Computer Educator of the Year from the International Association for Computer Information Systems.
Wendy Ceccucci (PhD and MBA, Virginia Polytechnic University) is a Professor and Chair of Computer Information Systems at QuinnipiacUniversity. Her teaching areas include business analytics and programming. She is the past president of the Education Special Interest Group (EDSIG) of the Association for Information Technology Professionals (AITP) and past Associate Editor of the Information Systems Education Journal (ISEDJ). Her research interests include Information Systems Pedagogy.
Mary McCarthy (DBA, Nova Southeastern University, MBA, University of Connecticut) is a professor and chair of Accounting, Central Connecticut State University. She has twenty years of financial reporting experience and has served as the controller for a Fortune 50 industry organization. She holds a CPA and CFA designation. She has authored numerous research articles.
Bibliographic Information
Book Title: Applying Predictive Analytics
Book Subtitle: Finding Value in Data
Authors: Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci
DOI: https://doi.org/10.1007/978-3-030-83070-0
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-83069-4Published: 23 December 2021
Softcover ISBN: 978-3-030-83072-4Published: 24 December 2022
eBook ISBN: 978-3-030-83070-0Published: 01 January 2022
Edition Number: 2
Number of Pages: XV, 274
Number of Illustrations: 34 b/w illustrations, 253 illustrations in colour
Topics: Communications Engineering, Networks, Computational Intelligence, Data Mining and Knowledge Discovery, Big Data/Analytics