Applying Predictive Analytics - Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi

Applying Predictive Analytics

Finding Value in Data
Buch | Hardcover
X, 205 Seiten
2019 | 1st ed. 2019
Springer International Publishing (Verlag)
978-3-030-14037-3 (ISBN)
69,54 inkl. MwSt
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This textbook presents a practical approach to predictive analytics for classroom learning. It focuses 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 example of how business analytics have been used in various aspects of organizations to solve issue 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.

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. He also serves as the director for the Master of Science in Business Analytics program. 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 as well as the MBA director. He served as a member of the board of the International Association for Computer Information Systems and is currently a member of the board of the EABOK project. Wendy Ceccucci (PhD and MBA, Virginia Polytechnic University) is a Professor and Chair of Computer Information Systems at Quinnipiac University. 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 lies in Information Systems Pedagogy. Leila A. Halawi (B.S, and MBA, Lebanese American University, DBA, Nova Southeastern University) is an associate professor of Management Information Systems in the College of Business at Embry-Riddle Aeronautical University. Prior to this, Dr. Halawi was an assistant professor of business administration in the School of Business at the Bethune-Cookman University. She was also the operations manager for Avis in Brandon, Florida for 5 years. She has authored several journal articles and contributed to the textbook Decision Support and Business Intelligence Systems. She is frequently invited to present her research at national and international conferences. Mary McCarthy (DBA, Nova Southeastern University, MBA, University of Connecticut) a professor of Accounting, Central Connecticut State. She has twenty years of financial reporting experience and has served as the controller for a Fortune 50 industry organization. She holds a CPA, CFA and CMA designation. She has authored numerous research articles.

lt;p>Introduction to Predictive Analytics.- Know Your Data - Data Preparation.- What do Descriptive Statistics Tell Us.- The First of the Big Three - Regression.- The Second of the Big Three - Decision Trees.- The Third of the Big Three - Neural Networks.- Model Comparisons and Scoring.- Appendix A.- Data Dictionary for the Automobile Insurance Claim Fraud Data Example.- Conclusion.

Erscheinungsdatum
Zusatzinfo X, 205 p.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 491 g
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik Elektrotechnik / Energietechnik
Technik Nachrichtentechnik
Schlagworte Applied data analytics textbook • Building analytics models • Business Analytics • Complex analytics model • Data Mining • decision trees • machine learning • Neural networks • Predicative analytics • Real-life business analytics examples • SAS Enterprise Miner • Supervised learning unsupervised learning • Using analytics models
ISBN-10 3-030-14037-7 / 3030140377
ISBN-13 978-3-030-14037-3 / 9783030140373
Zustand Neuware
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