Business Analytics with R and Python (AI for Risks) - Hardcover

Olson, David L.; Wu, Desheng Dash; Luo, Cuicui; Nabavi, Majid

Hardcover

ISBN 10: 9819747716 ISBN 13: 9789819747719

Publisher: Springer, 2024

Synopsis

This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.

About this title

About the Author

David L. Olson is the James & H.K. Stuart professor and chancellor’s professor at the University of Nebraska. He has published research in over 200 refereed journal articles, primarily on the topic of multiple objective decision-making, information technology, supply chain risk management, and data mining. He has authored over 50 books. He has served as an associate editor of Service Business, Decision Support Systems, Journal of Business Analytics, Decision Sciences and of various IEEE journals. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed professor at Texas A&M University from 1999 to 2001. He received the Herbert Simon Award for Outstanding Contribution in Information Technology and Decision Making in 2021. He is a fellow of the Decision Sciences Institute.

 

Desheng Dash Wu is a distinguished professor at the Economics and Management School, University of Chinese Academy of Sciences, Beijing, China. He has published over 150 ISI-indexed papers in refereed journals and 8 books with Springer. His current research interests include mathematical modeling of systems containing uncertain and risky situations, with a special interest in the finance-economics operations interface, maximizing operational and financial goals using the methodologies of game theory and large-scale optimization. He is an elected member of the Academia Europaea (the Academy of Europe), elected member of the European Academy of Sciences and Arts, and elected member of the International Eurasian Academy of Sciences.

Cuicui Luo is an associate professor at the International College of the University of Chinese Academy of Sciences, specializing in decision-making, machine learning, risk management, and financial mathematics. She earned her Ph.D. in financial mathematics from the University of Toronto in 2015. She also holds a Master's degree in Mathematical Finance (2009) and a Bachelor's degree in Actuarial Science (2006), both from the University of Toronto. Dr. Luo has published over 30 articles in esteemed academic journals. Her research focuses on the interplay between decision-making, machine learning, and portfolio optimization. She actively contributes to these fields through her scholarly publications and ongoing research.

Majid Nabavi earned his B.S. and M.S. degrees from the University of Tehran and MBA and Ph.D. in business administration with an emphasis in operations management from the University of Nebraska-Lincoln. His teaching areas include operations management, management science, database systems, and business analytics. Dr. Nabavi is a faculty member in the College of Business at the University of Nebraska-Lincoln. He has published in Quality Management Journal and Journal of Brand Strategy and presented research in regional and national conferences. He has been a co-principal investigator in research grant proposals and co-authored a book, Introduction to Business Analytics.

From the Back Cover

This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.

Edition details

• Publisher: Springer
• Publication date: 2024
• ISBN 10: 9819747716
• ISBN 13: 9789819747719
• Binding: Hardcover
• Language: English
• Number of pages: 206