Business Intelligence, Analytics, and Data Science

Ramesh Sharda (M.B.A., Ph.D., University of Wisconsin-Madison) is the Vice Dean for Research and Graduate Programs, Watson/ConocoPhillips Chair and a Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. He cofounded and directed OSU's PhD in Business for the Executives Program. About 200 papers describing his research have been published in major journals, including Operations Research, Management Science, Information Systems Research, Decision Support Systems, and Journal of MIS. He cofounded the AIS SIG on Decision Support Systems and Knowledge Management (SIGDSS). Dr. Sharda serves on several editorial boards, including those of Decision Sciences Journal, Decision Support Systems, and ACM Data Base. He has authored and edited several textbooks and research books and serves as the co-editor of several book series (Integrated Series in Information Systems, Operations Research/Computer Science Interfaces, and Annals of Information Systems) with Springer. He is also currently serving as the executive director of the Teradata University Network. His current research interests are in decision support sys- tems, business analytics, and technologies for managing information overload.Dursun Delen (Ph.D., Oklahoma State University) is the Spears Endowed Chair in Business Administration, Patterson Foundation Endowed Chair in Business Analytics, Director of Research for the Center for Health Systems Innovation, and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU). Prior to his academic career, he worked for a privately owned research and consultancy company, Knowledge Based Systems Inc., in College Station, Texas, as a research scientist for five years, during which he led a number of decision support and other information systems-related research projects funded by sev- eral federal agencies including Department of Defense (DoD), National Aeronautics and Space Administration (NASA), National Institute for Standards and Technology (NIST), Ballistic Missile Defense Organization (BMDO), and Department of Energy (DOE). Dr. Delen has published more than 100 peer reviewed articles, some of which have appeared in major journals like Decision Sciences, Decision Support Systems, Communications of the ACM, Computers and Operations Research, Computers in Industry, Journal of Production Operations Management, Artificial Intelligence in Medicine, International Journal of Medical Informatics, Expert Systems with Applications, and IEEE Wireless Communications. He recently authored/co-authored seven textbooks in the broad areas of business analyt- ics, data mining, text mining, business intelligence and decision support systems. He is often invited to national and international conferences for keynote addresses on topics related to data/text mining, business analytics, decision support systems, business intel- ligence and knowledge management. He served as the general cochair for the Fourth International Conference on Network Computing and Advanced Information Management (September 2-4, 2008, in Soul, South Korea) and regularly chairs tracks and mini-tracks at various information systems and analytics conferences. He is currently serving as editor- in-chief, senior editor, associate editor or editorial board member for more than a dozen academic journals. His research and teaching interests are in data and text mining, busi- ness analytics, decision support systems, knowledge management, business intelligence, and enterprise modeling.Efraim Turban (M.B.A., Ph.D., University of California, Berkeley) is a visiting scholar at the Pacific Institute for Information System Management, University of Hawaii. Prior to this, he

Chapter 1   An Overview of Business Intelligence, Analytics, and Data Science    3

Chapter 2   Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization    53

Chapter 3   Descriptive Analytics II: Business Intelligence and Data Warehousing    127

Chapter 4   Predictive Analytics I: Data Mining Process, Methods, and Algorithms    189

Chapter 5   Predictive Analytics II: Text, Web, and Social Media Analytics  247

Chapter 6   Prescriptive Analytics: Optimization and Simulation   319

Chapter 7   Big Data Concepts and Tools  369

Chapter 8   Future Trends, Privacy and Managerial Considerations in Analytics 417