Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support

Ramesh Sharda / Dursun Delen / Efraim Turban  
Total pages
February 2019
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Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support

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For courses in decision support systems, computerized decision-making tools, and management support systems.

Market-leading guide to modern analytics, for better business decisions
Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organizations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganization reflecting a new focus — analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

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Hallmark features of this title

  • Recognized as the most comprehensive introduction to business analytics and the methods and software that develop these systems.
  • Provides current, real-world examples throughout to help students understand how organizations use analytics to make sound business decisions.
  • Accompanied by a companion website that provides additional materials and software tutorials that enhance learning.

New to this Edition

New and updated features of this title

  • NEW: The text has been reorganized to focus on analytics and its enabling technologies (including AI, machine-learning, robotics, chatbots and IoT). Outdated material has also been replaced with up-to-date coverage, streamlining the text.
  • NEW: Key concepts related to emerging technologies and their benefits and ramifications are covered in 5 new chapters focused on Artificial Intelligence; Deep Learning and Cognitive Computing; Robotics; Knowledge Systems; and the Internet of Things.
  • NEW: An 85% new Ch. 14 deals with implementation issues of intelligent systems, including analytics (from ethics and privacy to organizational and societal impacts).
  • NEW/UPDATED: Opening vignettes and application cases are new or updated throughout to provide recent real-world stories, as well as recent examples of relevant applications of a specific technique or model.
  • EXPANDED: New exercises, Internet assignments and discussion questions have been added or refreshed throughout, and website links have been updated.
  • UPDATED: A dedicated website ( continues to provide links to learning materials and software tutorials, and houses material from previous editions for reference.

Table of Contents


  1. An Overview of Business Analytics, Decision Support Systems, Business Intelligence, Data Science, and Artificial Intelligence
  2. Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
  3. Nature of Data, Statistical Modeling, and Visualization


  1. Data Mining Process, Methods, and Applications
  2. Machine learning Techniques for Predictive Analytics
  3. Deep Learning and Cognitive Computing
  4. Text Mining, Sentiment Analysis, and Social Analytics


  1. Prescriptive Analytics with Optimization and Simulation
  2. Big Data, Location Analytics, and Cloud Computing


  1. Robotics: Industrial and Consumer Applications
  2. Group Decision Making, Collaborative Systems, and AI Support
  3. Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors
  4. The Internet of Things As a Platform for Intelligent Applications


  1. Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts


About our authors

Ramesh Sharda (MBA, PhD, University of Wisconsin—Madison) is Vice Dean for Research and Graduate Programs, Watson/ConocoPhillips Chair, and Regents Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University. His research has been published in major journals in management science and information systems, including?Management Science,?Operations Research,?Information Systems Research,?Decision Support Systems,?Decision Sciences Journal,?EJIS,?JMIS,?Interfaces,?INFORMS Journal on Computing, and?ACM Database. Dr. Sharda is a member of the editorial boards of journals such as?Decision Support Systems,?Decision Sciences, and?ACM Database. He has worked on many sponsored research projects with government and industry, and has been a consultant to many organizations. He also serves as the faculty director of Teradata University Network. Dr. Sharda received the 2013 INFORMS Computing Society HG Lifetime Service Award, and was inducted into the Oklahoma Higher Education Hall of Fame in 2016. He is a fellow of INFORMS.

Dursun Delen (PhD, Oklahoma State University) is the Spears and Patterson 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. 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 time he led a number of decision support and other information systems—related research projects funded by federal agencies such as DoD, NASA, NIST, and DOE. Dr. Delen’s research has appeared in major journals, including?Decision Sciences,?Decision Support Systems,?Communications of the ACM,?Computers and Operations Research,?Computers in Industry,?Journal of Production Operations Management,?Journal of American Medical Informatics Association,?Artificial Intelligence in Medicine, and?Expert Systems with Applications. He has published eight books and textbooks and more than 100 peer-reviewed journal articles, and is often invited to deliver keynote addresses at national and international conferences on topics related to business analytics, Big Data, data/text mining, business intelligence, decision support systems, and knowledge management. Dr. Delen served as the general co-chair for the 4th International Conference on Network Computing and Advanced Information Management in Seoul, South Korea, and regularly serves as chair on tracks and mini-tracks at various business analytics and information systems conferences. He is the co-editor-in-chief of the?Journal of Business Analytics, the area editor for?Big Data and Business Analytics on the Journal of Business Research, and chief editor, senior editor, associate editor, and editorial board member on more than a dozen other journals. His consultancy, research, and teaching interests are in business analytics, data and text mining, health analytics, decision support systems, knowledge management, systems analysis and design, and enterprise modeling.

Efraim Turban (MBA, PhD, University of California, Berkeley) is a visiting scholar at the Pacific Institute for Information System Management, University of Hawaii. Prior to this, he was on the staff of several universities, including City University of Hong Kong; Lehigh University; Florida International University; California State University, Long Beach; Eastern Illinois University; and the University of Southern California. Dr. Turban is the author of more than 110 refereed papers published in leading journals such as?Management Science,?MIS Quarterly, and?Decision Support Systems. He is also the author of 22 books, including?Electronic Commerce: A Managerial Perspective?and?Information Technology for Management. Dr. Turban is a consultant to major corporations worldwide. His current areas of interest are web-based decision support systems, digital commerce, and applied artificial intelligence.