Description

Book Synopsis

About our authors

Ramesh Sharda (MBA, PhD, University of WisconsinMadison) 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

Table of Contents
PART I: INTRODUCTION TO ANALYTICS AND AI

  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
PART II: PREDICTIVE ANALYTICS AND MACHINE LEARNING
  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
PART III: PRESCRIPTIVE ANALYTICS AND BIG DATA
  1. Prescriptive Analytics with Optimization and Simulation
  2. Big Data, Location Analytics, and Cloud Computing
PART IV: ROBOTICS, SOCIAL NETWORKS, AI, AND IoT
  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
PART V: CAVEATS OF ANALYTICS AND AI
  1. Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts

Analytics Data Science Artificial Intelligence

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    £273.31

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    Order before 4pm today for delivery by Mon 29 Jun 2026.

    A Hardback by Ramesh Sharda, Dursun Delen, Efraim Turban

    15 in stock


      View other formats and editions of Analytics Data Science Artificial Intelligence by Ramesh Sharda

      Publisher: Pearson Education (US)
      Publication Date: 18/02/2019
      ISBN13: 9780135192016, 978-0135192016
      ISBN10: 0135192013

      Description

      Book Synopsis

      About our authors

      Ramesh Sharda (MBA, PhD, University of WisconsinMadison) 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

      Table of Contents
      PART I: INTRODUCTION TO ANALYTICS AND AI

      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
      PART II: PREDICTIVE ANALYTICS AND MACHINE LEARNING
      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
      PART III: PRESCRIPTIVE ANALYTICS AND BIG DATA
      1. Prescriptive Analytics with Optimization and Simulation
      2. Big Data, Location Analytics, and Cloud Computing
      PART IV: ROBOTICS, SOCIAL NETWORKS, AI, AND IoT
      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
      PART V: CAVEATS OF ANALYTICS AND AI
      1. Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts

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