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All such documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/or its respective suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection with the use or performance of information available from the services.  \nThe documents and related graphics contained herein could include technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s) described herein at any time. Partial screen shots may be viewed in full within the software version specified.  \nMicrosoft® and Windows® are registered trademarks of the Microsoft Corporation in the U.S.A. and other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation  \nPearson Education Limited KAO Two  \nKAO Park Hockham Way Harlow  \nEssex  \nCM17 9SR United Kingdom  \nand Associated Companies throughout the world  \nVisit us [on the World Wide Web at: www.pearsonglobaleditions.com](on the World Wide Web at: www.pearsonglobaleditions.com)[ ](on the World Wide Web at: www.pearsonglobaleditions.com)© Pearson Education Limited, 2021  \nThe rights of Ramesh Sharda, Dursun Delen, and Efraim Turban to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.  \nAuthorized adaptation from the United States edition, entitled Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, 11th Edition, ISBN 978-0-13-519201-6 by Ramesh Sharda, Dursun Delen, andEfraim Turban, published by Pearson Education © 2021.  \nAcknowledgments of third-party content appear on the appropriate page within the text.  \nAll rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without either the prior written permission of the publisher or a license permitting restricted copying in the United Kingdom issued by the Copyright Licensing Agency Ltd, Saffron House, 6–10 Kirby Street, London EC1N 8TS.  \nPEARSON and ALWAYS LEARNING are exclusive trademarks owned by Pearson Education, Inc. or its affiliates in the U.S. and/ or other countries.  \nAll trademarks used herein are the property of their respective owners. The use of any trademark in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does the use of such trademarks imply any affiliatio","cbCaihcvwYQt5E1T","https://ap.wps.com/l/cbCaihcvwYQt5E1T","pdf",6942497,84,"English","# Preface\n# About the Authors\n# Part I Introduction to Analytics and AI\n## Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support\n## Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications\n## Chapter 3 Nature of Data, Statistical Modeling, and Visualization\n# Part II Predictive Analytics/Machine Learning\n## Chapter 4 Data Mining Process, Methods, and Algorithms\n## Chapter 5 Machine-Learning Techniques for Predictive Analytics\n## Chapter 6 Deep Learning and Cognitive Computing\n## Chapter 7 Text Mining, Sentiment Analysis, and Social Analytics\n# Part III Prescriptive Analytics and Big Data\n## Chapter 8 Prescriptive Analytics: Optimization and Simulation\n## Chapter 9 Big Data, Cloud Computing, and Location Analytics: Concepts and Tools\n# Part IV Robotics, Social Networks, AI and IoT\n## Chapter 10 Robotics: Industrial and Consumer Applications\n## Chapter 11 Group Decision Making, Collaborative Systems, and AI Support\n## Chapter 12 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors\n## Chapter 13 The Internet of Things","[{\"question\":\"What topics does this book cover across the decision-support pipeline?\",\"answer\":\"It spans analytics foundations, predictive analytics and machine learning, prescriptive analytics and big data, and applications in robotics, social networks, knowledge systems, and IoT.\"},{\"question\":\"How does the book approach predictive analytics?\",\"answer\":\"It focuses on data mining processes and methods, machine-learning techniques for predictive analytics, deep learning and cognitive computing, and text mining with sentiment and social analytics.\"},{\"question\":\"What are the prescriptive and big data components included?\",\"answer\":\"Prescriptive analytics topics include optimization and simulation, along with big data, cloud computing, and location analytics concepts and tools.\"}]","Analytics, Data Science, & Artificial Intelligence - Systems for Decision Support - 11th Edition | PDF",212]