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• Cover
• Cover
• Front Matter
• Title Page
• Copyright Page
• Preface
• Acknowledgments
• About the Authors
• About the Book
• Part I: Introduction
• Part I: Introduction
• Chapter 1: An Overview of Business Intelligence, Analytics, Data Science, and AI
• Introduction: An Overview of Business Intelligence, Analytics, Data Science, and AI
• 1.1: Opening Vignette: Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics
• 1.2: Changing Business Environments and Evolving Needs for Decision Support and Analytics
• 1.3: Decision-Making Processes and Computerized Decision Support Framework
• 1.4: Evolution of Computerized Decision Support to Analytics/Data Science
• 1.5: A Framework for Business Intelligence
• 1.6: Analytics Overview
• 1.7: Analytics Examples in Selected Domains
• 1.8: Plan of the Book
• 1.9: Resources and Links
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Chapter 2: Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
• Introduction: Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications
• 2.1: Opening Vignette: Grant Thornton Employs Aisera Chatbot to Reduce IT Help Desk Burden
• 2.2: Introduction to Artificial Intelligence
• 2.3: Human and Computer Intelligence
• 2.4: Major AI Technologies and Some Derivatives
• 2.5: AI Support for Decision Making
• 2.6: AI Applications in Various Business Functions
• 2.7: Introduction to Robotics
• 2.8: Illustrative Applications of Robotics
• 2.9: Conversational AI—Chatbots
• 2.10: Enterprise Chatbots
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Part II: Descriptive Analytics
• Part II: Descriptive Analytics
• Chapter 3: Descriptive Analytics I: Nature of Data, Big Data, and Statistical Modeling
• Introduction: Descriptive Analytics I: Nature of Data, Big Data, and Statistical Modeling
• 3.1: Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing
• 3.2: The Nature of Data in Analytics
• 3.3: A Simple Taxonomy of Data
• 3.4: The Art and Science of Data Preprocessing
• 3.5: Definition of Big Data
• 3.6: Fundamentals of Big Data Analytics
• 3.7: Big Data Technologies
• 3.8: Big Data and Stream Analytics
• 3.9: Statistical Modeling for Business Analytics
• 3.10: Regression Modeling for Inferential Statistics
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Chapter 4: Descriptive Analytics II: Business Intelligence Data Warehousing, and Visualization
• Introduction: Descriptive Analytics II: Business Intelligence Data Warehousing, and Visualization
• 4.1: Opening Vignette: Targeting Tax Fraud with Data Warehousing and Business Analytics
• 4.2: Business Intelligence and Data Warehousing
• 4.3: Data Warehousing Process
• 4.4: Data Warehousing Architectures
• 4.5: Data Management and Warehouse Development
• 4.6: Data Warehouse Administration, Security Issues, and Future Trends
• 4.7: Business Reporting
• 4.8: Data Visualization
• 4.9: Different Types of Charts and Graphs
• 4.10: The Emergence of Visual Analytics
• 4.11: Information Dashboards
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Part III: Predictive Analytics
• Part III: Predictive Analytics
• Chapter 5: Predictive Analytics I: Data Mining Process, Methods, and Algorithms
• Introduction: Predictive Analytics I: Data Mining Process, Methods, and Algorithms
• 5.1: Opening Vignette: Police Departments Are Using Predictive Analytics to Foresee and Fight Crime
• 5.2: Data Mining Concepts and Applications
• 5.3: Data Mining Applications
• 5.4: Data Mining Process
• 5.5: Data Mining Methods
• 5.6: Data Mining Software Tools
• 5.7: Data Mining Privacy Issues, Myths, and Blunders
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Chapter 6: Predictive Analytics II: Text, Web, and Social Media Analytics
• Introduction: Predictive Analytics II: Text, Web, and Social Media Analytics
• 6.1: Opening Vignette: Machine versus Human on Jeopardy!: The Story of Watson
• 6.2: Text Analytics and Text Mining Overview
• 6.3: Natural Language Processing (NLP)
• 6.4: Text Mining Applications
• 6.5: Text Mining Process
• 6.6: Sentiment Analysis and Topic Modeling
• 6.7: Web Mining Overview
• 6.8: Search Engines
• 6.9: Web Usage Mining (Web Analytics)
• 6.10: Social Analytics
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Chapter 7: Deep Learning and Cognitive Computing
• Introduction: Deep Learning and Cognitive Computing
• 7.1: Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence
• 7.2: Introduction to Deep Learning
• 7.3: Basics of “Shallow” Neural Networks
• 7.4: Process of Developing Neural Network–Based Systems
• 7.5: Illuminating the Black Box of ANN
• 7.6: Deep Neural Networks
• 7.7: Convolutional Neural Networks
• 7.8: Recurrent Networks and Long Short-Term Memory Networks
• CHATGPT
• 7.9: Computer Frameworks for Implementation of Deep Learning
• 7.10: Cognitive Computing
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Part IV: Prescriptive Analytics
• Part IV: Prescriptive Analytics
• Chapter 8: Prescriptive Analytics: Optimization and Simulation
• Introduction: Prescriptive Analytics: Optimization and Simulation
• 8.1: Opening Vignette: Balancing Delivery Routes, Production Schedules, and Inventory
• 8.2: Model-Based Decision-Making
• 8.3: Structure of Mathematical Models for Decision Support
• 8.4: Certainty, Uncertainty, and Risk
• 8.5: Decision Modeling with Spreadsheets
• 8.6: Mathematical Programming Optimization
• 8.7: Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
• 8.8: Decision Analysis with Decision Tables and Decision Trees
• 8.9: Introduction to Simulation
• 8.10: Genetic Algorithms and Developing GA Applications
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Part V: Software and Trends
• Part V: Software and Trends
• Chapter 9: Landscape of Business Analytics Tools
• Introduction: Landscape of Business Analytics Tools
• 9.1: Opening Vignette: How Seagate Is Using Knime to Tackle the Digital Transformation
• 9.2: Importance of Analytics Tools
• 9.3: Free and Open-Source Analytics’ Programming Languages
• 9.4: Free and Open-Source Analytics’ Visual Tools
• 9.5: Commercial Analytics Tools
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Chapter 10: AI-Based Trends in Analytics and Data Science
• Introduction: AI-Based Trends in Analytics and Data Science
• 10.1: Application Vignette: Discover Foods Employs IoT and Machine Learning to Ensure Food Quality
• 10.2: Cloud-Based Analytics
• 10.3: Location-Based Analytics
• 10.4: Image Analytics/Alternative Data
• 10.5: IoT Essentials
• Major Benefits and Drivers of IoT
• 10.6: IoT Applications
• 10.7: 5G Technologies and Impact on AI
• 10.8: Other Emerging AI Topics: Robotic Process Automation (RPA)
• 10.9: Bioinformatics and Health Network Science
• Network Analytics
• 10.10: Other Recent Developments
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Chapter 11: Ethical, Privacy, and Managerial Considerations in Analytics
• Introduction: Ethical, Privacy, and Managerial Considerations in Analytics
• 11.1: Opening Vignette: Lessons Learned from Analytics Journey in an Organization
• 11.2: Implementing Intelligent Systems: An Overview
• 11.3: Successful Deployment of Intelligent Systems
• 11.4: Implementing IoT and Managerial Considerations
• 11.5: Legal, Privacy, and Ethical Issues
• 11.6: Ethical/Responsible/Trustworthy AI
• 11.7: Impacts of Intelligent Systems on Organizations
• 11.8: Impacts on Jobs and Work
• 11.9: Potential Dangers of AI
• 11.10: Citizen Science and Citizen Data Scientists
• Chapter Highlights and Key Terms
• Questions and Exercises
• References
• Glossary