Efnisyfirlit

• 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

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