Business Intelligence, Analytics, Data Science, and AI, Global Edition

Höfundar: Ramesh Sharda; Dursun Delen; Efraim Turban (Útgáfa: 5)
Business Intelligence, Analytics, Data Science, and AI, Global Edition

Kaup valmöguleikar

Business Intelligence, Analytics, Data Science, and AI er leiðarvísir þinn um áhrif gervigreindar, gagnavísinda og greiningar á rekstur, sniðinn að því að búa þig undir stjórnunarstarf. Dæmasögur og raundæmi bókarinnar fjalla um fyrirtæki og félagasamtök samtímans og sýna hvað viðskiptagreind getur gert, hvað hún kostar og hvernig hún er réttlætt innan ólíkra rekstrareininga. Með umfjöllun um fjölmörg not gagnavísinda og gervigreindar kynnistu verkfærunum og lærir af reynslu ólíkra skipulagsheilda af þeim.

Bókin býður upp á ríkulegar hagnýtar æfingar sem leysa má með ýmsum hugbúnaði og hjálpa þér að nýta greiningu í framtíðarstarfi sem stjórnandi. Fimmta útgáfan sameinar uppfært efni tveggja fyrri bóka í eina, styrkt með fjórum nýjum köflum sem búa þig undir þá greiningar- og gervigreindartækni sem nú er í notkun, þar á meðal ChatGPT.

Nánar um bókina

Útgefandi
Pearson International Content
ISBN
9781292727530
Print ISBN
9781292459295
Format
ePub
Útgáfa
5
Höfundar
Ramesh Sharda; Dursun Delen; Efraim Turban
Tungumál
English
Útgefið
2024-08-23
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • 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