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End User License Agreement
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End User License Agreement
by James J. Cochran
INFORMS Analytics Body of Knowledge
Cover
Title Page
Copyright
Preface
List of Contributors
Chapter 1: Introduction to Analytics
1.1 Introduction
1.2 Conceptual Framework
1.3 Categories of Analytics
1.4 Analytics Within Organizations
1.5 Ethical Implications
1.6 The Changing World of Analytics
1.7 Conclusion
References
Chapter 2: Getting Started with Analytics
2.1 Introduction
2.2 Five Manageable Tasks
2.3 Real Examples
References
Further Reading: Papers
Further Reading: Books
Chapter 3: The Analytics Team
3.1 Introduction
3.2 Skills Necessary for Analytics
3.3 Managing Analytical Talent
3.4 Organizing Analytics
3.5 To Where Should Analytical Functions Report?
References
Chapter 4: The Data
4.1 Introduction
4.2 Data Collection
4.3 Data Preparation
4.4 Data Modeling
4.5 Data Management
Chapter 5: Solution Methodologies
5.1 Introduction
5.2 Macro-Solution Methodologies for the Analytics Practitioner
5.3 Micro-Solution Methodologies for the Analytics Practitioner
5.4 General Methodology-Related Considerations
5.5 Summary and Conclusions
5.6 Acknowledgments
References
Chapter 6: Modeling
6.1 Introduction
6.2 When Are Models Appropriate
6.3 Types of Models
6.4 Models Can Also Be Characterized by Whether They Are Deterministic or Stochastic (Random)
6.5 Counting
6.6 Probability
6.7 Probability Perspectives and Subject Matter Experts
6.8 Subject Matter Experts
6.9 Statistics
6.10 Inferential Statistics
6.11 A Stochastic Process
6.12 Digital Simulation
6.13 Mathematical Optimization
6.14 Measurement Units
6.15 Critical Path Method
6.16 Portfolio Optimization Case Study Solved By a Variety of Methods
6.17 Game Theory
6.18 Decision Theory
6.19 Susceptible, Exposed, Infected, Recovered (SEIR) Epidemiology
6.20 Search Theory
6.21 Lanchester Models of Warfare
6.22 Hughes' Salvo Model of Combat
6.23 Single-Use Models
6.24 The Principle of Optimality and Dynamic Programming
6.25 Stack-Based Enumeration
6.26 Traveling Salesman Problem: Another Case Study in Alternate Solution Methods
6.27 Model Documentation, Management, and Performance
6.28 Rules for Data Use
6.29 Data Interpolation and Extrapolation
6.30 Model Verification and Validation
6.31 Communicate with Stakeholders
6.32 Software
6.33 Where to Go from Here
6.34 Acknowledgments
References
Chapter 7: Machine Learning
7.1 Introduction
7.2 Supervised, Unsupervised, and Reinforcement Learning
7.3 Model Development, Selection, and Deployment for Supervised Learning
7.4 Model Fitting, Model Error, and the Bias-Variance Trade-Off
7.5 Predictive Performance Evaluation
7.6 An Overview of Supervised Learning Algorithms
7.7 Unsupervised Learning Algorithms
7.8 Conclusion
7.9 Acknowledgments
References
Chapter 8: Deployment and Life Cycle Management
8.1 Introduction
8.2 The Analytics Methodology: Understanding the Critical Steps in Deployment and Life Cycle Management
8.3 Overarching Issues of Life Cycle Management
Chapter 9: The Blossoming Analytics Talent Pool: An Overview of the Analytics Ecosystem
9.1 Introduction
9.2 Analytics Industry Ecosystem
9.3 Conclusions
References
Appendix: Writing and Teaching Analytics with Cases
A.1 What Is a Teaching Case?
A.2 My Motivation for Using Teaching Cases
A.3 Writing a Teaching Case
A.4 Using a Teaching Case
A.5 An Example of a Simple Case
A.6 Final Thoughts
Bibliography
Index
End User License Agreement
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