Cover image for INFORMS Analytics Body of Knowledge

INFORMS Analytics Body of Knowledge

Author James J. Cochran
Release Date: 2018/10/01
ISBN: 9781119483212
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Book Description

Standardizes the definition and framework of analytics

ABOK stands for Analytics Body of Knowledge. Based on the authors’ definition of analytics—which is “a process by which a team of people helps an organization make better decisions (the objective) through the analysis of data (the activity)”— this book from Institute for Operations Research and the Management Sciences (INFORMS) represents the perspectives of some of the most respected experts on analytics. The INFORMS ABOK documents the core concepts and skills with which an analytics professional should be familiar; establishes a dynamic resource that will be used by practitioners to increase their understanding of analytics; and, presents instructors with a framework for developing academic courses and programs in analytics.

The INFORMS ABOK offers in-depth insight from peer-reviewed chapters that provide readers with a better understanding of the dynamic field of analytics. Chapters cover: Introduction to Analytics; Getting Started with Analytics; The Analytics Team; The Data; Solution Methodology; Model Building; Machine Learning; Deployment and Life Cycle Management; and The Blossoming Analytics Talent Pool: An Overview of the Analytics Ecosystem. 

Across industries and academia, readers with various backgrounds in analytics – from novices who are interested in learning more about the basics of analytics to experienced professionals who want a different perspective on some aspect of analytics – will benefit from reading about and implementing the concepts and methods covered by the INFORMS ABOK. 

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. List of Contributors
  6. Chapter 1: Introduction to Analytics
    1. 1.1 Introduction
    2. 1.2 Conceptual Framework
    3. 1.3 Categories of Analytics
    4. 1.4 Analytics Within Organizations
    5. 1.5 Ethical Implications
    6. 1.6 The Changing World of Analytics
    7. 1.7 Conclusion
    8. References
  7. Chapter 2: Getting Started with Analytics
    1. 2.1 Introduction
    2. 2.2 Five Manageable Tasks
    3. 2.3 Real Examples
    4. References
    5. Further Reading: Papers
    6. Further Reading: Books
  8. Chapter 3: The Analytics Team
    1. 3.1 Introduction
    2. 3.2 Skills Necessary for Analytics
    3. 3.3 Managing Analytical Talent
    4. 3.4 Organizing Analytics
    5. 3.5 To Where Should Analytical Functions Report?
    6. References
  9. Chapter 4: The Data
    1. 4.1 Introduction
    2. 4.2 Data Collection
    3. 4.3 Data Preparation
    4. 4.4 Data Modeling
    5. 4.5 Data Management
  10. Chapter 5: Solution Methodologies
    1. 5.1 Introduction
    2. 5.2 Macro-Solution Methodologies for the Analytics Practitioner
    3. 5.3 Micro-Solution Methodologies for the Analytics Practitioner
    4. 5.4 General Methodology-Related Considerations
    5. 5.5 Summary and Conclusions
    6. 5.6 Acknowledgments
    7. References
  11. Chapter 6: Modeling
    1. 6.1 Introduction
    2. 6.2 When Are Models Appropriate
    3. 6.3 Types of Models
    4. 6.4 Models Can Also Be Characterized by Whether They Are Deterministic or Stochastic (Random)
    5. 6.5 Counting
    6. 6.6 Probability
    7. 6.7 Probability Perspectives and Subject Matter Experts
    8. 6.8 Subject Matter Experts
    9. 6.9 Statistics
    10. 6.10 Inferential Statistics
    11. 6.11 A Stochastic Process
    12. 6.12 Digital Simulation
    13. 6.13 Mathematical Optimization
    14. 6.14 Measurement Units
    15. 6.15 Critical Path Method
    16. 6.16 Portfolio Optimization Case Study Solved By a Variety of Methods
    17. 6.17 Game Theory
    18. 6.18 Decision Theory
    19. 6.19 Susceptible, Exposed, Infected, Recovered (SEIR) Epidemiology
    20. 6.20 Search Theory
    21. 6.21 Lanchester Models of Warfare
    22. 6.22 Hughes' Salvo Model of Combat
    23. 6.23 Single-Use Models
    24. 6.24 The Principle of Optimality and Dynamic Programming
    25. 6.25 Stack-Based Enumeration
    26. 6.26 Traveling Salesman Problem: Another Case Study in Alternate Solution Methods
    27. 6.27 Model Documentation, Management, and Performance
    28. 6.28 Rules for Data Use
    29. 6.29 Data Interpolation and Extrapolation
    30. 6.30 Model Verification and Validation
    31. 6.31 Communicate with Stakeholders
    32. 6.32 Software
    33. 6.33 Where to Go from Here
    34. 6.34 Acknowledgments
    35. References
  12. Chapter 7: Machine Learning
    1. 7.1 Introduction
    2. 7.2 Supervised, Unsupervised, and Reinforcement Learning
    3. 7.3 Model Development, Selection, and Deployment for Supervised Learning
    4. 7.4 Model Fitting, Model Error, and the Bias-Variance Trade-Off
    5. 7.5 Predictive Performance Evaluation
    6. 7.6 An Overview of Supervised Learning Algorithms
    7. 7.7 Unsupervised Learning Algorithms
    8. 7.8 Conclusion
    9. 7.9 Acknowledgments
    10. References
  13. Chapter 8: Deployment and Life Cycle Management
    1. 8.1 Introduction
    2. 8.2 The Analytics Methodology: Understanding the Critical Steps in Deployment and Life Cycle Management
    3. 8.3 Overarching Issues of Life Cycle Management
  14. Chapter 9: The Blossoming Analytics Talent Pool: An Overview of the Analytics Ecosystem
    1. 9.1 Introduction
    2. 9.2 Analytics Industry Ecosystem
    3. 9.3 Conclusions
    4. References
  15. Appendix: Writing and Teaching Analytics with Cases
    1. A.1 What Is a Teaching Case?
    2. A.2 My Motivation for Using Teaching Cases
    3. A.3 Writing a Teaching Case
    4. A.4 Using a Teaching Case
    5. A.5 An Example of a Simple Case
    6. A.6 Final Thoughts
    7. Bibliography
  16. Index
  17. End User License Agreement
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