It's All Analytics!

Table of contents

1. Cover
2. Half-Title
3. Title
4. Copyright
5. Contents
6. Foreword Number One
7. Foreword Number Two
8. Foreword Number Three
9. Preface
10. Endorsements
11. Authors
12. 1 You Need This Book
    1. Preamble
    2. The Hip, the Hype, the Fears, the Intrigue, and the Reality:
       1. Hype, Fear, and Intrigue No 1:
       2. Hype, Fear, and Intrigue No 2:
       3. Hype, Fear, and Intrigue No 3:
    3. Professionals Need This Book
       1. Introduction
       2. Technology Keeps Raging, but We Need More Than Technology to Be Successful
       3. Data and Analytics Explosion
    4. A Bright Side of the Revolution
       1. Where Is Someone to Turn for Information?
       2. The Problem, Too Many Self-Interests: The Need for an Objective View
       3. There Are Many Other Professional Stories That Are Concerned about Whether Analytics Is Important; Here Are a Few More Examples
    5. What This Book Is Not:
    6. Why This Book?
    7. Sure, Business, but Why Healthcare, Public Policy, and Business?
    8. How This Book Is Organized
    9. References
    10. Resources for the Avid Learner
13. 2 Building a Successful Program
    1. Preamble
    2. The Hip, the Hype, the Fears, the Intrigue, and the Reality
       1. The Hype
       2. Reality
       3. The Hype
       4. Reality
       5. The Hype
       6. Reality
    3. Introduction
    4. Culture and Organization – Gaps and Limitations
       1. Gaps in Analytics Programs
       2. Characterizing Common Problems
    5. Don’t Confuse Organizational Gaps for Project Gaps
    6. Justifying a Data-Driven Organization
    7. Motivations
       1. Critical Business Events
    8. Analytics as a Winning Strategy
       1. Part I – New Programs and Technologies
       2. Part II – More Traditional Methods of Justification
       3. Positive Return of Investment
       4. Scale
       5. Productivity
       6. Reliability
       7. Sustainability
    9. Designing the Organization for Program Success
    10. Motivation / Communication and Commitment
        1. Establish Clear Business Outcomes
    11. Organization Structure and Design
        1. The Organization and Its Goals – Alignment
    12. Organizational Structure
    13. Centralized Analytics
    14. Decentralized or Embedded Analytics
    15. Multidisciplinary Roles for Analytics
        1. Data Scientists
        2. Data Engineers
        3. Citizen Data Scientists
        4. Developers
        5. Business Experts
        6. Business Leaders
        7. Project Managers
    16. Analytics Oversight Committee (AOC) and Governance Committee (Board Report)
    17. Postscript
    18. References
    19. Resources for the Avid Learner
14. 3 Some Fundamentals – Process, Data, and Models
    1. Preamble
    2. The Hip, the Hype, the Fears, the Intrigue, and the Reality
       1. The Hype
       2. Reality
    3. Introduction
    4. Framework for Analytics – Some Fundamentals
    5. Processes Drive Data
    6. Models, Methods, and Algorithms
       1. Models, Models, Models
    7. Statistical Models
    8. Rules of Thumb, Heuristic Models
    9. A Note on Cognition
    10. Algorithms, Algorithms, Algorithms
    11. Distinction between Methods That Generate Models
    12. There Is No Free Lunch
    13. A Process Methodology for Analytics
        1. CRISP-DM: The Six Phases:
    14. Last Considerations
        1. Data Architecture
        2. Analytics Architecture
    15. Postscript
    16. References
    17. Resources for the Avid Learner
15. 4 It’s All Analytics!
    1. Preamble
    2. Overview of Analytics – It’s All Analytics
    3. Analytics of Every Form and Analytics Everywhere
       1. Introduction
       2. Analytics Mega List
    4. Breaking it Down, Categorizing Analytics
       1. Introduction
       2. Gartner’s Classification
       3. Descriptive Analytics
       4. Diagnostic Analytics
       5. Predictive Analytics
       6. Prescriptive Analytics
       7. Process Optimization
       8. Some Additional Thoughts on Classifying Analytics
    5. Fundamentals of Analytics – Data Basics
       1. Introduction
       2. Four Scales of Measurement
       3. Data Formats
       4. Data Stores
       5. Provisioning Data for Analytics
       6. Data Sourcing
       7. Data Quality Assessment and Remediation
       8. Integrate and Repeat
       9. Exploratory Data Analysis (EDA)
       10. Data Transformations
       11. Data Reduction
    6. Postscript
    7. References
    8. Resources for the Avid Learner
16. 5 What Are Business Intelligence (BI) and Visual BI?
    1. Preamble
    2. Introduction
    3. Background and Chronology
       1. Basic (Digital) Reporting
       2. A View inside the Data Warehouse and Interactive BI
       3. Beyond the Data Warehouse and Enhanced Interactive Visual BI and More
       4. Business Activity Monitoring an Alert-Based BI, Version 4.0
    4. Strengths and Weaknesses of BI
       1. Transparency and Single Version of the Truth
    5. Summary
    6. Postscript
    7. References
    8. Resources for the Avid Learner
17. 6 What Are Machine Learning and Data Mining?
    1. Preamble
    2. Overview of Machine Learning and Data Mining
       1. Is There a Difference?
       2. A (Brief) Historical Perspective of Data Mining and Machine Learning
    3. What Types of Analytics Are Covered by Machine Learning?
       1. An Overview of Problem Types and Common Ground
       2. The BIG Three!
       3. Regression
       4. Classification
       5. Natural Language Processing (NLP)
       6. Some (of Many) Additional Problem Classes
       7. Association, Rules and Recommender Systems
       8. Clustering
       9. Some Comments on Model Types
       10. Some Popular Machine Learning Algorithm Classes
           1. Trees 1.0: Classification and Regression Trees or Partition Trees
           2. Trees 2.0: Advanced Trees: Boosted Trees and Random Forests, for Classification and Regression
           3. Regression Model Trees and Cubist Models
           4. Logistic and Constrained/Penalized (LASSO, Ridge, Elastic Net) Regression
           5. Multivariate Adaptive Regression Splines
           6. Support Vector Machines (SVMs)
           7. Neural Networks in 1000 Flavors
           8. K-Means and Other Clustering Algorithms
           9. Directed Acyclic Graph Analytics (Optimization, Social Networks)
           10. Association Rules
           11. AutoML (Automated Machine Learning)
    4. Transparency and Processing Time of Algorithms
    5. Model Use and Deployment
    6. Major Components of the Machine Learning Process
    7. Advantages and Limitations of Using Machine Learning
    8. Postscript
    9. References
    10. Resources for the Avid Learner
18. 7 AI (Artificial Intelligence) and How It Differs from Machine Learning
    1. Preamble
    2. Introduction
       1. Let Us Outline Two Types of AI Here – Weak AI and Strong AI
    3. AI Background and Chronology
       1. Short History of Digital AI
          1. Resurrection in the 1980s
          2. Beyond the Second AI Winter
    4. Deep Learning, Bigger, and New Data
    5. Next-Generation AI
    6. Differences of BI, Data Mining, Machine Learning, Statistics vs AI
    7. Strengths and Weakness
       1. Some Weaknesses of AI
    8. AI’s Future
       1. “How ‘Rosy’ is the FUTURE for AI?”
    9. Postscript
    10. References
    11. Resources for the Avid Learner
19. 8 What Is Data Science?
    1. Preamble
    2. Introduction
    3. Mushing All the Terms – Same Thing?
       1. Today’s Data Science?
       2. Data Science vs BI and Data Scientist
       3. Data Science vs Data Engineering vs Citizen Data Scientist
       4. Backgrounds of Data Analytics Professionals
       5. Young Professionals’ Input on What Makes a Great Data Scientist
    4. Summary
    5. Postscript
    6. References
    7. Resources for the Avid Learner
20. 9 Big Data and Bigger Data, Little Data, Cloud, and Other Data
    1. Preamble
    2. Introduction
    3. Three Popular Forms and Two Divisions of Data
    4. What Is Big Data?
    5. Why the Push to Big Data? Why Is Big Data Technology Attractive?
    6. The Hype of Big Data
    7. Pivotal Changes in Big Data Technology
    8. Brief Notes on Cloud
    9. “Not Big Data” Is Alive and Well and Lessons from the Swamp
    10. A Brief Note on Subjective and Synthetic Data
    11. Other Important Data Focuses of Today and Tomorrow
        1. Data Virtualization (DV)
        2. Streaming Data
        3. Events (Event-Driven or Event Data)
        4. Geospatial
        5. IoT (Internet of Things)
        6. High-Performance In-Memory Computing Beyond Spark
        7. Grid and GPU Computing
        8. Near-Memory Computing
        9. Data Fabric
    12. Future Careers in Data
    13. Postscript
    14. References
    15. For the Avid Learner
21. 10 Statistics, Causation, and Prescriptive Analytics
    1. Preamble
    2. Some Statistical Foundations
       1. Introduction
       2. Two Major Divisions of Statistics – Descriptive Statistics and Inferential Statistics
       3. What Made Statistics Famous?
          1. Criminal Trials and Hypothesis Testing
          2. The Scientific Method
       4. Two Major Paradigms of Statistics
          1. Bayesian Statistics
          2. Classical or Frequentist Statistics
       5. Dividing It Up – Assumption Heavy and Assumption Light Statistics
          1. Non-parametric and distribution free statistics (assumption light)
       6. Four Domains in Statistics to Mention
          1. Statistics in Predictive Analytics
          2. Design of Experiments (DoE)
          3. Statistical Process Control (SPC)
          4. Time Series
       7. An Ever-Important Reminder
       8. Statistics Summary
          1. Advantages of Statistics vs BI, Machine Learning and AI
          2. Disadvantages of Statistics vs BI, Machine Learning and AI
    3. Comparison of Data-Driven Paradigms Thus Far
       1. Business Intelligence (BI)
       2. Machine Learning and Data Mining
       3. Artificial Intelligence (AI)
       4. Statistics
    4. Predictive Analytics vs Prescriptive Analytics – The Missing Link Is Causation
    5. Assuming or Establishing Causation
    6. Ladder of Causation
    7. Predicting an Increasing Trend – Structural Causal Models and Causal Inference
    8. Summary
    9. Postscript
    10. References
    11. Resources for the Avid Learner
22. 11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More)
    1. Preamble
    2. Introduction
    3. Computer Science
    4. Management Science
    5. Decision Science
    6. Operations Research
    7. Engineering
    8. Finance and Econometrics
    9. Simulation, Sensitivity and Scenario Analysis
       1. Sensitivity Analysis
       2. Scenario Analysis
       3. Systems Thinking
    10. Postscript
    11. References
    12. Resources for the Avid Learner
23. 12 Looking Ahead
    1. Farewell, Until Next Time
24. Index