Analytical Skills for AI and Data Science

Table of contents

1. Preface
   1. Why Analytical Skills for AI?
   2. Use Case-Driven Approach
   3. What This Book Isn’t
   4. Who This Book Is For
   5. What’s Needed
   6. Conventions Used in This Book
   7. Using Code Examples
   8. O’Reilly Online Learning
   9. How to Contact Us
   10. Acknowledgments
2. 1. Analytical Thinking and the AI-Driven Enterprise
   1. What Is AI?
   2. Why Current AI Won’t Deliver on Its Promises
   3. How Did We Get Here?
      1. The Data Revolution
   4. A Tale of Unrealized Expectations
   5. Analytical Skills for the Modern AI-Driven Enterprise
   6. Key Takeways
   7. Further Reading
3. 2. Intro to Analytical Thinking
   1. Descriptive, Predictive, and Prescriptive Questions
      1. When Predictive Analysis Is Powerful: The Case of Cancer Detection
      2. Descriptive Analysis: The Case of Customer Churn
   2. Business Questions and KPIs
      1. KPIs to Measure the Success of a Loyalty Program
   3. An Anatomy of a Decision: A Simple Decomposition
      1. An Example: Why Did You Buy This Book?
   4. A Primer on Causation
      1. Defining Correlation and Causation
      2. Some Difficulties in Estimating Causal Effects
   5. Uncertainty
      1. Uncertainty from Simplification
      2. Uncertainty from Heterogeneity
      3. Uncertainty from Social Interactions
      4. Uncertainty from Ignorance
   6. Key Takeaways
   7. Further Reading
4. 3. Learning to Ask Good Business Questions
   1. From Business Objectives to Business Questions
   2. Descriptive, Predictive, and Prescriptive Questions
   3. Always Start with the Business Question and Work Backward
   4. Further Deconstructing the Business Questions
      1. Example with a Two-Sided Platform
   5. Learning to Ask Business Questions: Examples from Common Use Cases
      1. Lowering Churn
      2. Cross-Selling: Next-Best Offer
      3. CAPEX Optimization
      4. Store Locations
      5. Who Should I Hire?
      6. Delinquency Rates
      7. Stock or Inventory Optimization
      8. Store Staffing
   6. Key Takeaways
   7. Further Reading
5. 4. Actions, Levers, and Decisions
   1. Understanding What Is Actionable
   2. Physical Levers
   3. Human Levers
      1. Why Do We Behave the Way We Do?
      2. Levers from Restrictions
      3. Levers That Affect Our Preferences
      4. Levers That Change Your Expectations
   4. Revisiting Our Use Cases
      1. Customer Churn
      2. Cross-Selling
      3. Capital Expenditure (CAPEX) Optimization
      4. Store Locations
      5. Who Should I Hire?
      6. Delinquency Rates
      7. Stock Optimization
      8. Store Staffing
   5. Key Takeaways
   6. Further Reading
6. 5. From Actions to Consequences: Learning How to Simplify
   1. Why Do We Need to Simplify?
      1. First- and Second-Order Effects
   2. Exercising Our Analytical Muscle: Welcome Fermi
      1. How Many Tennis Balls Fit the Floor of This Rectangular Room?
      2. How Much Would You Charge to Clean Every Window in Mexico City?
      3. Fermi Problems to Make Preliminary Business Cases
   3. Revisiting the Examples from Chapter 3
      1. Customer Churn
      2. Cross-Selling
      3. CAPEX Optimization
      4. Store Locations
      5. Delinquency Rates
      6. Stock Optimization
      7. Store Staffing
   4. Key Takeaways
   5. Further Reading
7. 6. Uncertainty
   1. Where Does Uncertainty Come From?
   2. Quantifying Uncertainty
      1. Expected Values
   3. Making Decisions Without Uncertainty
   4. Making Simple Decisions Under Uncertainty
   5. Decisions Under Uncertainty
      1. Is This the Best We Can Do?
      2. But This Is a Frequentist Argument
   6. Normative and Descriptive Theories of Decision-Making
   7. Some Paradoxes in Decision-Making Under Uncertainty
      1. The St. Petersburg Paradox
      2. Risk Aversion
   8. Putting it All into Practice
      1. Estimating the Probabilities
      2. Estimating Expected Values
      3. Frequentist and Bayesian Methods
   9. Revisiting Our Use Cases
      1. Customer Churn
      2. Cross-Selling
      3. CAPEX Optimization
      4. Store Locations
      5. Who to Hire
      6. Delinquency Rates
      7. Stock Optimization
   10. Key Takeaways
   11. Further Reading
8. 7. Optimization
   1. What Is Optimization?
      1. Numerical Optimization Is Hard
      2. Optimization Is Not New in Business Settings
      3. Price and Revenue Optimization
   2. Optimization Without Uncertainty
      1. Customer Churn
      2. Cross-Selling
      3. CAPEX Investment
      4. Optimal Staffing
      5. Optimal Store Locations
   3. Optimization with Uncertainty
      1. Customer Churn
      2. Cross-Selling
      3. Optimal Staffing
      4. Tricks for Solving Optimization Problems Under Uncertainty
   4. Key Takeaways
   5. Further Reading
9. 8. Wrapping Up
   1. Analytical Skills
      1. Asking Prescriptive Questions
      2. Understanding Causality
      3. Simplify
      4. Embracing Uncertainty
      5. Tackling Optimization
   2. The AI-Driven Enterprise of the Future
      1. Back to AI
   3. Some Final Thoughts
10. A. A Brief Introduction to Machine Learning
    1. What Is Machine Learning?
    2. A Taxonomy of ML Models
       1. Supervised Learning
       2. Unsupervised Learning
       3. Semisupervised Learning
    3. Regression and Classification
    4. Making Predictions
       1. Caveats to the Plug-in Approach
       2. Where Do These Functions Come From?
       3. Making Good Predictions
    5. From Linear Regression to Deep Learning
       1. Linear Regression
       2. Neural Networks
    6. A Primer on A/B Testing
    7. Further Reading
11. Index