Analytical Skills for AI and Data Science. Building Skills for an AI-Driven Enterprise

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• Preface
  • Why Analytical Skills for AI?
  • Use Case-Driven Approach
  • What This Book Isnt
  • Who This Book Is For
  • Whats Needed
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Analytical Thinking and the AI-Driven Enterprise
  • What Is AI?
  • Why Current AI Wont Deliver on Its Promises
  • How Did We Get Here?
    • The Data Revolution
      • The three Vs
      • Data maturity models
        • Descriptive stage
        • Predictive stage
        • Prescriptive stage
  • A Tale of Unrealized Expectations
  • Analytical Skills for the Modern AI-Driven Enterprise
  • Key Takeways
  • Further Reading
• 2. Intro to Analytical Thinking
  • Descriptive, Predictive, and Prescriptive Questions
    • When Predictive Analysis Is Powerful: The Case of Cancer Detection
    • Descriptive Analysis: The Case of Customer Churn
      • Describing churn
      • Predicting churn
      • Prescribing courses of action to reduce churn
  • Business Questions and KPIs
    • KPIs to Measure the Success of a Loyalty Program
  • An Anatomy of a Decision: A Simple Decomposition
    • An Example: Why Did You Buy This Book?
  • A Primer on Causation
    • Defining Correlation and Causation
    • Some Difficulties in Estimating Causal Effects
      • Problem 1: We cant observe counterfactuals
      • Problem 2: Heterogeneity
      • Problem 3: Confounders
      • Problem 4: Selection effects
      • A/B testing
  • Uncertainty
    • Uncertainty from Simplification
    • Uncertainty from Heterogeneity
    • Uncertainty from Social Interactions
    • Uncertainty from Ignorance
  • Key Takeaways
  • Further Reading
• 3. Learning to Ask Good Business Questions
  • From Business Objectives to Business Questions
  • Descriptive, Predictive, and Prescriptive Questions
  • Always Start with the Business Question and Work Backward
  • Further Deconstructing the Business Questions
    • Example with a Two-Sided Platform
  • Learning to Ask Business Questions: Examples from Common Use Cases
    • Lowering Churn
      • Defining the business question
      • Descriptive questions
      • Predictive questions
      • Prescriptive questions
    • Cross-Selling: Next-Best Offer
      • Defining the business question
      • Descriptive questions
      • Predictive questions
      • Prescriptive questions
    • CAPEX Optimization
    • Store Locations
    • Who Should I Hire?
    • Delinquency Rates
    • Stock or Inventory Optimization
    • Store Staffing
  • Key Takeaways
  • Further Reading
• 4. Actions, Levers, and Decisions
  • Understanding What Is Actionable
  • Physical Levers
  • Human Levers
    • Why Do We Behave the Way We Do?
    • Levers from Restrictions
      • Time restrictions
    • Levers That Affect Our Preferences
      • Genetics
      • Individual and social learning
      • Social reasons: strategic effects
      • Social reasons: conformity and peer effects
      • Framing effects
      • Loss aversion
    • Levers That Change Your Expectations
      • The availability and representativeness heuristics
  • Revisiting Our Use Cases
    • Customer Churn
    • Cross-Selling
    • Capital Expenditure (CAPEX) Optimization
    • Store Locations
    • Who Should I Hire?
    • Delinquency Rates
    • Stock Optimization
    • Store Staffing
  • Key Takeaways
  • Further Reading
• 5. From Actions to Consequences: Learning How to Simplify
  • Why Do We Need to Simplify?
    • First- and Second-Order Effects
  • Exercising Our Analytical Muscle: Welcome Fermi
    • How Many Tennis Balls Fit the Floor of This Rectangular Room?
    • How Much Would You Charge to Clean Every Window in Mexico City?
    • Fermi Problems to Make Preliminary Business Cases
      • Paying our customers for their contact info
      • Excessive contact attempts increase the probability of churn
      • Should you accept the offer from that startup?
  • Revisiting the Examples from Chapter 3
    • Customer Churn
    • Cross-Selling
    • CAPEX Optimization
      • Price effect
      • Quantity effect
    • Store Locations
    • Delinquency Rates
    • Stock Optimization
    • Store Staffing
  • Key Takeaways
  • Further Reading
• 6. Uncertainty
  • Where Does Uncertainty Come From?
  • Quantifying Uncertainty
    • Expected Values
      • Bidding for a highway construction contract
      • Interpreting expected values
  • Making Decisions Without Uncertainty
  • Making Simple Decisions Under Uncertainty
  • Decisions Under Uncertainty
    • Is This the Best We Can Do?
    • But This Is a Frequentist Argument
  • Normative and Descriptive Theories of Decision-Making
  • Some Paradoxes in Decision-Making Under Uncertainty
    • The St. Petersburg Paradox
    • Risk Aversion
  • Putting it All into Practice
    • Estimating the Probabilities
      • Estimating unconditional probabilities
      • Estimating conditional probabilities
      • A/B testing
      • Bandit problems
    • Estimating Expected Values
    • Frequentist and Bayesian Methods
  • Revisiting Our Use Cases
    • Customer Churn
    • Cross-Selling
    • CAPEX Optimization
    • Store Locations
    • Who to Hire
    • Delinquency Rates
    • Stock Optimization
  • Key Takeaways
  • Further Reading
• 7. Optimization
  • What Is Optimization?
    • Numerical Optimization Is Hard
    • Optimization Is Not New in Business Settings
    • Price and Revenue Optimization
  • Optimization Without Uncertainty
    • Customer Churn
    • Cross-Selling
    • CAPEX Investment
    • Optimal Staffing
    • Optimal Store Locations
  • Optimization with Uncertainty
    • Customer Churn
    • Cross-Selling
    • Optimal Staffing
    • Tricks for Solving Optimization Problems Under Uncertainty
  • Key Takeaways
  • Further Reading
• 8. Wrapping Up
  • Analytical Skills
    • Asking Prescriptive Questions
    • Understanding Causality
      • Thinking outside the box
    • Simplify
    • Embracing Uncertainty
      • Do-nothing approach
      • The data-driven approach
      • The model-driven approach
    • Tackling Optimization
      • Understanding the objective function
      • Dealing with local optima
      • Sensitivity to initial guesses
      • Scaling and production issues
  • The AI-Driven Enterprise of the Future
    • Back to AI
      • Learning how to make decisions
      • Some problems with this approach to automatic decision-making
      • Ethics
  • Some Final Thoughts
• A. A Brief Introduction to Machine Learning
  • What Is Machine Learning?
  • A Taxonomy of ML Models
    • Supervised Learning
    • Unsupervised Learning
    • Semisupervised Learning
  • Regression and Classification
  • Making Predictions
    • Caveats to the Plug-in Approach
    • Where Do These Functions Come From?
    • Making Good Predictions
  • From Linear Regression to Deep Learning
    • Linear Regression
      • Controlling for other variables
      • Overfitting
    • Neural Networks
      • Activation functions: adding some extra nonlinearity
      • The success of deep learning
  • A Primer on A/B Testing
    • A/B testing in practice
    • Understanding power and size calculations
    • False positives and false negatives
  • Further Reading
• Index

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