Data Science: The Hard Parts

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• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Data Analytics Techniques
• 1. So What? Creating Value with Data Science
  • What Is Value?
  • What: Understanding the Business
  • So What: The Gist of Value Creation in DS
  • Now What: Be a Go-Getter
  • Measuring Value
  • Key Takeaways
  • Further Reading
• 2. Metrics Design
  • Desirable Properties That Metrics Should Have
    • Measurable
    • Actionable
    • Relevance
    • Timeliness
  • Metrics Decomposition
    • Funnel Analytics
    • Stock-Flow Decompositions
    • P×Q-Type Decompositions
  • Example: Another Revenue Decomposition
  • Example: Marketplaces
  • Key Takeaways
  • Further Reading
• 3. Growth Decompositions: Understanding Tailwinds and Headwinds
  • Why Growth Decompositions?
  • Additive Decomposition
    • Example
    • Interpretation and Use Cases
  • Multiplicative Decomposition
    • Example
    • Interpretation
  • Mix-Rate Decompositions
    • Example
    • Interpretation
  • Mathematical Derivations
    • Additive Decomposition
    • Multiplicative Decomposition
    • Mix-Rate Decomposition
  • Key Takeaways
  • Further Reading
• 4. 2×2 Designs
  • The Case for Simplification
  • Whats a 2×2 Design?
  • Example: Test a Model and a New Feature
  • Example: Understanding User Behavior
  • Example: Credit Origination and Acceptance
  • Example: Prioritizing Your Workflow
  • Key Takeaways
  • Further Reading
• 5. Building Business Cases
  • Some Principles to Construct Business Cases
  • Example: Proactive Retention Strategy
  • Fraud Prevention
  • Purchasing External Datasets
  • Working on a Data Science Project
  • Key Takeaways
  • Further Reading
• 6. Whats in a Lift?
  • Lifts Defined
  • Example: Classifier Model
  • Self-Selection and Survivorship Biases
  • Other Use Cases for Lifts
  • Key Takeaways
  • Further Reading
• 7. Narratives
  • Whats in a Narrative: Telling a Story with Your Data
    • Clear and to the Point
    • Credible
    • Memorable
    • Actionable
  • Building a Narrative
    • Science as Storytelling
    • What, So What, and Now What?
      • What?
      • So what?
      • Now what?
  • The Last Mile
    • Writing TL;DRs
    • Tips to Write Memorable TL;DRs
    • Example: Writing a TL;DR for This Chapter
    • Delivering Powerful Elevator Pitches
    • Presenting Your Narrative
  • Key Takeaways
  • Further Reading
• 8. Datavis: Choosing the Right Plot to Deliver a Message
  • Some Useful and Not-So-Used Data Visualizations
    • Bar Versus Line Plots
    • Slopegraphs
    • Waterfall Charts
    • Scatterplot Smoothers
    • Plotting Distributions
  • General Recommendations
    • Find the Right Datavis for Your Message
    • Choose Your Colors Wisely
    • Different Dimensions in a Plot
    • Aim for a Large Enough Data-Ink Ratio
    • Customization Versus Semiautomation
    • Get the Font Size Right from the Beginning
    • Interactive or Not
    • Stay Simple
    • Start by Explaining the Plot
  • Key Takeaways
  • Further Reading
• II. Machine Learning
• 9. Simulation and Bootstrapping
  • Basics of Simulation
  • Simulating a Linear Model and Linear Regression
  • What Are Partial Dependence Plots?
  • Omitted Variable Bias
  • Simulating Classification Problems
    • Latent Variable Models
    • Comparing Different Algorithms
  • Bootstrapping
  • Key Takeaways
  • Further Reading
• 10. Linear Regression: Going Back to Basics
  • Whats in a Coefficient?
  • The Frisch-Waugh-Lovell Theorem
  • Why Should You Care About FWL?
  • Confounders
  • Additional Variables
  • The Central Role of Variance in ML
  • Key Takeaways
  • Further Reading
• 11. Data Leakage
  • What Is Data Leakage?
    • Outcome Is Also a Feature
    • A Function of the Outcome Is Itself a Feature
    • Bad Controls
    • Mislabeling of a Timestamp
    • Multiple Datasets with Sloppy Time Aggregations
    • Leakage of Other Information
  • Detecting Data Leakage
  • Complete Separation
  • Windowing Methodology
    • Choosing the Length of the Windows
    • The Training Stage Mirrors the Scoring Stage
    • Implementing the Windowing Methodology
  • I Have Leakage: Now What?
  • Key Takeaways
  • Further Reading
• 12. Productionizing Models
  • What Does Production Ready Mean?
    • Batch Scores (Offline)
    • Real-Time Model Objects
  • Data and Model Drift
  • Essential Steps in any Production Pipeline
    • Get and Transform Data
    • Validate Data
    • Training and Scoring Stages
    • Validate Model and Scores
    • Deploy Model and Scores
  • Key Takeaways
  • Further Reading
• 13. Storytelling in Machine Learning
  • A Holistic View of Storytelling in ML
  • Ex Ante and Interim Storytelling
    • Creating Hypotheses
      • Predicting human behavior
      • Predicting system behavior
      • Predicting downstream metrics
    • Feature Engineering
  • Ex Post Storytelling: Opening the Black Box
    • Interpretability-Performance Trade-Off
    • Linear Regression: Setting a Benchmark
    • Feature Importance
    • Heatmaps
    • Partial Dependence Plots
    • Accumulated Local Effects
  • Key Takeaways
  • Further Reading
• 14. From Prediction to Decisions
  • Dissecting Decision Making
  • Simple Decision Rules by Smart Thresholding
    • Precision and Recall
    • Example: Lead Generation
  • Confusion Matrix Optimization
  • Key Takeaways
  • Further Reading
• 15. Incrementality: The Holy Grail of Data Science?
  • Defining Incrementality
    • Causal Reasoning to Improve Prediction
    • Causal Reasoning as a Differentiator
    • Improved Decision Making
  • Confounders and Colliders
  • Selection Bias
  • Unconfoundedness Assumption
  • Breaking Selection Bias: Randomization
  • Matching
  • Machine Learning and Causal Inference
    • Open Source Codebases
    • Double Machine Learning
  • Key Takeaways
  • Further Reading
• 16. A/B Tests
  • What Is an A/B Test?
  • Decision Criterion
  • Minimum Detectable Effects
    • Choosing the Statistical Power, Level, and P
    • Estimating the Variance of the Outcome
    • Simulations
    • Example: Conversion Rates
    • Setting the MDE
  • Hypotheses Backlog
    • Metric
    • Hypothesis
    • Ranking
  • Governance of Experiments
  • Key Takeaways
  • Further Reading
• 17. Large Language Models and the Practice of Data Science
  • The Current State of AI
  • What Do Data Scientists Do?
  • Evolving the Data Scientists Job Description
    • Case Study: A/B Testing
    • Case Study: Data Cleansing
    • Case Study: Machine Learning
  • LLMs and This Book
  • Key Takeaways
  • Further Reading
• Index

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