Numsense! Data Science for the Layman


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Numsense! Data Science for the Layman

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About the Authors

Annalyn Ng
Annalyn Ng

Annalyn Ng completed her MPhil at the University of Cambridge Psychometrics Centre, where she mined consumer data for targeted advertising, and programmed cognitive tests for job recruitment. Annalyn was also an undergrad statistics tutor at the University of Michigan (Ann Arbor). Disney Research later roped her into their behavioral sciences team, where she examined psychological profiles of consumers.

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Kenneth Soo
Kenneth Soo

Kenneth Soo was the top student for all 3 years of his Math/OR/Stats/Econs (MORSE) degree at the University of Warwick. He is currently completing his MS in Statistics at Stanford University. He was a research assistant with the Operational Research & Management Sciences Group at University of Warwick, working on bi-objective robust optimization with applications in networks subject to random failures.

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Table of Contents

  • Foreword
  • Preface
  • Why Data Science?
  • 1. Basics in a Nutshell
    • 1.1 Data Preparation
    • 1.2 Algorithm Selection
    • 1.3 Parameter Tuning
    • 1.4 Evaluating Results
    • 1.5 Summary
  • 2. k-Means Clustering
    • 2.1 Finding Customer Clusters
    • 2.2 Example: Personality Profiles of Movie Fans
    • 2.3 Defining Clusters
    • 2.4 Limitations
    • 2.5 Summary
  • 3. Principal Component Analysis
    • 3.1 Exploring Nutritional Content of Food
    • 3.2 Principal Components
    • 3.3 Example: Analyzing Food Groups
    • 3.4 Limitations
    • 3.5 Summary
  • 4. Association Rules
    • 4.1 Discovering Purchasing Patterns
    • 4.2 Support, Confidence and Lift
    • 4.3 Example: Transacting Grocery Sales
    • 4.4 Apriori Principle
    • 4.5 Limitations
    • 4.6 Summary
  • 5. Social Network Analysis
    • 5.1 Mapping out Relationships
    • 5.2 Example: Geopolitics in Weapons Trade
    • 5.3 Louvain Method
    • 5.4 PageRank Algorithm
    • 5.5 Limitations
    • 5.6 Summary
  • 6. Regression Analysis
    • 6.1 Deriving a Trend Line
    • 6.2 Example: Predicting House Prices
    • 6.3 Gradient Descent
    • 6.4 Regression Coefficients
    • 6.5 Correlation Coefficients
    • 6.6 Limitations
    • 6.7 Summary
  • 7. k-Nearest Neighbors and Anomaly Detection
    • 7.1 Food Forensics
    • 7.2 Birds of a Feather Flock Together
    • 7.3 Example: Distilling Differences in Wine
    • 7.4 Anomaly Detection
    • 7.5 Limitations
    • 7.6 Summary
  • 8. Support Vector Machine
    • 8.1 No” or “Oh No”?
    • 8.2 Example: Predicting Heart Disease
    • 8.3 Delineating an Optimal Boundary
    • 8.4 Limitations
    • 8.5 Summary
  • 9. Decision Tree
    • 9.1 Predicting Survival in a Disaster
    • 9.2 Example: Escaping from the Titanic
    • 9.3 Generating a Decision Tree
    • 9.4 Limitations
    • 9.5 Summary
  • 10. Random Forests
    • 10.1 Wisdom of the Crowd
    • 10.2 Example: Forecasting Crime
    • 10.3 Ensembles
    • 10.4 Bootstrap Aggregating (Bagging)
    • 10.5 Limitations
    • 10.6 Summary
  • 11. Neural Networks
    • 11.1 Building a Brain
    • 11.2 Example: Recognizing Handwritten Digits
    • 11.3 Components of a Neural Network
    • 11.4 Activation Rules
    • 11.5 Limitations
    • 11.6 Summary
  • 12. A/B Testing and Multi-Armed Bandits
    • 12.1 Basics of A/B testing
    • 12.2 Limitations of A/B testing
    • 12.3 Epsilon-Decreasing Strategy
    • 12.4 Example: Multi-Arm Bandits
    • 12.5 Fun Fact: Sticking to the Winner
    • 12.6 Limitations of an Epsilon-Decreasing Strategy
    • 12.7 Summary
  • Appendix
    • A. Overview of Unsupervised Learning Algorithms
    • B. Overview of Supervised Learning Algorithms
    • C. List of Tuning Parameters
    • D. More Evaluation Metrics
  • Glossary
  • Data Sources and References
  • About the Authors

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