Books & Videos

Table of Contents

  1. Chapter 1 Introduction: Data-Analytic Thinking

    1. The Ubiquity of Data Opportunities

    2. Example: Hurricane Frances

    3. Example: Predicting Customer Churn

    4. Data Science, Engineering, and Data-Driven Decision Making

    5. Data Processing and “Big Data”

    6. From Big Data 1.0 to Big Data 2.0

    7. Data and Data Science Capability as a Strategic Asset

    8. Data-Analytic Thinking

    9. This Book

    10. Data Mining and Data Science, Revisited

    11. Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist

    12. Summary

  2. Chapter 2 Business Problems and Data Science Solutions

    1. From Business Problems to Data Mining Tasks

    2. Supervised Versus Unsupervised Methods

    3. Data Mining and Its Results

    4. The Data Mining Process

    5. Implications for Managing the Data Science Team

    6. Other Analytics Techniques and Technologies

    7. Summary

  3. Chapter 3 Introduction to Predictive Modeling: From Correlation to Supervised Segmentation

    1. Models, Induction, and Prediction

    2. Supervised Segmentation

    3. Visualizing Segmentations

    4. Trees as Sets of Rules

    5. Probability Estimation

    6. Example: Addressing the Churn Problem with Tree Induction

    7. Summary

  4. Chapter 4 Fitting a Model to Data

    1. Classification via Mathematical Functions

    2. Regression via Mathematical Functions

    3. Class Probability Estimation and Logistic “Regression”

    4. Example: Logistic Regression versus Tree Induction

    5. Nonlinear Functions, Support Vector Machines, and Neural Networks

    6. Summary

  5. Chapter 5 Overfitting and Its Avoidance

    1. Generalization

    2. Overfitting

    3. Overfitting Examined

    4. Example: Overfitting Linear Functions

    5. * Example: Why Is Overfitting Bad?

    6. From Holdout Evaluation to Cross-Validation

    7. The Churn Dataset Revisited

    8. Learning Curves

    9. Overfitting Avoidance and Complexity Control

    10. Summary

  6. Chapter 6 Similarity, Neighbors, and Clusters

    1. Similarity and Distance

    2. Nearest-Neighbor Reasoning

    3. Some Important Technical Details Relating to Similarities and Neighbors

    4. Clustering

    5. Stepping Back: Solving a Business Problem Versus Data Exploration

    6. Summary

  7. Chapter 7 Decision Analytic Thinking I: What Is a Good Model?

    1. Evaluating Classifiers

    2. Generalizing Beyond Classification

    3. A Key Analytical Framework: Expected Value

    4. Evaluation, Baseline Performance, and Implications for Investments in Data

    5. Summary

  8. Chapter 8 Visualizing Model Performance

    1. Ranking Instead of Classifying

    2. Profit Curves

    3. ROC Graphs and Curves

    4. The Area Under the ROC Curve (AUC)

    5. Cumulative Response and Lift Curves

    6. Example: churnperformance analytics for modeling performance analytics, for modeling churn Performance Analytics for Churn Modeling

    7. Summary

  9. Chapter 9 Evidence and Probabilities

    1. Example: Targeting Online Consumers With Advertisements

    2. Combining Evidence Probabilistically

    3. Applying Bayes’ Rule to Data Science

    4. A Model of Evidence “Lift”

    5. Example: Evidence Lifts from Facebook "Likes"

    6. Summary

  10. Chapter 10 Representing and Mining Text

    1. Why Text Is Important

    2. Why Text Is Difficult

    3. Representation

    4. Example: Jazz Musicians

    5. * The Relationship of IDF to Entropy

    6. Beyond Bag of Words

    7. Example: Mining News Stories to Predict Stock Price Movement

    8. Summary

  11. Chapter 11 Decision Analytic Thinking II: Toward Analytical Engineering

    1. Targeting the Best Prospects for a Charity Mailing

    2. Our Churn Example Revisited with Even More Sophistication

  12. Chapter 12 Other Data Science Tasks and Techniques

    1. Co-occurrences and Associations: Finding Items That Go Together

    2. Profiling: Finding Typical Behavior

    3. Link Prediction and Social Recommendation

    4. Data Reduction, Latent Information, and Movie Recommendation

    5. Bias, Variance, and Ensemble Methods

    6. Data-Driven Causal Explanation and a Viral Marketing Example

    7. Summary

  13. Chapter 13 Data Science and Business Strategy

    1. Thinking Data-Analytically, Redux

    2. Achieving Competitive Advantage with Data Science

    3. Sustaining Competitive Advantage with Data Science

    4. Attracting and Nurturing Data Scientists and Their Teams

    5. Examine Data Science Case Studies

    6. Be Ready to Accept Creative Ideas from Any Source

    7. Be Ready to Evaluate Proposals for Data Science Projects

    8. A Firm’s Data Science Maturity

  14. Chapter 14 Conclusion

    1. The Fundamental Concepts of Data Science

    2. What Data Can’t Do: Humans in the Loop, Revisited

    3. Privacy, Ethics, and Mining Data About Individuals

    4. Is There More to Data Science?

    5. Final Example: From Crowd-Sourcing to Cloud-Sourcing

    6. Final Words

  1. Appendix Proposal Review Guide

    1. Business and Data Understanding

    2. Data Preparation

    3. Modeling

    4. Evaluation and Deployment

  2. Appendix Another Sample Proposal

    1. Scenario and Proposal

  3. Glossary

  4. Appendix Bibliography

  5. Index

  6. Colophon