Preface
1. Introduction: Data-Analytic Thinking
-The Ubiquity of Data Opportunities
-Example: Hurricane Frances
-Example: Predicting Customer Churn
-Data Science, Engineering, and Data-Driven Decision Making
-Data Processing and 'Big Data'
-From Big Data 1.0 to Big Data 2.0
-Data and Data Science Capability as a Strategic Asset
-Data-Analytic Thinking
-This Book
-Data Mining and Data Science, Revisited
-Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist
-Summary
2. Business Problems and Data Science Solutions
-From Business Problems to Data Mining Tasks
-Supervised Versus Unsupervised Methods
-Data Mining and Its Results
-The Data Mining Process
-Implications for Managing the Data Science Team
-Other Analytics Techniques and Technologies
-Summary
3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
-Models, Induction, and Prediction
-Supervised Segmentation
-Visualizing Segmentations
-Trees as Sets of Rules
-Probability Estimation
-Example: Addressing the Churn Problem with Tree Induction
-Summary
4. Fitting a Model to Data
-Classification via Mathematical Functions
-Regression via Mathematical Functions
-Class Probability Estimation and Logistic 'Regression'
-Example: Logistic Regression versus Tree Induction
-Nonlinear Functions, Support Vector Machines, and Neural Networks
-Summary
5. Overfitting and Its Avoidance
-Generalization
-Overfitting
-Overfitting Examined
-Example: Overfitting Linear Functions
-* Example: Why Is Overfitting Bad?
-From Holdout Evaluation to Cross-Validation
-The Churn Dataset Revisited
-Learning Curves
-Overfitting Avoidance and Complexity Control
-Summary
6. Similarity, Neighbors, and Clusters
-Similarity and Distance
-Nearest-Neighbor Reasoning
-Some Important Technical Details Relating to Similarities and Neighbors
-Clustering
-Stepping Back: Solving a Business Problem Versus Data Exploration
-Summary
7. Decision Analytic Thinking I: What Is a Good Model?
-Evaluating Classifiers
-Generalizing Beyond Classification
-A Key Analytical Framework: Expected Value
-Evaluation, Baseline Performance, and Implications for Investments in Data
-Summary
8. Visualizing Model Performance
-Ranking Instead of Classifying
-Profit Curves
-ROC Graphs and Curves
-The Area Under the ROC Curve (AUC)
-Cumulative Response and Lift Curves
-Example: churnperformance analytics for modeling performance analytics, for modeling churn Performance Analytics for Churn Modeling
-Summary
9. Evidence and Probabilities
-Example: Targeting Online Consumers With Advertisements
-Combining Evidence Probabilistically
-Applying Bayes' Rule to Data Science
-A Model of Evidence 'Lift'
-Example: Evidence Lifts from Facebook "Likes"
-Summary
10. Representing and Mining Text
-Why Text Is Important
-Why Text Is Difficult
-Representation
-Example: Jazz Musicians
-* The Relationship of IDF to Entropy
-Beyond Bag of Words
-Example: Mining News Stories to Predict Stock Price Movement
-Summary
11. Decision Analytic Thinking II: Toward Analytical Engineering
-Targeting the Best Prospects for a Charity Mailing
-Our Churn Example Revisited with Even More Sophistication
12. Other Data Science Tasks and Techniques
-Co-occurrences and Associations: Finding Items That Go Together
-Profiling: Finding Typical Behavior
-Link Prediction and Social Recommendation
-Data Reduction, Latent Information, and Movie Recommendation
-Bias, Variance, and Ensemble Methods
-Data-Driven Causal Explanation and a Viral Marketing Example
-Summary
13. Data Science and Business Strategy
-Thinking Data-Analytically, Redux
-Achieving Competitive Advantage with Data Science
-Sustaining Competitive Advantage with Data Science
-Attracting and Nurturing Data Scientists and Their Teams
-Examine Data Science Case Studies
-Be Ready to Accept Creative Ideas from Any Source
-Be Ready to Evaluate Proposals for Data Science Projects
-A Firm's Data Science Maturity
14. Conclusion
-The Fundamental Concepts of Data Science
-What Data Can't Do: Humans in the Loop, Revisited
-Privacy, Ethics, and Mining Data About Individuals
-Is There More to Data Science?
-Final Example: From Crowd-Sourcing to Cloud-Sourcing
-Final Words
Appendix A: Proposal Review Guide
Appendix B: Another Sample Proposal
Appendix C: Bibliography
Index
