فهرست مطالب

• Copyright
• Table of Contents
• Preface
• Chapter 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
• Chapter 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
• Chapter 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
• Chapter 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
• Chapter 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
• Chapter 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
• Chapter 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
• Chapter 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: Performance Analytics for Churn Modeling
• Summary
• Chapter 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
• Chapter 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
• Chapter 11. Decision Analytic Thinking II: Toward Analytical Engineering
• Targeting the Best Prospects for a Charity Mailing
• Our Churn Example Revisited with Even More Sophistication
• Chapter 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
• Chapter 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
• Chapter 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
• Glossary
• Bibliography
• Index
• About the Authors