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

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

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”

Decision Analytic Thinking II: Toward Analytical Engineering: Targeting the Best Prospects for a Charity Mailing - Our Churn Example Revisited with Even More Sophistication - Assessing the Influence of the Incentive - From an Expected Value Decomposition to a Data Science Solution

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