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” - Data and Data Science Capability as a Strategic Asset - Data-Analytic Thinking From Business Problems to Data Mining Tasks - Supervised Versus Unsupervised Methods -Data Mining and Its Results - The Data Mining Process - Other Analytics Techniques and Technologies

From Correlation to Supervised Segmentation- Models, Induction, and Prediction - Supervised Segmentation: Selecting Informative Attributes- Supervised Segmentation with Tree-Structured Models - -Visualizing Segmentations - Trees as Sets of Rules - Example: Addressing the Churn Problem with Tree Induction Classification via Mathematical Functions - Linear Discriminant Functions - An Example of Mining a Linear Discriminant from Data - Support Vector Machines, Briefly - Nonlinear Functions, Support Vector Machines, and Neural Networks

Generalization – Overfitting -Overfitting Examined -Holdout Data and Fitting Graphs - Example: Overfitting Linear Functions - Example: Why Is Overfitting Bad? - From Holdout Evaluation to Cross-Validation - Overfitting Avoidance and Complexity Control - A General Method for Avoiding Overfitting Similarity and Distance - Nearest-Neighbor Reasoning -- Nearest Neighbors for Predictive Modeling - Clustering - Hierarchical Clustering - Nearest Neighbors Revisited: Clustering Around Centroids -Example: Clustering Business News Stories - Understanding the Results of Clustering

Evaluating Classifiers - Plain Accuracy and Its Problems - The Confusion Matrix - Problems with Unbalanced Classes - A Key Analytical Framework: Expected Value - Using Expected Value to Frame Classifier Use - Using Expected Value to Frame Classifier Ranking Instead of Classifying - Profit Curves - ROC Graphs and Curves - The Area Under the ROC Curve (AUC) - Cumulative Response and Lift Curves

Toward Analytical Engineering - Targeting the Best Prospects for a Charity Mailing - The Expected Value Framework: Decomposing the Business Problem and Recomposing the Solution Pieces - A Brief Digression on Selection Bias - Our Churn Example Revisited with Even More Sophistication - The Expected Value Framework: Structuring a More Complicated Business Problem - Assessing the Influence of the Incentive - From an Expected Value Decomposition to a Data Science Solution