Business Analytics

Dr. Richard Huntsinger is an author, professor, expert witness, Silicon Valley entrepreneur, Fortune 500 R&D executive, and management consultant with broad international business and technology experience leading programs in data analytics, process automation, and enterprise software development. He now serves as Faculty Director and Distinguished Teaching Fellow at the University of California, Berkeley, where he lectures and oversees research on data strategy and data science applied to business, law, and public policy.

Executive Overview; 1. Data and Decisions; 1.1 Learning Objectives; 1.2 Introduction; 1.3 Data-to-Decision Process Model; 1.4 Decision Models; 1.5 Sensitivity Analysis; 2. Data Preparation; 2.1 Learning Objectives; 2.2 Data Objects; 2.3 Selection; 2.4 Amalgamation; 2.5 Synthetic Variables; 2.6 Normalization; 2.7 Dummy Variables; 2.8 CASE | High-Tech Stocks; 3. Data Exploration; 3.1 Learning Objectives; 3.2 Descriptive Statistics; 3.3 Similarity; 3.4 Cross-Tabulation; 3.5 Data Visualization; 3.6 Kernel Density Estimation; 3.7 CASE | Fundraising Strategy; 3.8 CASE | Iowa Liquor Sales; 4. Data Transformation; 4.1 Learning Objectives; 4.2 Balance; 4.3 Imputation; 4.4 Alignment; 4.5 Principal Component Analysis; 4.6 CASE | Loan Portfolio; 5. Classification I; 5.1 Learning Objectives; 5.2 Classification Methodology; 5.3 Classifier Evaluation; 5.4 k-Nearest Neighbors; 5.5 Logistic Regression; 5.6 Decision Tree; 5.7 CASE | Loan Portfolio Revisited; 6. Classification II; 6.1 Learning Objectives; 6.2 Naive Bayes; 6.3 Support Vector Machine; 6.4 Neural Network; 6.5 CASE | Telecom Customer Churn; 6.6 CASE | Truck Fleet Maintenance; 7. Classification III; 7.1 Learning Objectives; 7.2 Multinomial Classification; 7.3 CASE | Facial Recognition; 7.4 CASE | Credit Card Fraud; 8. Regression; 8.1 Learning Objectives; 8.2 Regression Methodology; 8.3 Regressor Evaluation; 8.4 Linear Regression; 8.5 Regression Versions; 8.6 CASE | Call Center Scheduling; 9. Ensemble Assembly; 9.1 Learning Objectives; 9.2 Bagging; 9.3 Boosting; 9.4 Stacking; 10. Cluster Analysis; 10.1 Learning Objectives; 10.2 Cluster Analysis Methodology; 10.3 Cluster Model Evaluation; 10.4 k-Means; 10.5 Hierarchical Agglomeration; 10.6 Gaussian Mixture; 10.7 CASE | Fortune 500 Diversity; 10.8 CASE | Music Market Segmentation; 11. Special Data Types; 11.1 Learning Objectives; 11.2 Text Data; 11.3 Time Series Data; 11.4 Network Data; 11.5 PageRank for Network Data; 11.6 Collaborative Filtering for Network Data; 11.7 CASE | Deceptive Hotel Reviews; 11.8 CASE | Targeted Marketing.