Description
Business analytics is all about leveraging data analysis and analytical modeling methods to achieve business objectives. This is the book for upper division and graduate business students with interest in data science, for data science students with interest in business, and for everyone with interest in both. A comprehensive collection of over 50 methods and cases is presented in an intuitive style, generously illustrated, and backed up by an approachable level of mathematical rigor appropriate to a range of proficiency levels. A robust set of online resources, including software tools, coding examples, datasets, primers, exercise banks, and more for both students and instructors, makes the book the ideal learning resource for aspiring data-savvy business practitioners.
Table of Contents
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.
