Business Analytics

Erscheinungsjahr 2025
ISBN: 978-1-316-51215-9
Verlag: Cambridge University Press

Buch, Englisch, 700 Seiten, Format (B × H): 208 mm x 260 mm, Gewicht: 1694 g

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.

Autoren/Hrsg.

Huntsinger, Richard

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Management Unternehmensführung
• Wirtschaftswissenschaften Betriebswirtschaft Management Unternehmensorganisation & Entwicklungsstrategien

Inhaltsverzeichnis

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.

Über Autor(innen)

Huntsinger, Richard
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.