Business analytics is about leveraging data analysis and analytical modeling methods to achieve business objectives. Suitable for senior and graduate students in business or data science, this innovative text presents methods in an intuitive fashion and backs up business applications with an approachable level of mathematical rigor.
Business analytics is about leveraging data analysis and analytical modeling methods to achieve business objectives. Suitable for senior and graduate students in business or data science, this innovative text presents methods in an intuitive fashion and backs up business applications with an approachable level of mathematical rigor.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
