Data Science and
Predictive Analytics (UMich HS650)
2nd Edition to 1st Edition Chapter Mapping
Second to First Edition Chapter Mapping
| Second Edition Chapters | MAPPING | First Edition Chapters |
|---|---|---|
| 1. Introduction | ← | 1. Motivation + 2. Foundations of R |
| 2. Basic Visualization and Exploratory Data Analytics | ← | 3. Managing Data in R + 4. Data Visualization |
| 3. Linear Algebra, Matrix Computing & Regression Modeling | ← | 5. Linear Algebra & Matrix Computing + 10. Forecasting Numeric Data Using Regression Models |
| 4. Linear and Nonlinear Dimensionality Reduction (PCA, ICA, t-SNE, UMAP) | ← | 6. Dimensionality Reduction |
| 5. Supervised Classification | ← | 7. Lazy Learning: Classification Using Nearest Neighbors + 8. Probabilistic Learning: Classification Using Naive Bayes + 9. Decision Tree Divide and Conquer Classification |
| 6. Black Box Machine-Learning Methods: Neural Networks, Support Vector Machines, Random Forests | ← | 11. Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines |
| 7. Qualitative Learning Methods: Natural Language Processing, Text Mining, and Apriori Association Rule | ← | 12. Apriori Association Rules Learning + 20. Natural Language Processing/Text Mining |
| 8. Unsupervised Clustering - k-Means, spectral, Gaussian Mixture Modeling | ← | 13. k-Means Clustering |
| 9. Model Performance Assessment, Validation & Improvement | ← | 14. Model Performance Assessment + 15. Improving Model Performance + 21. Prediction and Internal Statistical Cross Validation |
| 10. Specialized Machine Learning Topics | ← | 16. Specialized Machine Learning Topics |
| 11. Variable Importance & Feature Selection | ← | 17. Variable/Feature Selection + 18. Regularized Linear Modeling and Controlled Variable Selection |
| 12. Big Longitudinal Data Analysis - classical and neural network approaches | ← | 19. Big Longitudinal Data Analysis |
| 13. Function Optimization | ← | 22. Function Optimization |
| 14. Deep Learning, Neural Networks | ← | 23. Deep Learning, Neural Networks |
First to Second Edition Chapter Mapping
| First Edition Chapters | MAPPING | Second Edition Chapters |
|---|---|---|
| 1. Motivation | → | 1. Introduction |
| 2. Foundations of R | → | 1. Introduction |
| 3. Managing Data in R | → | 2. Basic Visualization and Exploratory Data Analytics |
| 4. Data Visualization | → | 2. Basic Visualization and Exploratory Data Analytics |
| 5. Linear Algebra & Matrix Computing | → | 3. Linear Algebra, Matrix Computing & Regression Modeling |
| 6. Dimensionality Reduction | → | 4. Linear and Nonlinear Dimensionality Reduction (PCA, ICA, t-SNE, UMAP) |
| 7. Lazy Learning: Classification Using Nearest Neighbors | → | 5. Supervised Classification |
| 8. Probabilistic Learning: Classification Using Naive Bayes | → | 5. Supervised Classification |
| 9. Decision Tree Divide and Conquer Classification | → | 5. Supervised Classification |
| 10. Forecasting Numeric Data Using Regression Models | → | 3. Linear Algebra, Matrix Computing & Regression Modeling |
| 11. Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines | → | 6. Black Box Machine-Learning Methods: Neural Networks, Support Vector Machines, Random Forests |
| 12. Apriori Association Rules Learning | → | 7. Qualitative Learning Methods: Natural Language Processing, Text Mining, and Apriori Association Rule |
| 13. k-Means Clustering | → | 8. Unsupervised Clustering - k-Means, spectral, Gaussian Mixture Modeling |
| 14. Model Performance Assessment | → | 9. Model Performance Assessment, Validation & Improvement |
| 15. Improving Model Performance | → | 9. Model Performance Assessment & Improvement |
| 16. Specialized Machine Learning Topics | → | 10. Specialized Machine Learning Topics |
| 17. Variable/Feature Selection | → | 11. Variable Importance & Feature Selection |
| 18. Regularized Linear Modeling and Controlled Variable Selection | → | 11. Variable Importance & Feature Selection |
| 19. Big Longitudinal Data Analysis | → | 12. Big Longitudinal Data Analysis - classical and neural network approaches |
| 20. Natural Language Processing/Text Mining | → | 7. Qualitative Learning Methods: Natural Language Processing, Text Mining, and Apriori Association Rule |
| 21. Prediction and Internal Statistical Cross Validation | → | 9. Model Performance Assessment, Validation & Improvement |
| 22. Function Optimization | → | 13. Function Optimization |
| 23. Deep Learning, Neural Networks | → | 14. Deep Learning, Neural Networks |
References
- Dinov, ID. (2018) Data Science and Predictive Analytics: Biomedical and Health Applications using R, Springer (ISBN 978-3-319-72346-4).
- Dinov, ID. (2023) Data Science and Predictive Analytics: Biomedical and Health Applications using R, 2nd edition, Springer Series in Applied Machine Learning (978-3-031-17482-7).