Data science and predictive analytics : biomedical and health applications using R

1. Introduction 2. Basic Visualization and Exploratory Data Analytics 3. Linear Algebra, Matrix Computing, and Regression Modeling 4. Linear and Nonlinear Dimensionality Reduction 5. Supervised Classification 6. Black Box Machine Learning Methods 7. Qualitative Learning Methods-Text Mining, Natural Language Processing, and Apriori Association Rules Learning 8. Unsupervised Clustering 9. Model Performance Assessment, Validation, and Improvement 10. Specialized Machine Learning Topics 11. Variable Importance and Feature Selection 12. Big Longitudinal Data Analysis 13. Function Optimization 14. Deep Learning, Neural Networks

This textbook uses mathematical foundations, computational algorithms, statistical inference techniques, and machine learning to tackle biomedical informatics, health analytics, and decision science challenges. It covers topics such as visualization, linear modeling, supervised classification, black-box machine learning, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The book is suitable for formal didactic instruction and self-learning, covering applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The material is useful to readers, instructors, researchers, engineers, and professionals in various fields. The supporting website offers examples, datasets, functional scripts, electronic notebooks, and additional materials.