Data Science and Predictive Analytics
|Author||Ivo D. Dinov|
|Subject||Computer science, Data science, artificial intelligence|
|Media type||Print (hardcover)|
Using the statistical computing platform R and a broad range of biomedical case-studies, the 23 chapters of the book provide explicit examples of importing, exporting, processing, modeling, visualizing, and interpreting large, multivariate, incomplete, heterogeneous, longitudinal, and incomplete datasets ( big data). 
The Data Science and Predictive Analytics textbook is divided into the following 23 chapters, each progressively building on the previous content.
- Foundations of R
- Managing Data in R
- Data Visualization
- Linear Algebra & Matrix Computing
- Dimensionality Reduction
- Lazy Learning: Classification Using Nearest Neighbors
- Probabilistic Learning: Classification Using Naive Bayes
- Decision Tree Divide and Conquer Classification
- Forecasting Numeric Data Using Regression Models
- Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines
- Apriori Association Rules Learning
- k-Means Clustering
- Model Performance Assessment
- Improving Model Performance
- Specialized Machine Learning Topics
- Variable/Feature Selection
- Regularized Linear Modeling and Controlled Variable Selection
- Big Longitudinal Data Analysis
- Natural Language Processing/Text Mining
- Prediction and Internal Statistical Cross Validation
- Function Optimization
- Deep Learning, Neural Networks
The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer reviewed in the International Statistical Institute’s ISI Review Journal  and the Journal of the American Library Association.  Many scholarly publications reference the DSPA textbook.  
As of January 17, 2021, the electronic version of the book ( ISBN 978-3-319-72347-1) is freely available on SpringerLink  and has been downloaded over 6 million times. The textbook is globally available in print and electronic formats in many college and university libraries  and has been used for data science, computational statistics, and analytics classes at various institutions. 
- Dinov, Ivo. Data Science and Predictive Analytics: Biomedical and Health Applications Using R. Springer.
- Capaldi, Mindy. "(Review) Data Science and Predictive Analytics: Biomedical and Health Applications Using R". International Statistical Review. 87 (1). doi: 10.1111/insr.12317.
- Saracco, Benjamin. "Review of Data Science and Predictive Analytics: Biomedical and Health Applications Using R". Journal of the Medical Library Association. 108 (2). doi: 10.5195/jmla.2020.901. S2CID 214729817.
- Dinov, Ivo D. (2018). Data Science and Predictive Analytics. doi: 10.1007/978-3-319-72347-1. ISBN 978-3-319-72346-4. S2CID 52098523.
- Textbook library availability
- Courses using the DSPA textbook