Data Science and Predictive Analytics
| File:Data Science and Predictive Analytics (book cover).png | |
| Author | Ivo D. Dinov |
|---|---|
| Language | English |
| Subject | Computer science, Data science, artificial intelligence |
| Publisher | Springer |
Publication date | 2018 |
| Publication place | Switzerland |
| Media type | Print (hardcover) |
| ISBN | 978-3-319-72346-4 |
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The textbook Data Science and Predictive Analytics: Biomedical and Health Applications using R, authored by Ivo D. Dinov, was published in August 2018 by Springer.[1]
This textbook covers some of the mathematical foundations, computational techniques, and artificial intelligence approaches used in data science research and applications.[2]
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).[3]
Structure
The Data Science and Predictive Analytics textbook is divided into the following 23 chapters, each progressively building on the previous content.
- Motivation
- 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
Reception
The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer reviewed in the International Statistical Institute’s ISI Review Journal[2] and the Journal of the American Library Association.[3] Many scholarly publications reference the DSPA textbook.[4][5]
As of January 17, 2021, the electronic version of the book (ISBN 978-3-319-72347-1) is freely available on SpringerLink[6] and has been downloaded over 6 million times. The textbook is globally available in print and electronic formats in many college and university libraries[7] and has been used for data science, computational statistics, and analytics classes at various institutions.[8]
References
- ^ Dinov, Ivo. Data Science and Predictive Analytics: Biomedical and Health Applications Using R. Springer.
- ^ a b Capaldi, Mindy. "(Review) Data Science and Predictive Analytics: Biomedical and Health Applications Using R". International Statistical Review. 87 (1). doi:10.1111/insr.12317.
- ^ a b Saracco, Benjamin. "Review of Data Science and Predictive Analytics: Biomedical and Health Applications Using R". Journal of the American Library Association. 108 (2). doi:10.5195/jmla.2020.901.
- ^ https://www.altmetric.com/details/36035686/citations
- ^ https://scholar.google.com/scholar?oi=bibs&hl=en&cites=10523091112419095119
- ^ https://link.springer.com/book/10.1007%2F978-3-319-72347-1
- ^ Textbook library availability
- ^ Courses using the DSPA textbook