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Data Science and Predictive Analytics

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Data Science and Predictive Analytics: Biomedical and Health Applications using R
AuthorIvo D. Dinov
LanguageEnglish
SubjectComputer science, Data science, artificial intelligence
PublisherSpringer
Publication date
2018
Publication placeSwitzerland
Media typePrint (hardcover)
ISBN978-3-319-72346-4

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. This transdisciplinary graduate-level textbook blends mathematical foundations, computational techniques, and artificial intelligence approaches to showcase contemporary data science applications [1]. Using the statistical computing platform R and a broad range of biomedical case-studies, the 23 chapters of the book provide hands-on examples of importing, exporting, manipulating, modeling, visualizing, and interrogating large, multivariate, incomplete, heterogeneous, longitudinal, and incomplete datasets (Big data) [2].

Structure

The Data Science and Predictive Analytics textbook is divided into the following 23 chapters, each progressively building on the previous content.

  1. Motivation
  2. Managing Data in R
  3. Data Visualization
  4. Linear Algebra & Matrix Computing
  5. Dimensionality Reduction
  6. Lazy Learning: Classification Using Nearest Neighbors
  7. Probabilistic Learning: Classification Using Naive Bayes
  8. Decision Tree Divide and Conquer Classification
  9. Forecasting Numeric Data Using Regression Models
  10. Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines
  11. Apriori Association Rules Learning
  12. k-Means Clustering
  13. Model Performance Assessment
  14. Improving Model Performance
  15. Specialized Machine Learning Topics
  16. Variable/Feature Selection
  17. Regularized Linear Modeling and Controlled Variable Selection
  18. Big Longitudinal Data Analysis
  19. Natural Language Processing/Text Mining
  20. Prediction and Internal Statistical Cross Validation
  21. Function Optimization
  22. Deep Learning, Neural Networks

Reception

The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer reviewed in the International Statistical Review Journal [1] and the Journal of the American Library Association [2].

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 available globally in many college and university libraries [3] and has been used for data science, statistics, and analytics classes at the University of Michigan and UCLA [4].

References

  1. ^ 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.
  2. ^ 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.
  3. ^ Textbook library availability
  4. ^ Courses using the DSPA textbook


Category:Computer science books Category:Statistics books Category:Artificial intelligence Category:Springer Science+Business Media books

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