Data Science and Predictive Analytics

Biomedical and Health Applications using R

  • Ivo D. Dinov

Table of contents

  1. Front Matter
    Pages i-xxxiv
  2. Ivo D. Dinov
    Pages 1-12
  3. Ivo D. Dinov
    Pages 13-62
  4. Ivo D. Dinov
    Pages 63-141
  5. Ivo D. Dinov
    Pages 143-199
  6. Ivo D. Dinov
    Pages 201-231
  7. Ivo D. Dinov
    Pages 233-266
  8. Ivo D. Dinov
    Pages 423-442
  9. Ivo D. Dinov
    Pages 443-473
  10. Ivo D. Dinov
    Pages 475-496
  11. Ivo D. Dinov
    Pages 497-511
  12. Ivo D. Dinov
    Pages 513-556
  13. Ivo D. Dinov
    Pages 557-572
  14. Ivo D. Dinov
    Pages 623-658
  15. Ivo D. Dinov
    Pages 659-695
  16. Ivo D. Dinov
    Pages 735-763
  17. Ivo D. Dinov
    Pages 765-817
  18. Back Matter
    Pages 819-832

About this book


Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic training environments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pillars for the urgently needed bridge to close that supply and demand predictive analytic skills gap.

Exposing the enormous opportunities presented by the tsunami of Big data, this textbook aims to identify specific knowledge gaps, educational barriers, and workforce readiness deficiencies. Specifically, it focuses on the development of a transdisciplinary curriculum integrating modern computational methods, advanced data science techniques, innovative biomedical applications, and impactful health analytics.

The content of this graduate-level textbook fills a substantial gap in integrating modern engineering concepts, computational algorithms, mathematical optimization, statistical computing and biomedical inference. Big data analytic techniques and predictive scientific methods demand broad transdisciplinary knowledge, appeal to an extremely wide spectrum of readers/learners, and provide incredible opportunities for engagement throughout the academy, industry, regulatory and funding agencies.


big data R statistical computing predictive analytics data science health analytics machine learning statistical learning in R hands-on machine learning Big Data methods data management streaming visualization neural networks controlled variable selection text mining natural language processing cross-validation deep learning

Authors and affiliations

  • Ivo D. Dinov
    • 1
  1. 1.University of Michigan–Ann ArborAnn ArborUSA

Bibliographic information