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  • Textbook
  • © 2018

Data Science and Predictive Analytics

Biomedical and Health Applications using R

Authors:

  • A novel transdisciplinary treatise of predictive health analytics

  • Complete and self-contained treatment of the theory, experimental modeling, system development, and validation of predictive health analytics

  • Unique collection of case-studies, advanced scientific concepts, lightweight tools, and end-to-end workflow protocols that can be used to learn, practice and apply to new challenges

  • Includes unique interactive content supported by community of 100,000 R-developers

  • Represents a blended STEM-Health Science approach to challenging biomedical problems

  • Support reproducible science, transdisciplinary collaboration, and sharing

Buying options

eBook USD 69.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-72347-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 89.99
Price excludes VAT (USA)
Hardcover Book USD 89.99
Price excludes VAT (USA)

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Table of contents (23 chapters)

  1. Front Matter

    Pages i-xxxiv
  2. Motivation

    • Ivo D. Dinov
    Pages 1-12
  3. Foundations of R

    • Ivo D. Dinov
    Pages 13-62
  4. Managing Data in R

    • Ivo D. Dinov
    Pages 63-141
  5. Data Visualization

    • Ivo D. Dinov
    Pages 143-199
  6. Linear Algebra & Matrix Computing

    • Ivo D. Dinov
    Pages 201-231
  7. Dimensionality Reduction

    • Ivo D. Dinov
    Pages 233-266
  8. Apriori Association Rules Learning

    • Ivo D. Dinov
    Pages 423-442
  9. k-Means Clustering

    • Ivo D. Dinov
    Pages 443-473
  10. Model Performance Assessment

    • Ivo D. Dinov
    Pages 475-496
  11. Improving Model Performance

    • Ivo D. Dinov
    Pages 497-511
  12. Specialized Machine Learning Topics

    • Ivo D. Dinov
    Pages 513-556
  13. Variable/Feature Selection

    • Ivo D. Dinov
    Pages 557-572
  14. Big Longitudinal Data Analysis

    • Ivo D. Dinov
    Pages 623-658

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.


Keywords

  • 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

Reviews

“Data Science and Predictive Analytics is an effective resource for those desiring to extend their knowledge of data science, R or both. The book is comprehensive and serves as a reference guide for data analytics, especially relating to the biomedical, health care and social fields.” (Mindy Capaldi, International Statistical Review, Vol. 87 (1), 2019)

Authors and Affiliations

  • University of Michigan–Ann Arbor, Ann Arbor, USA

    Ivo D. Dinov

About the author

Dr. Ivo Dinov is the Director of the Statistics Online Computational Resource (SOCR) at the University of Michigan and is an expert in mathematical modeling, statistical analysis, high-throughput computational processing and scientific visualization of large datasets (Big Data). His applied research is focused on neuroscience, nursing informatics, multimodal biomedical image analysis, and distributed genomics computing. Examples of specific brain research projects Dr. Dinov is involved in include longitudinal morphometric studies of development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s disease, Parkinson’s disease). He also studies the intricate relations between genetic traits (e.g., SNPs), clinical phenotypes (e.g., disease, behavioral and psychological test) and subject demographics (e.g., race, gender, age) in variety of brain and heart related disorders. Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for science education and active learning.



Bibliographic Information

Buying options

eBook USD 69.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-72347-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 89.99
Price excludes VAT (USA)
Hardcover Book USD 89.99
Price excludes VAT (USA)