Table of contents
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.
- Book Title Data Science and Predictive Analytics
- Book Subtitle Biomedical and Health Applications using R
- DOI https://doi.org/10.1007/978-3-319-72347-1
- Copyright Information Ivo D. Dinov 2018
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-3-319-72346-4
- Softcover ISBN 978-3-030-10187-9
- eBook ISBN 978-3-319-72347-1
- Edition Number 1
- Number of Pages XXXIV, 832
- Number of Illustrations 198 b/w illustrations, 1245 illustrations in colour
Probability and Statistics in Computer Science
Data Mining and Knowledge Discovery
- Buy this book on publisher's site
“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)