Название: Data Science and Predictive Analytics: Biomedical and Health Applications using R Автор: Ivo D. Dinov
Издательство: Springer
ISBN: 3319723464
Год: 2018
Страниц: 851
Язык: английский
Формат: pdf (true)
Размер: 65.4 MB
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.
This book introduces the foundations of R programming for visualization, statistical computing and scientific inference. Specifically, in this Chapter we will (1) discuss the rationale for selecting R as a computational platform for all DSPA demonstrations; (2) present the basics of installing shell-based R and RStudio user-interface; (3) show some simple R commands and scripts (e.g., translate long-to-wide data format, data simulation, data stratification and subsetting); (4) introduce variable types and their manipulation; (5) demonstrate simple mathematical functions, statistics, and matrix operators; (6) explore simple data visualization; and (7) introduce optimization and model fitting.
Скачать Data Science and Predictive Analytics: Biomedical and Health Applications using R