Explore ARCExplore ARC

Data Science and Predictive Analytics Textbook

By | Research

A new textbook Data Science and Predictive Analytics: Biomedical and Health Applications using R provides a solid Data Science foundation and identifies challenges, opportunities, and strategies for designing, collecting, managing, processing, interrogating, analyzing and interpreting complex health and biomedical datasets. It focuses on active-learning by integrating driving motivational challenges with mathematical foundations, computational statistics, and modern scientific inference. The material builds scientific intuition, computational skills, and data-wrangling abilities to tackle Big biomedical and health data problems. The resources include well-documented R-scripts and software recipes implementing atomic data-filters as well as complex end-to-end predictive big data analytics solutions.

The increasing popularity of this textbook, authored by a MIDAS Faculty, Ivo D. Dinov, and part of the Springer Computer Science Series, is evidenced by the number of readers and downloads. Compared to other computer science books, in 2018 this textbook had > 18,000 downloads vs. 14,000 for the average textbook, and for the first quarter of 2019, it had > 4,400 downloads compared to an average of 1,400 downloads. In addition, the book’s website (http://dspa.predictive.space) has 100,000’s of online readers, who have free access to all learning modules, assignments, videos, software tools, and case studies.