The Data Science and Predictive Analytics (DSPA) course (offered as a massive open online
course, MOOC, as well as a traditional University of Michigan class) aims to build computational
abilities, inferential thinking, and practical
skills for tackling core data scientific challenges. It explores foundational concepts in
data management, processing, statistical computing, and dynamic visualization using modern
programming tools and agile web-services. Concepts, ideas, and protocols are illustrated
through examples of real observational, simulated and research-derived datasets. Some prior
quantitative experience in programming, calculus, statistics, mathematical models, or linear
algebra will be necessary.
This open graduate course will provide a general overview of the principles, concepts,
techniques, tools and services for managing, harmonizing, aggregating, preprocessing, modeling,
analyzing and interpreting large, multi-source, incomplete, incongruent, and heterogeneous data
(Big Data). The focus will be to expose students to common challenges related to handling
Big Data and present the enormous opportunities and power associated with our ability to
interrogate such complex datasets, extract useful information, derive knowledge, and provide
actionable forecasting. Biomedical, healthcare, and social datasets will provide context
for addressing specific driving challenges. Students will learn about modern data analytic
techniques and develop skills for importing and exporting, cleaning and fusing, modeling
and visualizing, analyzing and synthesizing complex datasets. The collaborative design,
implementation, sharing and community validation of high-throughput analytic workflows
will be emphasized throughout the course.
- Mindy Capaldi (2019)
Data Science and Predictive Analytics: Biomedical and Health Applications Using R ,
Dinov, Ivo D. Springer, 2018, xxxiv + 832 pages, $89.99, hardcover ISBN: 978‐3‐319‐72346,
- Benjamin H. Saracco. (2020)
Review of Data Science and Predictive Analytics: Biomedical and Health Applications Using R,
J Med Libr Assoc. 108(2): 344.
DSPA Wikipedia article.
The DSPA textbook
is available globally at a number of public libraries
and university archives