The Data Science and Predictive Analytics (DSPA) course (offered both, as a traditional University of Michigan class (HS650) and a massive open online course, MOOC) aims to build computational abilities, inferential thinking, and practical skills for tackling core data scientific challenges. It explores foundational concepts in data management as well as artificial intelligence processing, statistical computing, and dynamic scientific visualization. All 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 DSPA graduate course provides a general overview of the fundamental principles, machine learning concepts, artificial intelligence techniques, and 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 decision-making power, associated with our ability to interrogate 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 spirit of open and reproducible science, collaborative design, implementation, sharing and community validation of high-throughput analytic workflows will be emphasized throughout the course.