./images/HS650_Fall.gif The Data Science and Predictive Analytics (DSPA) course 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. Mathematical concepts, algorithmic ideas, and computational 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 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.


You can view the General DSPA Prerequisites. To ensure students are comfortable in this DSPA course, consider taking the self-assessment (pretest) prior to enrolling in the course.

To summarize, students should have prior experience with college level (undergrad) mathematical modeling, statistical analysis, or programming courses or permission of the instructor. Some MOOCs may be taken as prerequisites, e.g., Corsera, EdX1, EdX2. Additional examples of remediation courses are provided in the self-assessment (pretest).

Course Objectives

Trainees successfully completing the course will:
(1) Gain understanding of the computational foundations of Big Data Science
(2) Develop critical inferential thinking
(3) Gather a tool chest of R libraries for managing and interrogating raw, derived, observed, experimental, and simulated big healthcare datasets
(4) Possess practical skills for handling complex datasets.

Target Audience

This course will be appropriate for trainees who have significant interest in learning data scientific and predictive analytic methods that can commit substantial amount of time to focus an undivided attention to study, practice and interact with other trainees in the course. Review the DSPA Topics to decide in the course coverage is of interest to you.


Class notes, datasets, and learning materials will be provided. This course will cover topics like managing data with R, various Learning Classifiers, model-based and model free forecasting and predictive analytics, evaluation of classification performance, and ensemble methods.

Topics Covered

The following topics will be covered in varying degree of depth. The topics-flowchart provides an interactive view of the DSPA content.




This course is designed to build specific data science skills and predictive analytic competencies.


University of Michigan affiliates can directly register for the course using their UMich credentials and following these RO instructions. Non-affiliated learners and students outside the University of Michigan need to first obtain a UMich friend account (using an outside email) that can then be used to register for the MOOC portion of the DSPA course.
Time/Place: 1000 SNB2, Monday and Wednesday 8:30-10:30 AM. Dr. Dinov's Fall 2023 Office Hours: Fridays 9:00-10:00 AM (GMT-5) SNB2 4126
Life Stream Class Channel (2212) » Archived Videos (available to registered students only) »

UMich Graduate Credit

To obtain UMich graduate credit and get a course grade for completing HS650, students must enroll in HS650, through the registrar's office, and complete all requirements in due time. This option is only available to currently enrolled University of Michigan graduate students.
Other students, fellows, and non-UMich affiliates can enroll in the DSPA course as a MOOC. Upon satisfactory completion of the course, they may request course completion certificate, but this certificate does not transfer as UMich grad credit (Rackham Graduate School rules). Non-UMich trainees may either apply for (1) admission to a Michigan Graduate Degree program, or (2) for admission as a non-candidate for degree (NCFD) to earn credit for graduate-level courses, including this DSPA Course, see the details here.

Canvas/Instructure Course Management System (CMS)

(Fall 2023) DSPA Canvas CMS website provides additional course materials, homeworks, projects, activities, and discussion forums.


The DSPA course is made possible with substantial support from Michigan Institute for Data Science (MIDAS), Statistics Online Computational Resources (SOCR), the Department of Computational Medicine and Bioinformatics (DCMB), and the Department of Health Behavior and Biological Sciences (HBBS/UMSN).
Ideas, scripts, software, code, protocols and documentation from the broad and diverse R statistical computing community have been utilized throughout the DSPA materials.

Many colleagues, students, researchers, and fellows have shared their constructive expertise, valuable time, and critical assessment for generating, validating, and enhancing these open-science resources. Among these are Christopher Aakre, Simeone Marino, Yueyang Shen, Jiachen Xu, Ming Tang, Nina Zhou, Chao Gao, Alex Kalinin, Syed Husain, Brady Zhu, Farshid Sepehrband, Lu Zhao, Sam Hobel, Hanbo Sun, Tuo Wang, Jerome Choi, Yi Zhao, Yuming Sun, Brian Athey, and many others.

Fair Use Licensing

Like all SOCR resources, and to support open-science, these resources (learning materials and source-code) are CC-BY-SA and LGPL licensed.


Dinov, ID. (2018) Data Science and Predictive Analytics: Biomedical and Health Applications using R, Springer (ISBN 978-3-319-72346-4).
Dinov, ID. (2023) Data Science and Predictive Analytics: Biomedical and Health Applications using R, 2nd ed., Springer Series in Applied Machine Learning, ISBN 978-3-031-17482-7.
The DSPA textbook is available globally at a number of public libraries, bookstores, and university archives.
University of Michigan UCLA UC Berkeley Additional (World) Access Links

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