DSPA_Background.png The Data Science and Predictive Analytics (DSPA) MOOC (offered by the University of Michigan) 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.


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. Trainees are expected to commit substantial amount of time and focus their 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.




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


This is a continuously running MOOC. All learning modules are available on the DSPA Canvas Catalog site.
All prospective learners will need to make accounts, register for DSPA2 MOOC, and use the UMich Canvas Catalog website, see the Enrollment link below.
Learning modules, assignments, datasets, case-studies, audio and video, and other materials are available under each chapter of the DSPA course. Enrollment ยป

DSPA MOOC Course Certification

Course mastery certificates for completion of the entire DSPA MOOC course, or specific parts of it, may be requested by all students that actively participate in the course and complete successfully and timely the appropriate course sections, modules and assignments. Use this link to request DSPA MOOC completion certificates.

UMich Graduate Credit

To obtain UMich grad credit and get a course grade for completing HS650, UMich graduate 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 course as a MOOC. Upon satisfactory completion of the course, they may request course completion certificate, see above, but this certificate does not transfer as UMich graduate 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, and the Graduate Data Science Certificate (GDSC) Program.

Course Management System

Create an account on UMich Canvas Catalog site. Then access the DSPA MOOC: Canvas CMS website, which provides all course management and resources.


The DSPA MOOC is made possible with substantial support from the Statistics Online Computational Resources (SOCR), Michigan Institute for Data Science (MIDAS), the Department of Computational Medicine and Bioinformatics (DCMB), and SPL/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, Kaiming Cheng, Jiachen Xu, Ming Tang, Nina Zhou, Chao Gao, Alex Kalinin, Syed Husain, Brady Zhu, Farshid Sepehrband, Lu Zhao, Sam Hobel, Hanbo Sun, Tuo Wang, 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.
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