Massive Open Online Course (MOOC)
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.,
Additional examples of remediation courses are provided in the
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.
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
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
The following topics will be covered in varying degree of depth.
Ivo D. Dinov,
This course is designed to build specific data science skills and
predictive analytic competencies.
This is a continuously running MOOC.
University of Michigan affiliates can directly register for the course using their
and the Enrollment
Non-affiliated learners and students outside the University of Michigan
need to first obtain
a UMich friend account
(using an outside email)
this registration form
to be added to the DSPA course.
Learning modules, assignments, datasets, case-studies,
audio and video
materials are available under each chapter of the DSPA course
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
, students must enroll
in HS650, through the
, 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 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
The DSPA MOOC 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)
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, 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