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. 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 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 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
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.
The topics-flowchart provides
an interactive view of the DSPA content.
Ivo D. Dinov,
This course is designed to build specific data science skills and
predictive analytic competencies.
University of Michigan affiliates can directly register for the course
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: 1240 SNB
Monday and Wednesday 8:30-10:30 AM. Dr. Dinov's Fall'20 Office Hours: Fridays 9:00-10:00 AM (GMT-5)
Life Stream Class Channel »
Archived Videos »
UMich Graduate Credit
To obtain UMich grad credit and get a course grade for completing HS650, 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
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)
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)
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, 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
The DSPA textbook
is available globally at a number of public libraries
and university archives