HS 650 001 FA 2019
HS 650 Fall 2019
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 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.
Prerequisites
You can view the General DSPA Prerequisites Links to an external site.. To ensure students are comfortable in this DSPA course, consider taking the self-assessment (pretest) Links to an external site. 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 Links to an external site., EdX1 Links to an external site., EdX2 Links to an external site.. Additional examples of remediation courses are provided in the self-assessment (pretest) Links to an external site..
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 Links to an external site. to decide in the course coverage is of interest to you.
Notes
Class notes, datasets, and learning materials will be provided Links to an external site.. 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 Links to an external site..
Instructor
Ivo D. Dinov Links to an external site., SOCR Links to an external site., MIDAS Links to an external site., HBBS/UMSN Links to an external site., DCMB/UMMS Links to an external site..
Competencies
Logistics
University of Michigan affiliates can directly register for the course Links to an external site. using their UMich credentials Links to an external site. and the Enrollment link below. Non-affiliated learners and students outside the University of Michigan need to first obtain a UMich friend account Links to an external site. (using an outside email) that can then be used to register for the MOOC version of the DSPA course Links to an external site..Time/Place: 1240 SNB Links to an external site., Monday and Wednesday 8:30-10:30 AM. Dr. Dinov's Fall'19 OHs: Thursdays 9:30-10:20 AM SNB 4126 Links to an external site.
Archived Videos » Links to an external site.
UMich Graduate Credit
To obtain UMich grad credit and get a course grade for completing HS650, students must enroll in HS650, through the registrar's office Links to an external site., 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 Links to an external site.. Upon satisfactory completion of the course, they may request course completion certificate Links to an external site., see above, but this certificate does not transfer as UMich grad credit (Rackham Graduate School rules Links to an external site.). 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 Links to an external site..
Course Management System
DSPA Canvas CMS website provides additional course materials and discussion forums.Acknowledgments
The DSPA course is made possible with substantial support from Michigan Institute for Data Science (MIDAS) Links to an external site., Statistics Online Computational Resources (SOCR) Links to an external site., the Department of Computational Medicine and Bioinformatics (DCMB) Links to an external site., and the Department of Health Behavior and Biological Sciences (HBBS/UMSN) Links to an external site..Ideas, scripts, software, code, protocols and documentation from the broad and diverse R statistical computing community Links to an external site. 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 Links to an external site., and to support open-science, these resources (learning materials and source-code) are CC-BY-SA Links to an external site. and LGPL Links to an external site. licensed.Textbook
Dinov, ID. (2018) Data Science and Predictive Analytics: Biomedical and Health Applications using R Links to an external site., Springer (ISBN 978-3-319-72346-4).
Course Summary:
| Date | Details | Due |
|---|---|---|
| Fri Sep 20, 2019 | Assignment HW1 | due by 11:59pm |
| Fri Oct 4, 2019 | Assignment HW2 | due by 11:59pm |
| Fri Oct 18, 2019 | Assignment HW3 | due by 11:59pm |
| Fri Nov 1, 2019 | Assignment HW4 | due by 11:59pm |
| Fri Nov 15, 2019 | Assignment HW5 | due by 11:59pm |
| Fri Dec 6, 2019 | Assignment HW6 | due by 11:59pm |
| Wed Dec 11, 2019 | Assignment Central Campus Fall 2019 Evaluation | due by 11:59pm |
| Fri Dec 13, 2019 | Assignment Term Paper | due by 11pm |
| Assignment General Participation |
This course content is offered under a CC Attribution Share Alike
Links to an external site. license. Content in this course can be considered under this license unless otherwise noted.