Home > Academics > Undergraduate Programs & Admissions > Data Science (College of Engineering) > DS-Eng Electives and Capstone Courses

Electives and capstone courses for DS-Eng

Electives and capstone courses

As indicated in the DS-Eng Program Guide, the Data Science program requires elective and capstone courses. Current courses in these categories are listed here:

Data Science Advanced Technical Electives
Data Science Application Electives
Data Science Capstone Courses
Data Science Flexible Technical Electives

Note: There is overlap between the lists of approved Advanced Technical Electives, Application Electives, and Capstone courses. Students may not double-count a course in multiple categories. Additionally, students should consult with online course guides and the departments offering the following courses for questions regarding course availability and eligibility for enrollment.

Approved data science advanced technical electives

BIOINF 527: Introduction to Bioinformatics & Computational Biology
IOE 310: Introduction to Optimization Methods
EARTH 408: Introduction to GIS in the Earth Sciences (or GEOG 506; credit for both will not be granted)
ECON 409: Game Theory
EECS 388: Introduction to Computer Security
EECS 442: Computer Vision
EECS 445: Introduction to Machine Learning
EECS 467: Autonomous Robotics
EECS 477: Introduction to Algorithms
EECS 588: Parallel Computing
EECS 482: Operating Systems
EECS 484: Database Management Systems
EECS 485: Web Database and Information Systems
EECS 492: Introduction to Artificial Intelligence
EECS 505: Computational Data Science and Machine Learning
IOE 460: Decision Analysis
IOE 465: Design of Experiments
IOE 466: Statistical Quality Control
IOE 474: Simulation
IOE 491, Section 77 only (Winter 2016 only): Constraint Programming
MATH 419: Linear Spaces and Matrix Theory
MATH 451: Advanced Calculus 1
MATH 471: Introduction to Numerical Methods
SI 422: Evaluation of Systems and Services
SI 649: Information Visualization
SI 650: Information Retrieval
STATS 403: Introduction to Quantitative Research Methods
STATS 406: Introduction to Statistical Computing
STATS 415: Data Mining
STATS 425: Introduction to Probability
STATS 426: Introduction to Theoretical Statistics
STATS 430: Applied Probability
STATS 449: Topics in Biostatistics
STATS 451: Introduction to Bayesian Data Analysis
STATS 470: Introduction to Design of Experiments
STATS 480: Survey Sampling Techniques
STATS 485: Capstone Seminar
STATS 509: Statistical Models and Methods for Financial Data
STATS 531: Analysis of Time Series
STATS 548: Computations in Probabilistic Modeling in Bioinformatics

Approved data science application electives

AOSS 420: Environmental Ocean Dynamics
AOSS 477: Space Weather Modeling
ASTRO 361: Astronomical Techniques
BIOINF 527: Introduction to Bioinformatics & Computational Biology
BIOINF 551: Proteome Informatics
CLIMATE 410: Earth System Modeling
CLIMATE 462: Instrumentation for Atmospheric and Space Sciences
CLIMATE 475: Earth System Interactions
CLIMATE 476: Ocean Dynamics and Climate
CMPLXSYS 510: Introduction to Adaptive Systems
EARTH 408: Introduction to GIS in the Earth Sciences (or GEOG 506; credit for both will not be granted)
EARTH 414: Weather Systems
ECON 409: Game Theory
ECON 452: Introduction to Econometrics
EEB 315: Ecology and Evolution of Complex Disease
EEB 391: Introduction to Evolution: Quantitative Approach
EEB 408: Modeling for Ecology and Evolutionary Biology
EEB 416: Introduction to Bioinformatics
EEB 430: Modeling Infectious Diseases
EEB 466: Mathematical Ecology
EEB 481: Population Dynamics and Ecology
EECS 442: Computer Vision
EECS 467: Autonomous Robotics
IOE 413: Optimization Modeling in Health Care
IOE 437: Automatic Human Factors
MATH 422: Risk Management and Insurance
MATH 423: Mathematics of Finance
MATH 463: Math Modeling in Biology
MCDB 408: Genomic Biology
POLSCI 490: Game Theory and Formal Models
POLSCI 499: Quantitative Methods of Political Analysis
PSYCH 448: Mathematical Psychology
SI 365: Cyberscience: Computational Science and the Rise of the Fourth Paradigm
SI 429: eCommunities: Analysis and Design of Online Interaction Environments
SI 542: Introduction to Health Informatics
SI 554: Consumer Health Informatics
SI 639: Web Archiving
SI 650: Information Retrieval
STATS 449: Topics in Biostatistics
STATS 509: Statistical Models and Methods for Financial Data
STATS 545: Data Analysis in Molecular Biology
STATS 547: Probabilistic Modeling in Bioinformatics
STATS 548: Computations in Probabilistic Modeling in Bioinformatics

Approved data science capstone courses

If a course project has substantial data science components, students may seek approval to use the course as the Capstone. A project that is focused on software development is likely insufficient unless there is also substantial data analysis or work on other data issues. Students should seek a preliminary read on the suitability of the course by submitting a project proposal to dsengadvisor@umich.edu at the beginning of the course. Students must submit a final project report and a cover letter at the end of the course to dsengadvisor@umich.edu, pointing out the main data science features of the project. This approval process may be used for established courses as well as independent study courses. Please note that no project is guaranteed approval.

EECS 486: Information Retrieval & Web Search
STATS 485: Capstone Seminar
STATS 489: Independent Study, Statistics Subject Area
EECS 499: Independent Study, Computer Science Subject Area

Some EECS special topics courses (398, 498, 598) are approved on a term-by-term basis for use as the DS Capstone. See this page for current offerings and approvals.

Multidisciplinary Design Program courses with projects directly related to Data Science may be approved for use as a DS Capstone by the DS-Eng Chief Program Advisor. See the MDP homepage for more information about the program. Some MDP projects are pre-approved for use as the DS Capstone on a term-by-term basis. For those not pre-approved, students should follow the approval process instructions above.

Approved data science flexible technical electives

Courses approved as advanced technical electives are automatically approved for use as flexible DS technical electives (however, as noted above, cannot be double-counted between categories). See the Approved Data Science Advanced Technical Electives. Additional approved DS Flexible Technical Electives courses are listed below.

Directed Study Rule: Only 4 hours of directed/independent study or research courses (total across all departments, i.e. EECS, IOE, Civil, etc.) can count toward flexible tech electives. EECS 499 is only open to seniors; sophomores and juniors should consider EECS 399 (can count toward flexible tech electives only if enrolled in FA14 or later).

Physics

Any PHYSICS course at the 300-level or higher (except 333, 334, 420, and 481). [PHYS 496, 497, 498, 499: see Directed Study Rule above.]

School of Information

SI 301: Models of Social Information Processing
SI 364: Building Interactive Applications
SI 422: Evaluation of Systems and Services

Statistics

Any statistics class at 500-level or 600-level except for seminar and independent study courses.

Academics

Electives and capstone courses for DS-Eng