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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 (4 credits)
EARTH 408: Introduction to GIS in the Earth Sciences (3 credits)
ECON 409: Game Theory (4 credits)
EECS 367: Introduction to Autonomous Robotics (4 credits)
EECS 388: Introduction to Computer Security (4 credits)
EECS 442: Computer Vision (4 credits)
EECS 444: Analysis of Societal Networks (4 credits)
EECS 448: Applied Machine Learning for Modeling Human Behavior
EECS 445: Introduction to Machine Learning (4 credits)
EECS 467: Autonomous Robotics (4 credits)
EECS 471: Applied Parallel Programming with GPUs (4 credits)
EECS 476: Data Mining (4 credits)
EECS 477: Introduction to Algorithms (4 credits)
EECS 487: Introduction to Natural Language Processing (4 credits)
EECS 588: Parallel Computing (4 credits)
EECS 482: Operating Systems (4 credits)
EECS 484: Database Management Systems (4 credits)
EECS 485: Web Database and Information Systems (4 credits)
EECS 492: Introduction to Artificial Intelligence (4 credits)
EECS 505: Computational Data Science and Machine Learning (4 credits)
BIOINF 527: Introduction to Bioinformatics & Computational Biology (4 credits)
IOE 310: Introduction to Optimization Methods (3 credits)
IOE 460: Decision Analysis (3 credits)
IOE 465: Design of Experiments (3 credits)
IOE 466: Statistical Quality Control (3 credits)
IOE 474: Simulation (4 credits)
IOE 491, Section 77 only (Winter 2016 only): Constraint Programming
MATH 419: Linear Spaces and Matrix Theory (3 credits)
MATH 420: Advanced Linear Algebra (3 credits)
MATH 451: Advanced Calculus 1 (3 credits)
MATH 471: Introduction to Numerical Methods (3 credits)
MATH 472: Numerical Methods with Financial Applications (3 credits)
ROB 320: Robot Operating Systems (4 credits)
SI 422: Evaluation of Systems and Services (3 credits)
SI 649: Information Visualization (3 credits)
SI 650: Information Retrieval (3 credits)
STATS 403: Introduction to Quantitative Research Methods (4 credits)
STATS 406: Introduction to Statistical Computing (4 credits)
STATS 415: Data Mining (4 credits)
STATS 425: Introduction to Probability (3 credits)
STATS 426: Introduction to Theoretical Statistics (3 credits)
STATS 430: Applied Probability (3 credits)
STATS 449: Topics in Biostatistics
STATS 451: Introduction to Bayesian Data Analysis (3 credits)
STATS 470: Introduction to Design of Experiments (4 credits)
STATS 480: Survey Sampling Techniques (4 credits)
STATS 485: Capstone Seminar (3 credits)
STATS 509: Statistical Models and Methods for Financial Data (3 credits)
STATS 531: Analysis of Time Series (3 credits)
STATS 548: Computations in Probabilistic Modeling in Bioinformatics

Approved data science application electives

ASTRO 361: Astronomical Techniques (4 credits)
BIOINF 527: Introduction to Bioinformatics & Computational Biology (4 credits)
BIOINF 528: Structural Bioinformatics (3 credits)
BIOINF 547: Mathematics of Data (3 credits)
CLIMATE 410: Earth System Modeling (4 credits)
CLIMATE 462: Instrumentation for Atmospheric and Space Sciences
CLIMATE 475: Earth System Interactions (4 credits)
CLIMATE 476: Ocean Dynamics and Climate
CMPLXSYS 510: Introduction to Adaptive Systems (3 credits)
COGSCI 445: Machine Learning for NLP (3 credits)
EARTH 408: Introduction to GIS in the Earth Sciences (or GEOG 506; credit for both will not be granted) (3 credits)
EARTH 414: Weather Systems (3 credits)
ECON 409: Game Theory (4 credits)
ECON 452: Introduction to Econometrics (4 credits)
EEB 315: Ecology and Evolution of Complex Disease (3 credits)
EEB 391: Introduction to Evolution: Quantitative Approach (4 credits)
EEB 408: Modeling for Ecology and Evolutionary Biology (3 credits)
EEB 430: Modeling Infectious Diseases
EECS 367: Introduction to Autonomous Robotics (4 credits)
EECS 442: Computer Vision (4 credits)
EECS 444: Analysis of Societal Networks (4 credits)
EECS 448: Applied Machine Learning for Modeling Human Behavior
EECS 449: Conversational Artificial Intelligence (4 credits)
EECS 467: Autonomous Robotics (4 credits)
EECS 476: Data Mining (4 credits)
EECS 487: Intro to Natural Language Processing (4 credits)
IOE 413: Optimization Modeling in Health Care (3 credits)
IOE 437: Automatic Human Factors (3 credits)
IOE 452: Corporate Finance (3 credits)
MATH 423: Mathematics of Finance (3 credits)
MATH 463: Math Modeling in Biology (3 credits)
MCDB 408: Genomic Biology (3 credits)
POLSCI 490: Game Theory and Formal Models (4 credits)
POLSCI 499: Quantitative Methods of Political Analysis (3 credits)
PSYCH 448: Mathematical Psychology (3 credits)
ROB 320: Robot Operating Systems (4 credits)
SI 365: Cyberscience: Computational Science and the Rise of the Fourth Paradigm (3 credits)
SI 429: eCommunities: Analysis and Design of Online Interaction Environments (3 credits)
SI 554: Consumer Health Informatics (3 credits)
SI 650: Information Retrieval (3 credits)
SI 639: Web Archiving (1.5 credits)
STATS 449: Topics in Biostatistics (3 credits)
STATS 509: Statistical Models and Methods for Financial Data (3 credits)
STATS 545: Data Analysis and Molecular Biology (3 credits)
STATS 547: Probabilistic Modeling in Bioinformatics (3 credits)
STATS 548: Computations in Probabilistic Modeling in Bioinformatics (1 credit)

Approved data science capstone courses

EECS 442: Computer Vision (4 credits)
EECS 448: Applied Machine Learning for Modeling Human Behavior
EECS 449: Conversational Artificial Intelligence (4 credits)
EECS 467: Autonomous Robotics Design Experience (4 credits)
EECS 476: Data Mining (WN2020 and WN2021 only) (4 credits)
EECS 486: Information Retrieval & Web Search (4 credits)
STATS 485: Capstone Seminar (3 credits)
STATS 489: Independent Study, Statistics Subject Area (*Additional approval required, 3-4 credits)
EECS 499: Independent Study, Computer Science Subject Area (*Additional approval required, 3-4 credits)

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 below.

*If a course/independent study 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.

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.

When subjects have been approved at the 300-lvl or higher, departmental credit in that category (301X, 401X, etc.) can be used as FTE credit.

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).