To minor in Data Science (DS), a student must have a member of this field on the student’s committee. Students can nominate a Data Science field faculty member to their committee through Student Center. Be sure that the field faculty member is listed as a Minor Member for Data Science.

The minor member will record the courses taken in the minor on a form supplied by the field. The completed form will be given to the Chair of the student’s committee, e.g., at the B-exam.

The course requirement is at least 4 courses and at least 14 credit hours. All courses used for satisfying Data Science Minor requirements must be numbered 5000 or higher with at least 6 credit hours numbered 6000 or higher. All courses used towards the minor must be completed with a letter grade of B or better. Courses will be in four areas: computation, statistical modeling and inference, optimization, and ethical, legal, and policy issues. The DGS will maintain a list of courses in each of these areas. The courses that a student uses towards the minor must be in at least two of the four areas, and no more than two courses in any one of the areas can be used towards the minor. A student’s course selection must be approved by the minor committee member.

At most two of the four courses used towards the minor in DS can be courses in the student’s major or courses cross-listed in the major, e.g., a CS major can use at most two CS courses or courses cross-listed as CS courses. At least two courses used for the DS minor must be courses not used for the major or for a second minor.

The initial lists of courses in these areas are below. These lists will be updated periodically by the DGS with the assistance of other field members. Any course listed here that is numbered below 5000 will have an appropriate graduate level course number in the near future.

### Computation

- CS 5777 (Principles of Large-Scale Machine Learning Systems)
- CS 5555 (Health Tech, Data, and Systems)
- CS 5780 (Machine Learning for Intelligent Systems)
- CS 5786 (Machine Learning for Data Science)
- CS 5787 (Deep Learning)
- CS 6210 (Matrix Computations)
- CS 6220 (Data-Sparse Matrix Computations)
- CS 6241 (Numerical Methods for Data Science)
- CS 6741 (Topics in Natural Language Processing and Machine Learning)
- CS 6772 (Bayesian Machine Learning)
- CS 6780 (Advanced Machine Learning)
- CS 6781 (Theoretical Foundations of Machine Learning)
- CS 6783 (Machine Learning Theory)
- CS 6784 (Advanced Topics in Machine Learning)
- CS 6787 (Advanced Machine Learning Systems)

### Statistical modeling and inference

- Any STSCI course numbered 5000 or above except courses numbered 59XX, 69XX, or 79XX.

[Some ORIE courses are cross-listed as STSCI courses. Either version can be used for the Data Science minor.]

- ORIE 5740 (Statistical Data Mining I)
- ORIE 5742 (Information Theory, Probability Modeling, and Deep Learning with Scientific and Financial Applications)
- ORIE 5500 (Engineering Probability and Statistics II)
- ORIE 5550 Applied Time Series Analysis)
- ORIE 5730 (Applied Machine Learning)
- ORIE 5741 (Learning with Big Messy Data)
- ORIE 5750 (Applied Machine Learning)
- ORIE 5270 (Big Data Technologies)
- ORIE 5751 (Inference and Decision Making)
- ORIE 6700 (Statistical Principles)
- ORIE 6746 (Theory of Causal Inference and Decision Making)
- ORIE 6750 (Optimal Learning)
- ORIE 6780 (Bayesian Statistics and Data Analysis)
- ORIE 6741 (Bayesian Machine Learning)
- ORIE 7140 (Theory of Linear Models)
- MATH 7740 (Statistical Learning Theory: Classification, Pattern Recognition, Machine Learning)
- ECE 5412 (Bayesian Estimation and Stochastic Optimization)
- ECE 5420 (Fundamentals of Machine Learning)
- ECE 5620 (Fundamentals of Data Compression)
- ECE 5630 (Information Theory for Data Transmission, Security, and Machine Learning)
- ECE 5960 (Special Topics in Electrical and Computer Engineering: Data Science and Social Networks)
- BME 5310/ECE 5970 (Machine Learning with Biomedical Data
- INFO 5311 (Interactive Information Visualization)
- INFO 5100 (Visual Data Analytics for the Web)
- INFO 5200 (Learning Analytics)
- INFO 5304 (Data Science in the Wild)
- INFO 6350 (Text Mining History and Literature)
- BTRY 6020 (Statistical Methods II)
- BTRY 6381 (Statistical Modeling and Inference)

### Optimization

- ORIE 5300 (Optimization I)
- ORIE 5310 (Optimization II)
- ORIE 5370 (Optimization Modeling in Finance)
- ORIE 5380 (Optimization Methods)
- ORIE 6300 (Mathematical Programming I)
- ORIE 6310 (Mathematical Programming II)
- ORIE 6310 (Integer Programming)
- ORIE 6326 (Convex Optimization)
- ORIE 6751 (Data-Driven Optimization Under Uncertainty: Theory, Methods, and Current Trends)
- INFO 6010 (Computational Methods for Information Science Research)
- AEM 7130 (Dynamic Optimization)

### Ethical, Legal, and Policy Issues

- INFO 5250 (Surveillance and Privacy)
- INFO 5325 (Ethical Thinking about Digital Technologies and Data)
- INFO 6210 (Information, Technology, and Society)
- INFO 6620 (Social Research Design and Method)
- CS 5436/INFO 5303 (Privacy in the Digital Age)