Enhance your studies with a focus on data science

The Ph.D minor in Data Science is designed for doctoral students interested in understanding the methods of data gathering and analysis.

Completing the Minor

The Data Science Minor provides students with foundational knowledge across multiple aspects of modern data science, combining technical skills with ethical considerations.

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.

 

view Data Science Faculty 

Students must complete a minimum of 14 credit hours through coursework in multiple core data science areas.

Minimum Requirements:

  • 4+ courses (14+ credit hours total)
  • All courses must be 5000-level or higher
  • At least 6 credit hours at 6000-level or higher
  • Minimum grade of B in all courses

Course Areas (must span at least 2 areas, maximum 2 courses per area):

  1. Computation
  2. Statistical modeling and inference
  3. Optimization
  4. Ethical, legal, and policy issues

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.  

  • 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)

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 5510 (Stochastic Processes for Decision Making)
  • 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)
  • INFO 6751 (Causal Inference in Observational Settings)
  • BTRY 6020 (Statistical Methods II)
  • BTRY 6381 (Statistical Modeling and Inference)
  • ORIE 5300 (Optimization I)
  • ORIE 5310 (Optimization II)
  • ORIE 5370 (Optimization Modeling in Finance)
  • ORIE 5380 (Optimization Methods)
  • ORIE 6170 (Engineering Societal Systems)
  • 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)
  • BTRY 6381 (Statistical Modeling and Inference)
  • 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)
  • STSCI 5600 Integrated Ethics in Data Science
  • SYSEN 6690 Information Design for Strategic Decision-Making