Data Science
Master of Science in Data Science
For More Information
Campus address: Gates Dell Complex (GDC) 7.504, phone (512) 232-0693, fax (512) 475-8297, campus mail code: D9800
Mailing address: The University of Texas at Austin, Graduate Program in Statistics, Department of Statistics and Data Sciences, 2317 Speedway Stop D9800, Austin TX 78712
E-mail: michael.mahometa@austin.utexas.edu
URL: https://ms-datascience.utexas.edu
Facilities for Graduate Work
This degree is 100% online, and uses none of the physical facilities associated with The University of Texas at Austin, nor the departments jointly providing the degree (the Department of Statistics and Data Sciences and the Department of Computer Science). Students in the program will have access to university electronic resources such as library services.
Areas of Study
Graduate degree candidates are expected to develop broad competence in the discipline of Data Science as a whole. The Master of Science in Data Science is a 100% online program, with recommended completion models of one-and-a-half to three years. The program provides advanced training in the theory and methodologies that comprise the field of data science. That training includes, but is not limited to, courses in probability, simulation, data visualization, data mining, data ethics, data analysis, large scale data-based inquiry for big data, non-standard design methodologies, machine learning, deep learning, algorithmic techniques, and optimization. The program integrates some of the following substantive areas of application: biology, computer science, economics, education, engineering, government, neuroscience, and psychology. All courses required for program completion are offered in accordance with University policies that govern non-formula-funded (Option III) programs.1
Admission Requirements
Upon admission to the program, the student should demonstrate a background knowledge of mathematics and statistics equivalent to that acquired in upper-division courses in probability and statistics. Students should have a degree of mathematical maturity and critical thinking skills. Students should also demonstrate a technical acumen in relevant statistical/mathematical software, and experience in computing environments and programming. Deficiencies may be made up by taking courses suggested by the graduate adviser. In most cases, these courses may not be counted toward the degree.1
Graduate Studies Committee
The following faculty members served on the Graduate Studies Committee (GSC) in the spring 2020 semester.
GSC list updated fall 2020 based on spring 2020 appointments. |
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Catherine Calder Adam R Klivans Philipp Kraehenbuehl Raymond J Mooney Peter Mueller |
C Greg Plaxton Vijaya Ramachandran Purnamrita Sarkar Robert A Van De Geijn Corwin Zigler |
| 1 | Added fall 2020.
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