Data Science

Master of Science in Data Science

For More Information

Campus address: Welch Hall (WEL) 5.216, phone (512) 232-0693, fax (512) 475-8297, campus mail code: D9800

Mailing address: Graduate Program in Data Science, Department of Statistics and Data Sciences, The University of Texas at Austin, 105 East 24th St. Stop D9800, Austin TX 78712

E-mail: msdatascience@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.

Admission Requirements

To be considered for 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, multivariable calculus and linear algebra. 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 advisor. In most cases, these courses may not be counted toward the degree.

Graduate Studies Committee

The following faculty members served on the Graduate Studies Committee (GSC) in the spring 2022 semester.

Catherine Calder Adam R Klivans Philipp Kraehenbuehl Raymond J Mooney Peter Mueller

C Greg Plaxton Vijaya Ramachandran Purnamrita Sarkar Stephen G Walker Corwin Zigler