Curriculum & Courses


Curriculum for the Master of Science in Data Science program is designed to offer a balance between foundational statistical theory and application through computer science processes. This is accomplished through courses in statistics with topics such as probability and simulation, regression analysis, data visualization, and with computer science topics such as machine learning, algorithms, and optimization. We believe this balanced program design will provide students with a holistic understanding of Data Science experience. Students will learn not only the “how” but also the “why” of Data Science application. Students will progress through the courses in a weekly released, asynchronous instruction, delivered through the edX platform, created and supervised by UT Austin faculty and staff, with rigorous assessments, projects, and exams.

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At Your Own Pace

Built to provide maximum flexibility, whether you’re a full-time student or a working professional, the online Master’s in Data Science was designed to enable students to further their education on their own terms. Many people will complete the degree within two to three years, but student may take up to six years to complete their program of work.

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Rigorous Courses

Online program students will enjoy the same rigorous training and the same credential as our existing top-ten-ranked graduate program. The resulting degrees will be indistinguishable.

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Foundational Knowledge

Our program has been designed to offer you a balanced understanding of the field of Data Science, by providing foundational statistical knowledge in areas such as probability, simulation, and regression-based models, and then incorporating that knowledge into the applied processes of data science in areas such as machine learning and optimization. A core guide in our course creation process has been providing students with not only the “how” but also the “why” of Data Science application.

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Coursework Overview

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three foundational courses


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seven additional required courses


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10 courses

This is a 30 hour program which will consist of a student completing 10 courses. It is the hope of the program that students begin the program with 9 hours (or 3 courses) of foundational coursework. These foundational courses will include:

To complete the program of work, there are 21 hours ( or 7 courses) of additional required courses. These courses include:


Advanced Predictive Models
Catherine Calder & Purnamrita Sarkar
Data Exploration & Visualization
Claus O. Wilke
Data Structures & Algorithms
Calvin Lin
Deep Learning
Philipp Krähenbühl
Design Principles & Causal Inference
Corwin Zigler
Natural Language Processing
Greg Durrett
Sujay Sanghavi & Constantine Caramanis
Principles of Machine Learning
Adam Klivans & Qiang Liu
Probability & Inference
Mary Parker
Regression & Predictive Modeling
Stephen Walker
Reinforcement Learning
Peter Stone & Scott Niekum

Enrollment Options

Courses are offered by semester and follow The University of Texas at Austin academic calendar. Students may begin courses in the semester they applied for admissions (either the fall or spring semester). Students are required to be enrolled in the long semester, fall and spring semesters, whereas the summer semester is optional.

Students may enroll in the MSDS program on a part-time or full-time basis. For working professionals, we recommend taking one to two courses per semester.

Students are allowed a maximum of six years to complete the MSDS degree.


Advance your career with UT Austin's Master of Data Science online.