Combine the School of Data Science’s data-driven curriculum with Darden’s top-ranked MBA program to position yourself as a coveted hire and indispensable leader.
The hybrid program is designed to accommodate students interested in pursuing careers in digital technology-focused industries and lead the digital transformation efforts of traditional industries. You will combine business administration, computer programming, data wrangling, analytics, and data science skills, maximizing your career potential across sectors.
This combined program allows you to obtain both the master's in business administration and the Master of Science in Data Science in two years (24 months) instead of the three years (33 months) that would be required if each were completed separately.
Application Process and Deadlines
To apply for the MSDS/MBA combination degree:
If you are admitted to Darden, then you may apply to the MSDS program between the priority deadline (January 8) and final regular decision deadline (March 22). Decisions will be released on a rolling basis.
Meet current MSDS/MBS Student and Student Ambassador Tae Yoon Kim:
Tae Yoon Kim
Hometown: Seoul, South Korea
Undergrad institution: Rice University
Major: Chemical and Biomolecular Engineering
What has been your favorite class in the program so far?
Practice and Application of Data Science with Professor Jonathan Kropko
What are your research/career interests?
Tech – Product Management
What made you choose UVA for your master's in data science?
I am part of the MBA/MSDS dual-degree program and am also enrolled at the Darden School of Business. The ability to complete both degrees without having to attend an additional year is an attractive proposition to any career-oriented individual.
What is a fun fact about you?
I lived in Hong Kong, Malaysia, Japan, and South Korea while growing up.
Program Format & Curriculum
MSDS/MBA combination degree students take classes for both programs at the same time across 24 months in a hybrid format. Courses begin with 9 credits in-person in the summer semester preceding your first year at Darden. The remaining semesters’ courses may be completed online.
Note: International students must enroll in 12 credits each Fall and Spring; 9 credits must be in-person. International students should carefully consider the program, as it may require enrollment in additional in-person elective courses in order to maintain F-1 visa status.
How Online Courses Work
Our online course content is available asynchronously, with a flipped classroom model. Flipped classrooms are learner-focused environments that allow students to explore course topics independently, outside of class, using asynchronous course content. Each week, students can expect one hour of live synchronous time per course, which will be scheduled in the evenings (EST) during weekdays. Live classroom time serves as a forum for class discussion aimed at deepening student understanding of course material. Students also have the option to attend live faculty office hours and group study sessions throughout the week.
MSDS Curriculum (2 years, 11 graded courses, 32 credit hours)*
*The MSDS curriculum evolves every academic year to keep up with industry standards and therefore is subject to change
Summer 1: Summer Session II & III
- DS 5100: Programming for Data Science (3) – in-person
An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but also learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization. This course is project based, consisting of a semester project and final project presentations. - STAT 6021: Linear Models for Data Science (3) – in-person
An introduction to linear statistical models in the context of data science. Topics include simple, multiple linear regression, logistic regression, and generalized linear models. The primary software is R. Data wrangling in R will also be covered. - DS 6001: Practice and Application of Data Science (3) – in-person
This course covers data science practice, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.
Year 1, Fall Term: 3 credit hours
- CS 5012: Foundations of Computer Science (3) – online
Provide a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency for non-CS, non-CpE students. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security). - Darden: Full-time student
Year 1 Spring: 3 credit hours
The 3 credit hours are taken online (asynchronous content with 1 hour/week synchronous class).
- DS 6030: Statistical Learning (3) – online
This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. Coursework is conducted in the R programming language. - Elective 1: Satisfied with GBUS 7351 Decision Analysis I and GBUS 7352 Decision Analysis II (must earn a B- or better in both)
- Darden: Full-time student
Year 2, Fall: 6 credit hours online (asynchronous content with 1 hour/week synchronous class)
- DS 6040: Bayesian Machine Learning (3) – online
Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference. - DS 6050: Deep Learning (3) – online
A graduate-level course on deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students will complete several substantive programming assignments. - Darden Second Year: Credit hours vary by quarter but will add up to 22.5
Year 2, Spring: 5 credit hours online (asynchronous content with 1 hour/week synchronous class), 3 credit hours in person
- DS 6002: Ethics of Big Data (2) – online
This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter them in their professional lives. - DS 6015: Data Science Capstone Project Work (3) – online
This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects - Elective 2: 5000-level or higher, at least 3 credit hours) – in-person
Possible Electives: in-person (Charlottesville, VA)
- CS 6160: Theory of Computation
- CS 6444: Parallel Computing
- CS 6501: Special Topics in Computer Science
Topics in Computer Science to be approved by the School of Data Science and may include Text Mining, Cloud Computing, Defense Against the Dark Arts, and Vision & Language. - CS 6750: Database Systems
- DS 5001: Exploratory Text Analytics
- DS 5110: Big Data Systems
- ECON 8720: Time Series Econometrics
- ECON 7720: Econometrics II
- EVSC 7070: Advanced Use of Geographical Information Systems
- GCOM 7240: Advanced Quantitative Analysis
- PHS 5705: Recent Advances in Public Health Genomics
- PHS 7310: Clinical Trials Methodology
- PSYC 5720: Fundamentals of Item Response Theory
- PSYC 7760: Introduction to Applied Multivariate Methods
- SARC 5400: Data Visualization
- STAT 6250: Longitudinal Data Analysis
- STAT 6260: Categorical Data Analysis
- SYS 6023: Cognitive Systems Engineering
- SYS 6050: Risk Analysis
- SYS 6582: Selected Topics in Systems Engineering
Topics in Systems Engineering to be approved by the School of Data Science and may include Reinforcement Learning, User Experience Design, and Sensors & Perception. - SYS 7001: System and Decision Sciences
- Other electives are possible, depending on available courses and as approved by the School of Data Science.
Tuition & Fees
We understand graduate school is an investment of your time and resources. Visit Funding Your Graduate Degree for options to help pay for graduate school.
Tuition and fees are the total sum of the M.S. in Data Science and MBA programs for the academic years for which you are enrolled. Tuition and fees for the MSDS program are the same as the 11-month program for summer courses, then follows the Online program’s tuition for the remaining four terms it takes to complete both the MSDS and MBA degrees. A non-refundable $500 admission tuition deposit is required to accept your offer of admission to the MSDS program and is applied toward tuition.
Tuition and fees are set annually by the University of Virginia Board of Visitors in early spring.
Connect with Us
If you are interested in exploring the MSDS/MBA combination degree, we encourage you to connect with us to learn more about the programs, curriculum, and admissions process:
- For the School of Data Science, contact SDS Admissions.
- For the Darden School of Business, contact Merav Frazier.
- For technical questions about the application, contact SDS Technical Support.
