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 Masters 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

Applicants interested in the MSDS/MBA dual degree should apply to the Darden School in the first three rounds (Early Action, Round One, Round Two). If offered admission by Darden, apply to the MSDS Program by the following deadlines:  

  • Priority Deadline: Jan. 6, 2023 (11:59 pm EST) 

  • Final Deadline: Mar. 20, 2023 (11:59 pm EST) 

Decisions will be made on a rolling basis (applicants will receive a decision within two weeks of their application to the MSDS/MBA program).  

Allow ample time to upload all required documentation to your application prior to the deadline date. Late applications will not be accepted. If you have questions about the application process, contact Admissions Coordinator Degi Betcher

Program Format & Curriculum

MSDS/MBA dual degree students take classes for both programs at the same time across 24 months in a hybrid format.

Summer prior to Darden starts (9 credits, in-person)
Fall 1st Year: 3 credits, online
Spring 1st Year: 3 credits, online; 3 credits, in-person
Fall 2nd Year: 6 credits, online
Spring 2nd Year: 5 credits, online; 3 credits, in-person

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 is evolved 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: 6 credit hours* 
*3 credit hours online (asynchronous content with 1 hour/week synchronous class) and 3 credit hours in person

  • 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 (5000-level or higher, at least 3 credit hours) – in-person
  • 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: Full-time student 

Year 2, Spring: 8 credit hours, online (asynchronous content with 1 hour/week synchronous class)

  • 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, though the cost is distributed across the five terms it takes to complete the MSDS and MBA dual degrees. A non-refundable $400 admission tuition deposit is required to accept your offer of admission to the MSDS program and is applied toward tuition. View Darden's breakdown of tuition and fees for the MBA program.

The following tuition breakdown is for the 2022-2023 academic year. Tuition and fees are set annually by the University of Virginia Board of Visitors in early spring.

Virginia Residents 

Summer 2022 
Tuition + Fees: $10,600 

Fall 2022 
Tuition + Fees: $2,944 

Spring 2023 
Tuition + Fees: $2,944 

Fall 2023 
Tuition and Fees: $8,833 

Spring 2024 
Tuition and Fees: $8,833 

Total Tuition and Fees: $34,154 

Non-Virginia Residents 

Summer 2022 
Tuition + Fees: $14,974 

Fall 2022 
Tuition + Fees: $4,160 

Spring 2023 
Tuition + Fees: $4,160 

Fall 2023 
Tuition and Fees: $12,478 

Spring 2024 
Tuition and Fees: $12,478 

Total Tuition and Fees: $48,250 

Connect with Us

If you are interested in exploring the MSDS/MBA dual degree, we encourage you to connect with us to learn more about the programs, curriculum, and admissions process: 

MSDS Program Director Rafael Alvarado serves as the School of Data Science faculty advisor for the MSDS/MBA dual degree, and Robert Carraway serves as the faculty advisor for the dual degree at the Darden School of Business.