Academics

12 credits

• DS 6001 - Data Engineering I: Data Pipeline Architecture (3 credits)
  • This course covers the practice of 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.
• DS 5030 - Understanding Uncertainty (3 credits)
  • Provides an in-depth exploration of probabilistic and statistical methods used to understand, quantify, and manage uncertainty. Learn foundational concepts in probability and statistics, simulation techniques, and modern approaches to parameter estimation, decision theory, and hypothesis testing. Topics include parametric and nonparametric methods, Bayesian and frequentist paradigms, and applications of uncertainty in real-world problems.
• DS 5012 - Computation for Data Science (3 credits)
  • Provides a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).
• DS 6021 - Machine Learning I: Introduction to Predictive Modeling (3 credits)
  • Comprehensive introduction to predictive modeling, a cornerstone of data science and machine learning. Learn the fundamental concepts, techniques, and tools used to build models while emphasizing both theoretical understanding and practical applications. The topics we will cover include an in-depth analysis of linear models and different variants, their extension to generalized linear models, and an introduction to nonparametric regression.

12 credits

• DS 6002 - Ethics of Big Data (3 credits)
  • 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 6030 - Machine Learning II: Data Mining and Statistical Learning (3 credits)
  • 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.
• DS 6050 - Machine Learning III: Deep Learning (3 credits)
  • 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. A course covering statistical techniques such as regression.
• Restricted Elective (3 credits)
• Capstone Program Project Preparation (non-credit workshop) 

Summer Term

9 credits

• DS 6015 - Data Science Capstone (3 credits)
  • Designed for capstone project teams to meet in groups with advisors and clients to advance work on their projects.
• DS 5110 - Data Engineering II:  Big Data Systems (3 credits)
  • Trends in hardware and software for Big Data Systems and applications. Cover principles driving data infrastructures, which enabled the training of AI models on datasets (speech, sounds, images, video, languages) and may extend to structured data (text, images, time series). AI and machine learning practitioners build and deploy data science projects on Amazon Web Services unifying data science, data engineering, and application development.
• Restricted Elective (3 credits)

Graduate in August 

OR

Participate in a pre-approved qualified internship over the Summer term, and return for the Fall term

Fall Term

• DS 5110 - Data Engineering II:  Big Data Systems (3 credits)
  • Trends in hardware and software for Big Data Systems and applications. Cover principles driving data infrastructures, which enabled the training of AI models on datasets (speech, sounds, images, video, languages) and may extend to structured data (text, images, time series). AI and machine learning practitioners build and deploy data science projects on Amazon Web Services unifying data science, data engineering, and application development.
• Restricted Elective (3 credits)
• Restricted Elective (3 credits)

Graduate in December 

Students select their elective courses in consultation with the Program Director. A variety of electives are available, including but not limited to those listed in the Graduate Record. Students are required to take a minimum of 6 total credit hours of elective courses. Elective courses must be at the 5000 level or higher to count toward the MSDS program unless otherwise preapproved. Examples of electives:

• CS 6501: Special Topics in Computer Science (e.g.: Text Mining, Cloud Computing, Defense Against the Dark Arts, Vision & Language)
• DS 5001: Text as Data
• DS 6040: Bayesian Machine Learning
• DS 6051 Decoding Large Language Models
• ECON 8720: Time Series Econometrics
• ECON 7720: Econometrics II
• EVSC 5020: Introduction to Geographic Information Systems (GIS)
• EVSC 7070: Advanced Use of Geographical Information Systems
• SARC 5400: Data Visualization
• STAT 5170: Applied Time Series
• STAT 5310: Clinical Trials Methodology
• STAT 6260: Categorical Data Analysis
• SYS 5581: Selected Topics in Systems Engineering
• SYS 6050: Risk Analysis
• SYS 6582: Selected Topics in Systems Engineering (e.g.: Reinforcement Learning, User Experience Design, Sensors & Perception)

Availability of electives varies by year, and courses must be approved by the School of Data Science. Students interested in taking more than 6 credit hours of electives will need submit a petition for approval.

These optional concentrations will align electives and capstone projects with industry roles or applied research areas, signaling to future employers that you are ready to enter the field. The Genomics and Sports Data Science concentrations have limited capacity and require application through a short answer question. Those selected will be notified in their offer letters. The AI and Data Visualization concentrations have unlimited seats, do not require an application, and must be declared by the end of your first term in the program. 

Artificial Intelligence

According to the World Economic Forum's Future of Jobs Report 2025, AI is the single fastest growing desired skill, with 86% of surveyed employers anticipating that AI and related technologies will be the drivers of transformation in their industries. Moreover, AI technologies have doubled across all industries within the last decade, with AI Engineer and AI Consultant taking the #1 and #2 spots on LinkedIn’s 2025 Jobs on the Rise. 

The Artificial Intelligence (AI) concentration equips students with a deep understanding of modern AI methods, from natural language processing and large language models to ethical and responsible deployment of AI systems. This concentration builds on the core foundational data science courses in the curriculum - Machine Learning I, II, and III. Through electives such as Text as Data and Introduction to Large Language Models, and a capstone focused on real-world AI applications, students gain both the theoretical grounding and practical skills necessary to design, evaluate, and apply intelligent systems across diverse domains.

The AI concentration is open to all students in the residential M.S. in Data Science program and may be declared at the end of the first term. Required concentration courses include DS 5001 Text as Data (3 credits) and DS 6051 Decoding Large Language Models (3 credits). DS 6015 Capstone will focus on a project requiring robust artificial intelligence tools and techniques. Working in interdisciplinary teams, they will integrate skills from the program to design, implement, and present an applied project for a client.

Data Visualization

The Data Visualization concentration prepares students to transform complex data into clear, meaningful, and aesthetically powerful visual stories. Building on the core data science courses, students in the Data Visualization concentration will explore advanced techniques in visual design, human-centered data interaction, and network representation of data. The concentration emphasizes both the art and science of visual communication, enabling students to create interactive, web-enabled visualizations that drive understanding and insight for audiences across different domains.

Roles such as UI and UX Designers are projected to grow +45% over the next five years, with in-demand core skills sought by employers, such as creativity (57%), design and user-experience (25%); both of which are expected to increase by 2030 to 66% and 45%, respectively. Aligned coursework in data visualization will prepare graduates to be pioneers in this growing field. 

The Data Visualization concentration is open to all students in the residential M.S. in Data Science program and may be declared at the end of the first term. Required concentration courses include taking two electives in human-centered design, data visualization, and/or interactive applications. DS 6015 Capstone will focus on a project requiring robust data visualization tools and techniques. Working in interdisciplinary teams, they will integrate skills from the program to design, implement, and present an applied project for a client.

Genomics

Initiated by grant funding from the NIH’s National Human Genome Research Institute, the SDS and the Center for Public Health Genomics will collaborate to establish REGARDS (Research Experiences in Genomics Applications of Responsible Data Science). Students who pursue Genomics will acquire vital genomics content alongside their data science curriculum with an emphasis on responsible data science and research ethics crucial to the training of the biomedical and informatics workforce. 

The concentration is competitive entry and must be applied for during the application process; refer to the Admissions page for more information. Those admitted to the Genomics Concentration will be notified in their offer letter. Required Genomics concentration courses will be selected with the aid of the Program Director, to include foundational coursework in genomics and computational biology. DS 6015 Capstone will focus on a project with the Department of Genome Sciences, a subsidiary of UVA's School of Medicine. Working in interdisciplinary teams, they will integrate skills from the program to design, implement, and present an applied project in genomics applications.

Sports Data Science

The Sports Data Science concentration provides a unique opportunity to work with the Virginia Cavaliers in UVA Athletics, which features 25+ NCAA Division I teams. Sports data science is an interdisciplinary field that applies data science methods to understand and improve athletic performance, team strategy, and organizational decision-making. Students in this concentration will gain exposure to traditional sports analytics, sports science, and business analytics, preparing them to work with both competition-focused and operational datasets. Coursework emphasizes multimodal data sources such as game statistics, wearable sensors, biomechanics, and fan engagement metrics. The concentration equips graduates with the skills to derive actionable insights for athletes, coaches, clinicians, and executives while addressing critical issues of equity, privacy, and ethics in sports data. 

The concentration is competitive entry and must be applied for during the application process; refer to the Admissions page for more information. Those admitted to the Sports Data Science Concentration will be notified in their offer letter. Required concentration courses include a course in sports and data methodologies and a new practicum with the UVA Athletics Department. DS 6015 Capstone will focus on a sports data science problem. Working in interdisciplinary teams, they will integrate skills from the program to design, implement, and present an applied project for a client such as a university athletic department, professional team, or industry partner.