Academics

Academic Timeline

The 33-credit-hour online M.S. in Data Science (MSDS) program is offered across five terms. The curriculum is tightly prescribed. Courses are interdisciplinary and taught by faculty at the School of Data Science and from across the University of Virginia. Several large data sets are used between courses to increase the program’s cohesion. Students take a core set of courses with elective courses offered in semesters 4 and 5. At the conclusion of the MSDS program, students address an important data science challenge through a sponsored team capstone project. 

For course details and descriptions by term, see below. 

Semester 1

DS 6001 - Data Engineering I: Data Pipeline Architecture (3 credits)

Covers the practice of data science, 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 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.

Semester 2

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.

Semester 3

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 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.

Semester 4

Restrictive Elective (3 credits)

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.

Semester 5

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. Capstone course for MSDS Online students.

Restrictive Elective (3 credits)

Possible Electives

Below is a sample of electives that have been offered previously in the online MSDS program. Availability of electives may vary by semester. The development of additional electives is underway.

DS 5001 -  Text as Data (3 credits)

Introduction to text analytics with a focus on long-form documents, such as reviews, news articles, and novels. Students convert source texts into structure-preserving analytical form and then apply information theory, NLP tools, and vector-based methods to explore language models, topic models, sentiment analyses, and narrative structures. The focus is on unsupervised methods to explore cognitive and social patterns in texts.

DS 5002 - How to Train Your LLM: Engineering LLMs for Custom Tasks (3 credits)

Train your own LLM for a custom task. Learn about the LLM lifecycle from architecture, to pre-training, to supervised finetuning, to deployment, to model editing/updating, including discussing LLM limitations. End up with your own trained LLM, a HuggingFace model card you can show off in technical interviews, and a plan for how to stay up to date with this fast-moving field.

DS 5003 - Healthcare for Data Science (3 credits)

Provides healthcare domain knowledge, healthcare data understanding, and data science methodologies to solve problems. Understand data types, models, and sources, including electronic health record data; health outcomes, quality, risk, and safety data; and unstructured data, such as clinical text data; biomedical sensor data; and biomedical image data. Querying with SQL, data visualization with Tableau, and analysis and prediction with Python.

DS 5004 - Applied Reinforcement Learning (3 credits)

Reinforcement Learning is a dynamic area in machine learning that allows an agent to learn by interacting with its environment. This enables learning when the ground truth is unavailable or outdated (think predicting Netflix usage before and during a pandemic). This course will introduce topics including k-armed bandits, Markov Decision Processes, value functions, Policy Gradients, Q-Learning, and deep Q-Learning. 

DS 5007 - Don't Invent The Torment Nexus: The History of Technology & Work (3 credits)

This course looks into the past, present, and future of technologies that impact labor, with an eye to empowering students with knowledge about the social, economic, and political dimensions of the tools they use both inside and outside of work. The course covers labor history, whistleblowers, and hidden histories of common technologies that reorient common assumptions about what technologies can do, and what they have done in the past.

DS 5072 - Data Science for Social Impact (3 credits)

Provides domain knowledge of humanitarian action, the data and data systems used by humanitarian organizations, the lifesaving implications of predictive analytics in disaster contexts and the complex data ethics considerations unique to the social impact sector. Communicating with clients/stakeholders/decision makers about resource allocation strategy and critically evaluating tools, data sources and methods are key features of the course.
 

DS 5111 - Streamlining Data Science with Software and Automation Skills (3 credits)

Code an end-to-end data science project with core software engineering and automation to quickly integrate into a corporate environment. Use version control to focus on solutions, leverage automation at your command line and in the cloud, deliver solid code by incorporating testing, lower extension and maintenance time with OOP and Design Patterns, ensuring your code's path to production to deliver a complete package to the enterprise.

DS 6040 - Bayesian Machine Learning (3 credits)

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.

SARC 5400 - Data Visualization (3 credits)

Thinking with Images. People have been looking at data for centuries — with their eyes — to discover patterns, meaning, and insight into the most important challenges of their time. This course teaches visual and spatial thinking coupled with visual data tools and interactive web coding to envision information. Far beyond plotting, finding ways to respond to complex problems, we will study and make useful and compelling tools to see. 

Weekly Breakdown

The online M.S. in Data Science Program employs a flipped classroom model to accommodate the needs of students balancing professional and academic goals. Flipped classrooms are learner-focused environments that allow students to explore course topics independently, outside of class, using asynchronous course content. 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.

Each week, students can expect one hour of live synchronous time per course, which will be scheduled in the evenings (ET) during weekdays. On a weekly basis, most part-time online MSDS students typically spend 10 to 12 hours per course completing asynchronous content, which includes homework, projects, and readings. However, the workload can vary by student and by course, and total hours may depend on learning styles and level of comfort with program prerequisites.