Innovative Electives Empower Students to Customize Their Data Science Pathways
In today’s competitive data landscape, it’s not enough to simply understand algorithms or code in Python. Employers and innovators alike are looking for data scientists who can think critically, apply their knowledge to diverse domains, and adapt to a fast-changing field.
In the University of Virginia’s Online M.S. in Data Science (MSDS) program, students gain that advantage through an experience that combines the academic rigor of UVA, the flexibility of online learning, and the collaborative strength of a cohort model. One of the key differentiators of this program lies in its electives: specialized, faculty-designed courses that invite students to explore, experiment, and expand their expertise in areas that matter most to their goals.
A Cohort Experience with Flexibility and Depth
Unlike many asynchronous, self-paced online programs or "MOOC-based" degrees, the online master's at the UVA School of Data Science is built around a cohort model that emphasizes connection and collaboration. Students progress together through the core curriculum, developing a strong foundation in programming, statistics, and machine learning, while also forming professional relationships that last well beyond graduation and into future career connections.
As MSDS Online Program Director Jon Kropko explains, “After three semesters of core training in data science, we build on that training with electives in semesters four and five.”
Those final semesters are where customization begins. Through a carefully curated selection of electives, students dive deeper into specialized topics, gaining both technical mastery and domain understanding. The courses are live and interactive, taught by expert faculty who bring research, industry experience, and mentorship directly into the virtual classroom.
The result is a learning experience that feels deeply personal; structured enough to ensure rigor, but flexible enough to fit into students’ professional and personal lives.
Two Kinds of Electives, One Purpose: Impact
Kropko describes the program’s electives as falling into two distinct but complementary categories: “One kind teaches a high-end, difficult and valuable skill, such as reinforcement learning, advanced data visualization, text as data, and large language model engineering," he said. "The other kind focuses on how data science is used in a profession that our students might want to pursue, such as health care, finance, international development and aid, software engineering, and the tech industry in general.”
This intentional design ensures that students graduate not only with advanced technical capabilities but with an understanding of how those capabilities translate into real-world impact.
"The How to Train Your LLM elective gave me a deep understanding of how LLMs work," said MSDS Online student Jett Badalament-Tirrell. "Knowing their strengths and limitations has guided my work in and outside the classroom and I find it to have been one of the most valuable classes I have taken in the program."
The UVA Difference: Faculty, Structure, and Substance
While other programs often rely on pre-recorded content or self-paced modules, UVA’s online courses are faculty-led and community-driven. Students engage in live discussions, collaborative projects, and peer feedback that simulate the dynamics of a professional data team.
“Our instructors are people who not only know the material but have a great deal of personal experience with it,” Kropko noted. Electives are offered in smaller class sizes, creating space for meaningful interaction. “The electives have smaller class sizes, and our goal is to provide students and teachers with the opportunity to form real mentorship relationships,” he said.
This model ensures that every elective is more than a technical exercise – it’s a learning lab where ideas are tested, debated, and refined. Students benefit from direct access to faculty who are not only experts in their field but also active researchers and practitioners shaping the future of data science.
Additionally, the curriculum is nimble and forward-looking. The School of Data Science continuously develops new electives in response to emerging technologies (for instance, DS 5002 - How to Train Your LLM: Engineering LLMs for Custom Tasks) and market needs, ensuring that UVA students remain ahead of the curve.
A Dynamic Portfolio of Electives
The elective offerings reflect the breadth and range of disciplines that define data science at UVA. They are not add-ons or afterthoughts; they are an intentional extension of the program’s mission to prepare principled, impact-driven data scientists.
Here’s a closer look at some of the electives that set UVA’s online master’s in data science apart. Below is a sample of courses offered in the Online MSDS program (availability may vary by semester, with additional electives currently in development):
DS 5001 – Text as Data
From product reviews to policy documents, text data is everywhere. This course introduces students to natural language processing and information theory, focusing on how to extract meaning and structure from unstructured text. Students learn to explore sentiment, narrative, and social patterns, all using unsupervised methods.
DS 5002 – How to Train Your LLM: Engineering LLMs for Custom Tasks
As large language models (LLMs) reshape industries, UVA’s online students are learning to train and customize their own. This cutting-edge course covers the full lifecycle, from architecture to deployment, culminating in a trained model and a HuggingFace model card students can share in portfolios and interviews.
DS 5003 – Healthcare for Data Science
For those drawn to healthcare and biomedicine, this course bridges data science with clinical understanding. Students work with a range of healthcare data types, from EHRs to biomedical images, applying analytical methods to improve outcomes and support evidence-based decisions.
DS 5004 – Applied Reinforcement Learning
How does an AI learn to play chess? Or optimize energy use, or recommend the next show on Netflix? Through reinforcement learning. This course introduces the concepts behind intelligent agents, Markov Decision Processes, Q-learning, and policy gradients, giving students hands-on experience with one of the fastest-evolving areas in AI.
DS 5007 - Don't Invent the Torment Nexus: the History of Technology and Work
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
This course 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 is a key feature of the course. Students will also critically evaluate tools, data sources, and methods.
DS 5111 - Streamlining Data Science with Software and Automation Skills
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, and 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
Uncertainty is a fact of life – and of data. Bayesian methods help data scientists model and quantify that uncertainty to make more informed decisions. Students learn Bayesian inference, MCMC methods, and variational inference techniques that underpin many of today’s most advanced AI systems.
SARC 5400 – Data Visualization
Offered in collaboration with the UVA School of Architecture, this course fuses design, communication, and analysis. Students learn to think visually and spatially, using interactive web tools and coding to create clear, meaningful, and even beautiful visualizations that bring data to life.
More Than a Degree: A Tailored Path to Impact
Every online master's student arrives with a different story. Some are seasoned professionals expanding their analytics toolkits, while others are career changers entering a high-demand field. The elective structure allows each student to chart their own path, gaining both depth and relevance.
For example:
- A healthcare data analyst might pair "Healthcare for Data Science" with "Bayesian Machine Learning" to specialize in predictive modeling for patient outcomes.
- A software engineer may combine "Applied Reinforcement Learning" with "How to Train Your LLM" to advance toward AI development roles.
Each pathway reflects UVA’s belief that data science is for everyone, and that its most powerful applications emerge when technical skill meets human insight.
The Value of Choice with the Power of Community
In a crowded landscape of online data science degrees, UVA’s Online MSDS program stands out for its combination of academic rigor, meaningful choice, and human connection. Students gain not only the technical depth expected of a top-tier graduate program but also the creative flexibility to pursue their passions and the support of a lifelong professional network.
Electives aren’t just a curricular feature; they’re a signal of what UVA values most: curiosity, adaptability, and leadership in the face of change.
Through its online program, the School of Data Science invites students everywhere to join a community that’s shaping the future of the field.
The Graduate Record represents the official repository for academic program requirements.
Learn more about the part-time, 100% online M.S. in Data Science at the University of Virginia. Request more information, connect with Admissions, or start your application today.
