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What Incoming Students Can Expect from the Three-Year Data Science Major
Students from the inaugural class of data science majors at the University are now in their second year of the B.S. in Data Science (BSDS) program, and the School of Data Science is welcoming an additional 125 undergraduates this fall. The academic program at the University's newest school is expanding and evolving alongside the field of data science. With updated concentrations and study abroad options, students who major in data science have the opportunity to shape the future of the degree and the fast-growing field.
First, data science is a new, interdisciplinary field that studies data, which is being generated in massive quantities across the world on a daily basis. Data scientists apply various methods and technology to better understand and generalize findings from that data, enabling more informed decision-making and problem-solving. Using computational, statistical and mathematical methods, data science is broadly applied across every discipline, field and sector in the world.
Data science is one of the few majors at the University that is a three-year program, but Brian Wright, associate professor of data science and director of undergraduate programs, says this was by design because there is so much to learn. “We wanted to make the undergraduate experience as comprehensive as possible and also allow students space to take those skills and apply them,” he said.
Students are also helping to shape the future of the budding program, providing their input and helping to chart the course for future cohorts. "As a new program, students are putting their mark on their experience in and outside of the classroom," Wright said. "Faculty and staff are eager for their feedback and opinions on how to enhance co-curricular activities and build a strong BSDS community."
The program adopts the School’s framework for data science, the 4+1 model, which loosely groups data science into four domains: Systems, Analytics, Design and Data + Society. These are then applied to a fifth domain, Practice. This model is designed to highlight how data science is interdisciplinary and helps to distinguish it from related fields like computer science and statistics.
The data science curriculum provides an overview of each domain, and one of the four domains is chosen as a core concentration for more detailed examination. Each student follows the same curriculum layout in the first year, with options to study abroad in the second year, and room for concentration electives in their final year.
Additionally, there are optional collaborative concentrations that refine and specify data science for a variety of fields. Current offerings include Accounting Analytics, Astronomy, Educational Analytics, Environmental Science, Human Movement and Physiology, Mathematics and Neuroscience. The School is working hard to expand these options, eventually looking to offer concentrations for most applicable majors at the University.
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The year one academic structure also supports the major's cohort model, a signature part of the program. Students are in the same standard set of core classes, learning and doing group projects every day, and growing together as people and data scientists.
This first year explores the theme “understand,” introducing students to the foundations of technical concepts like computing, data and probability. In the fall semester, students take DS 2022, “Systems I: Introduction to Computing” and DS 2023, “Communicating with Data” as well as a computational probability class and the first calculus requirement. In the spring semester, students are exposed to ethical data science with DS 2024, “Value I: Ethics & Policy in Data Science” and DS 3021, “Analytics I: Machine Learning” as well as a foundational data science mathematics class.
The theme of year two is “apply,” translating these skills into model building and machine learning. In the fall, students take DS 3022, “Data Engineering” and DS 4021, “Analytics II: Machine Learning.” In the spring, students take DS 3026, “Principles of Inference & Prediction;” DS 4320, “Data by Design;” and DS 4024, “Value II: Explainable AI.”
In the fall semester of year two, students can choose to study abroad. In collaboration with the International Studies Office, the School of Data Science offers semester-long study abroad options that satisfy BSDS core and concentration courses, general education requirements and electives. These include UVA in Valencia and DIS Scandinavia, and all data science courses are taught in English — though the Valencia program requires that students be eligible to take SPAN 2010 - Intermediate Spanish and they must take at least one Spanish-language course.
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The topic of year three is “analyze, evaluate, create,” focusing mostly on the student’s chosen core concentration, while leaving room to take electives and classes of other subjects. In the fall semester, students will take three courses from their selected concentration, with room for optional second concentration courses in the spring semester. Students finish in the spring with DS 4022, “Data Science Project,” which allows students to propose their own projects, develop a data-driven system and publish their completed project to an open forum.
This final course requires students to employ an amalgamation of everything they have learned in the major, while also creating a deliverable that can be used to apply for jobs or graduate education. The School routinely emphasizes soft skills — communicating and translating complex technical ideas into language that can be easily understood — as integral to a data science career.
BSDS majors also have access to a mentor program that connects them with faculty and research opportunities. In the program, every student is matched with a faculty member that will advise them throughout their year. Wright said they are also planning a new research program that is inclusive of experiential learning, where students that want to do research are matched with faculty members to have those opportunities.
A point of emphasis for the School is that anyone can be a data scientist and that data science is ingrained in a multitude of industries. “We try and make it very approachable for a wide variety of backgrounds,” Wright said, encouraging students not to think of data science as a purely quantitative experience and to imagine many possibilities for their degree.
The wide range of applications was a major reason why BSDS ambassador Tara Ghose chose to study data science. “A big consideration for me was just how applicable it was, and how easy it is to merge into pretty much any industry with that technical background," she said.
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Ghose shared how impactful the classroom atmosphere of freedom and collaboration has been. One of the most influential classes Ghose has taken in the BSDS program thus far was “Communicating with Data.” Through this course, she learned how essential it is for data scientists to approach their work with both curiosity and integrity.
“We had the freedom to explore datasets that aligned with our personal interests, whether it be socioeconomic issues or Olympic medal counts,” Ghose said. “More importantly, the class taught me how to critically evaluate data visualizations and approach data storytelling.
Beyond learning technical skills like building dashboards in Python and working with the tidyverse R programming language package, Ghose said she gained a deeper understanding of data as a tool for communication that carries real-world implications. "These are insights I’ll carry with me into any career I pursue, where clear, responsible and impactful data communication will be key,” she said.
Mason Nicoletti, president of the Undergraduate Data Science Council, plans to use his degree to improve the field of medicine and public health, particularly in the areas of genomics, drug discovery, and precision medicine. “The BSDS program has taught me to think about impact,” he said. “Everything we do involving variables and numbers has the ability to tell a story. My role as a data scientist is to uncover the details that are not so easily identifiable or understandable. Then comes the most important part — sharing this information in a way that can make a difference.”
Those interested in the data science major apply during the second semester of their first year, and double-majoring with another program in the College of Arts & Sciences is also an option. The application requires a copy of a current University transcript, three essay questions, a resume and a recommendation from a supervisor, advisor, mentor or instructor, though this individual cannot be School of Data Science faculty.
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The application also requires fulfillment of two required prerequisite courses: DS 1001, "Foundations of Data Science" and DS 1002, "Programming for Data Science," which can be completed or in progress at the time of submission. Both courses are offered in the fall and spring semesters, can be taken in any order, do not have prerequisites and may be taken concurrently.
While not eligible for the BSDS program, second-, third- and fourth-year students, as well as transfer students, may pursue a minor in data science or apply to the School’s graduate programs. The minor in data science is declared through a minor declaration form and requires one course from each category: foundations programming, analytics, systems, data design or value and a domain elective or final project. The School's M.S. in Data Science is offered in two formats: a 12-month residential format and a 20-month, part-time online format.
Together, along with a Ph.D. program, these offerings reflect the School’s belief that data science is for everyone and that students at all levels can help shape the future of this evolving field. For questions and more information about the BSDS program specifically, email the admissions team at sdsadmissions@virginia.edu or contact Assistant Director of Admissions Recruitment Jacob Angelo at angelojm@virginia.edu.
