Concentrations — School of Data Science

The Bachelor of Science in Data Science (BSDS) at the University of Virginia offers a variety of dynamic concentrations, allowing you to tailor your degree to match your interests and career goals. All BSDS students will select a core concentration in Analytics, Systems, Design, or Value, providing a strong foundation in data science.

You can further customize your experience with an optional collaborative concentration that connects data science with other disciplines across the University: analytical accounting, astronomy, educational analytics, environmental science, human movement and physiology, mathematics, and neuroscience. These concentrations not only prepare you for industry roles and research opportunities but also empower you to apply data science skills to real-world challenges, making your degree as unique as your ambitions.

Core Concentrations

All BSDS students select one of the following concentrations from the School of Data Science. Students will select three courses for their chosen concentration. The four core concentrations are:

Analytics Concentration

DS 4121 - Foundations of Text Analytics (3 credits)
DS 4125 - Introduction to Deep Learning (3 credits)
DS 4126 - Computer Vision (3 credits)
DS 5122 - Large Language Models (3 credits)

Systems Concentration

DS 4220 - IoT and Sensor Data (3 credits)
DS 4221 - Advanced Databases (3 credits)
DS 5220 - Advanced Cloud Computing (3 credits)
DS 5221 - Stream Processing (3 credits)

Design Concentration

DS 4320 - Data by Design (3 credits)
DS 4329 - Data Design Studio (3 credits)
DS 5320 - Human-Centered Design (3 credits)

Value Concentration

DS 4322 - Technology Regulation and Data Science (3 credits)
DS 4423 - Data, Technology, and Society (3 credits)
DS 5420 - Mainframes to Memes (Past, Present, and Future of Information Technology (3 credits)

Note: Students may not double-count courses across concentrations.

Collaborative Concentrations

BSDS students have the option to further customize their degree with a collaborative concentration. Note that some courses may double-count for General Education area requirements (see: "Course Attributes"); refer to Stellic, SIS, or Lou's List for full course descriptions, prerequisites, and course attributes.

Accounting Analytics Concentration

A concentration in analytical accounting with the McIntire School of Commerce provides students with the skills to navigate the rapidly evolving financial landscape by combining data science expertise with foundational accounting knowledge. Students will learn to harness tools like predictive modeling, data visualization, and advanced analytics to uncover insights from financial data, enhance decision-making processes, and detect patterns that drive strategic business outcomes. This interdisciplinary approach prepares graduates to tackle challenges such as fraud detection, financial forecasting, and performance analysis, making them invaluable assets in fields ranging from corporate finance to auditing and consulting. By bridging the gap between data science and accounting, students are uniquely equipped to thrive in a world where data-driven solutions are transforming the financial industry.

The concentration requires the following three sequential courses:

COMM 2010 - Introduction to Financial Accounting (3 credits)
COMM 2020 - Introduction to Management Accounting (3 credits)
COMM 3110 - Intermediate Accounting I (3 credits)

This concentration consists entirely of courses at the UVA McIntire School of Commerce. It is also designed to fulfill most of the prerequisites required to apply for McIntire’s M.S. in Accounting. Data science students who completed the Accounting Analytics Concentration and are interested in pursuing additional studies in accounting are encouraged to take an additional elective (COMM 3120 - Intermediate Accounting II) in order to apply to the master's program.

Astronomy Concentration

The Astronomy Concentration with the College of Arts and Sciences allows students to explore the universe through the power of data. By combining data science skills such as machine learning, big data analysis, and computational modeling with the study of celestial phenomena, students gain the tools to analyze vast datasets from telescopes, satellites, and space missions. This interdisciplinary approach enables students to address questions about the origins of the universe, the behavior of stars and galaxies, and the potential for life on other planets. Graduates with this concentration are prepared to contribute to cutting-edge research in astrophysics, space exploration, and cosmology, as well as to pursue careers in data-driven industries that value critical thinking and advanced analytical capabilities.

Students interested in this concentration should take MATH 1310- Calculus I and MATH 1320 - Calculus II; MATH 1310 counts toward the BSDS major and MATH 1320 will complete the QCDA general education requirement. Note that MATH 1320 is a co-requisite for PHYS 1420 (or pre-requisite for PHYS 1425), which is the first course in the concentration sequence.

To complete the Astronomy Concentration, students must complete:

PHYS 1420 - Introductory Physics (3 credits, 1 lab credit) -or- PHYS 1425 - Introductory Physics for Engineers (3 credits, 1 lab credit)
ASTR 2110 - Introduction to Astrophysics I (3 credits)
ASTR 2120 - Introduction to Astrophysics II (3 credits)
ASTR 4140 - Research Methods in Astrophysics (3 credits)

Educational Analytics Concentration

The Educational Analytics Concentration with the School of Education and Human Development allows students to learn the tools needed for effective decision-making in educational settings. Students will be exposed to norms and practices associated with the field while also learning the advantages and opportunities data-science-centered methods can provide to education-oriented datasets. Graduates will be prepared to drive institutional activity and improve outcomes in K-12 or higher education.

Three courses are required to complete the Educational Analytics Concentration:

DS 4121 - Foundations of Text Analytics (3 credits) -or- DS 4221 - Advanced Databases (3 credits)
EDLF 2080 - Fundamentals of Health and Social Science Statistics (3 credits)
EDLF 4110 - Numbers are People: The Ethics of Data-driven Decision-making in Education (3 credits)

Environmental Science Concentration

The Environmental Science Concentration with the College of Arts and Sciences equips students to tackle pressing environmental challenges through the lens of data. By combining data science techniques such as geospatial analysis, machine learning, and predictive modeling with the study of ecosystems, climate systems, and sustainability, students gain the skills to analyze complex environmental data and develop innovative solutions. This interdisciplinary approach prepares students to address critical issues like climate change, resource management, biodiversity conservation, and environmental policy. Graduates with this concentration are uniquely positioned to make an impact in fields ranging from environmental consulting to renewable energy and governmental or non-profit organizations focused on sustainability and conservation.

The Environmental Sciences Concentration requires students to complete at least three courses: one core course with lab and two elective courses at the 3000-, 4000-, or 5000-level.

Required Courses (select one, including lab):
- EVSC 2800/2801 - Fundamentals Geology + lab (3 credits, 1 lab credit)
- EVSC 3200/3201 - Fundamentals Ecology + lab (3 credits, 1 lab credit)
- EVSC 3300/3301 - Atmosphere and Weather + lab (3 credits, 1 lab credit)
- EVSC 3600/3601 - Physical Hydrology + lab (3 credits, 1 lab credit)
Elective courses (choose two - review descriptions and prerequisites here):
- EVSC 3020 - GIS Methods (4 credits)
- EVSC 4010 - Introduction to Remote Sensing (3 credits)
- EVSC 4035 - Drones in Scientific Research (2 credits)
- EVSC 4080 - Quantitative Methods in Environmental Sciences (3 credits)
- EVSC 4170 - Spatial Ecology (3 credits)
- EVSC 4290 - Limnology: Inland Water Ecosystems (3 credits)
- EVSC 4340 - Human Biometeorology: Weather, Climate, and Human Health (3 credits)
- EVSC 4370 - Climate Near the Ground (3 credits)
- EVSC 4390 - Climate Modeling and Analysis (3 credits)
- EVSC 4470 - Introduction to Climatological Analysis (3 credits)
- EVSC 4559 - Objective Analysis in Environmental Science (3 credits)
- EVSC 4630 - Land-Atmosphere Interaction (3 credits)
- EVSC 4640 - Water Resources in a Changing World (4 credits)
- EVSC 4660 - Hydrological Field Methods and Data Analysis (3 credits)
- EVSC 4840 - Marine Geoscience (3 credits)
- EVSC 4890, 4891 - Planetary Geology + lab (3 credits, 1 lab credit)
- EVSC 5030 - Applied Statistics for Environmental Sciences (4 credits)
- EVSC 5040 - Messy Data: Statistical Methods in Ecology and Environmental Sciences (4 credits)
- EVAT 5300 - Introduction to Climatology (3 credits)
- EVAT 5400 - Boundary Layer Meteorology (4 credits)
- EVEC 5220 - Terrestrial Ecology (4 credits)
- EVGE 5820 - Geomorphology (4 credits)
- EVGE 5840/5841 - Sediment Processes + lab (3 credits, 1 lab credit)
- EVHY 5610 - GIS Watershed Resilience (3 credits)
- EVHY 5640 - Catchment Hydrology: Process and Theory (3 credits)
- EVHY 5650 - Hydrological Transport Processes (4 credits)

Human Movement and Physiology Concentration

The Human Movement and Physiology Concentration with the School of Education and Human Development empowers students to explore the complexities of the human body through data-driven insights. By combining advanced data science techniques such as statistical modeling, machine learning, and wearable technology analysis with the study of biomechanics, exercise science, and physiology, students gain the tools to analyze movement patterns, optimize performance, and advance healthcare interventions. This interdisciplinary approach prepares graduates to address challenges in areas such as sports science, rehabilitation, injury prevention, and human performance research. With this unique skill set, students are equipped to contribute to innovations in health technology, physical therapy, and personalized wellness solutions.

Three courses are required to complete the Human Movement and Physiology Concentration:

Required course: KINE 2000 - Introduction to Kinesiology (3 credits)
Elective courses (select two):
- KINE 2200 - Motor Development (3 credits)
- KINE 3410 - Exercise Physiology (3 credits)*
- KINE 3600/KINE 3601 - Musculoskeletal Anatomy + lab (3 credits, 1 lab credit)
- KINE 3620 - Biomechanics/Motor Control of Human Movement (3 credits)
- KINE 3660 - Neuroscience of Exercise (3 credits)
- KINE 4600/KINE 4601 - Athletic Injuries + lab (3 credits, 1 lab credit) *prerequisite: KINE 3600
*prerequisites: BIOL 3410 - Human Anatomy & Physiology I + BIOL 3420 - Human Anatomy & Physiology II.

Mathematics Concentration

The Mathematics Concentration with the College of Arts and Sciences provides students with a rigorous foundation in the mathematical principles that underpin modern data science. By combining advanced mathematical techniques such as linear algebra, probability, optimization, and numerical analysis with hands-on data science tools like machine learning and statistical modeling, students develop the analytical skills to solve complex, data-driven problems. This interdisciplinary approach equips graduates to tackle challenges in fields such as finance, cryptography, artificial intelligence, and scientific computing, while also preparing them for graduate studies in mathematics or related disciplines. With this concentration, students are uniquely positioned to excel in careers requiring deep quantitative reasoning and computational expertise.

Students interested in the Mathematics Concentration must take MATH 1310 - Calculus I and MATH 1320 - Calculus II. MATH 1310 counts toward the BSDS major and MATH 1320 will complete the QCDA general education requirement. Completion of MATH 1320 is a prerequisite for upper-division MATH courses; MATH 2310 - Calculus III is highly recommended. Refer to the MATH course descriptions for the full list of prerequisites.

To complete the Mathematics Concentration, students must complete a course in Linear Algebra, then one 3000-level course, and one course that is 4000-level or higher. The suggested pathways below list the recommended sequence of courses.

Suggested Pathway No. 1:

MATH 3350 - Applied Linear Algebra (3 credits)
MATH 3250 - Ordinary Differential Equations (4 credits)
MATH 4220 - Partial Differential Equations and Applied Mathematics (3 credits) -or- MATH 4300 - Numerical Analysis (3 credits) -or- MATH 4720 - Differential Geometry (3 credits)

Suggested Pathway No. 2:

MATH 3000 - Transition to Higher Mathematics (4 credits)
MATH 3350 - Applied Linear Algebra (3 credits) -or- MATH 3351 - Elementary Linear Algebra (3 credits)
MATH 4040 - Discrete Math (3 credits) -or- MATH 4559 - Topological Data Analysis (3 credits)

Suggested Pathway No. 3:

MATH 3351 - Elementary Linear Algebra (3 credits)
MATH 3250 - Ordinary Differential Equations (4 credits)
MATH 4720 - Differential Geometry (3 credits)

Suggested Pathway No. 4:

MATH 3351 - Elementary Linear Algebra (3 credits)
MATH 3100 - Introduction to Probability (3 credits)
MATH 4110 - Introduction to Stochastic Processes (3 credits)

Neuroscience (NESC) Concentration

The Neuroscience Concentration with the College of Arts and Sciences offers students the unique opportunity to explore the intersection of data and the brain, equipping them with skills to solve some of the most complex challenges in science and medicine. By combining data science techniques like machine learning, statistical modeling, and data visualization with the study of brain function, behavior, and cognition, students gain the tools to analyze large-scale neural data, develop innovative approaches to neurological research, and contribute to advancements in areas such as mental health, neurodegenerative disease, and brain-computer interfaces. This interdisciplinary focus not only prepares students for careers at the cutting edge of neuroscience and technology but also positions them as leaders in a rapidly evolving field where data-driven insights are shaping the future of healthcare and human understanding.