Concentrations

The Accounting Analytics Concentration 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.

Below are the list of courses required to complete the concentration.

• COMM 2010 - Introduction to Financial Accounting
• COMM 2020 - Introduction to Management Accounting
• COMM 3110 - Intermediate Accounting I

This concentration consist 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.

A concentration in astronomy 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.

To complete the Astronomy Concentration, students must complete:

• Calculus I (MATH 1310, MATH 1210 or AP credit)
• PHYS 1420/1425 - Introductory Physics
• ASTR 2110 - Introduction to Astrophysics I 
• ASTR 2120 - Introduction to Astrophysics II 
• ASTR 4140 - Research Methods in Astrophysics 

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

A concentration in human movement and physiology 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 
• Elective courses (select two):
  • KINE 2200 - Motor Development 
  • KINE 3410 - Exercise Physiology 
  • KINE 3600 - Musculoskeletal Anatomy 
  • KINE 3620 - Biomechanics/Motor Control of Human Movement 
  • KINE 3660 - Neuroscience of Exercise 
  • KINE 4600 - Athletic Injuries 

A concentration in mathematics 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.

To complete the Mathematics Concentration, students must complete:

• Math 3350 or Math 3351 (Students should plan ahead as one or the other of these is a prerequisite for later courses.)
• Two additional mathematics courses at or above the 3000 level, including at least one at or above the 4000 level. Suggested pathways include:
  • 3351, 3250 (ODE), 4220 (PDE)
  • 3350, 3250 (ODE), 4300 (Numerical Analysis)
  • 3000 (Proofs), 3350 or 3351, 4040 (Discrete Math)
  • 3351, 3250 (ODE), 4720 (Differential Geometry)
  • 3351, 3100 (Probability), 4110 (Stochastic Processes) 
  • 3351, 3000, 4559 (Topological Data Analysis, spring only)

Note: Many upper division mathematics courses have a prerequisite of either MATH 3310 (Basic Real Analysis) or MATH 3354 (Introduction to Abstract Algebra). 
 

A concentration in neuroscience offers students a 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.

Students must complete at least 3 courses totaling 9 credits.

• BIOL 2100 - Introduction to Biology with Laboratory: Cell Biology & Genetics
• Introductory neuroscience course (select one):
  • PSYC 3200 - Fundamentals of Neuroscience
  • BIOL 3050 - Introduction to Neurobiology. 
• Elective course (select one): 
  • NESC 3559 - The Study of Neuroscience From Molecules to Minds
  • Electives from Group II
  • PSYC 3160 - Cognitive Neuroscience