Academics

Prerequisites

The BSDS program has prerequisites of the following two courses, which must be completed or in progress at the time of application:

• DS 1001: Foundation of Data Science (3 credits)
• DS 1002: Programming for Data Science (3 credits)*

Both courses are offered in the fall and spring semesters, do not have prerequisites, and may be taken concurrently. Students interested in pursuing the BSDS program must take both courses in their first-year to be eligible to apply. 

Learn more about the application process on our Admissions page.

*Refer to our FAQs for alternatives to DS 1002.

Once admitted to the program, BSDS students will follow a three-year curriculum. Courses used to satisfy requirements in the Data Science Minor (DS 2002, DS 2003, DS 2004, DS 3001, & DS 4002) do not fulfill requirements in the B.S. of Data Science.

First Year of Major: Understand

Fall

• DS 2022 – Systems I: Intro. to Computing  
  • Will center on exposing students to contemporary pipelines for data analysis through a series of steadily escalating use cases. The course will begin with simple local database construction such as SQLite and foundation knowledge in terms of computational environments. The content will lay the groundwork for more advanced Systems Domain courses in the major.
• DS 2023 – Design I: Communicating with Data
  • Designed not only to teach students tools necessary to visualize data but also effective techniques for explaining data driven results with an emphasis on communicating statistical output in a manner that best represents the findings. Lays the foundation for more advanced topics in the Data Design domain. Content on the development of interactive plots and dashboards will also be included.
• DS 2026 – Computational Probability
  • Covers the fundamentals of probability theory & stochastic processes. Become conversant in the tools of probability. Clearly describe & implement concepts related to random variables, properties of probability, distributions, expectations, moments, transformations, model fit, basic inference, sampling distributions, discrete & continuous time Markov chains, & Brownian motion. Illustrate most topics with both analytic & computational solutions.
• MATH 1190/1210/1310 or APMA 1090 – Calculus I* 

Spring 

• DS 2024 – Value I: Ethics & Policy in Data Science
  • Explores principles and applications of data ethics within a broader social framework. Works to lay foundational knowledge for more advanced courses in the Value domain of the major. Will discuss who is responsible for doing responsible data science, question how our work shapes the world around us, and understand the impacts of big data on people and communities.
• DS 3021 – Analytics I: Machine Learning
  • Exposes students to foundational knowledge in the area of analytics, especially as it relates to machine learning. The focus is on methods needed to prepare data for machine learning models, how to evaluate the output of ML models and engineering features.
• DS 3025 – Mathematics for Data Science
  • Engage with and train in the use of key concepts in machine learning and math: OLS estimator for regression; logistic regression & maximum likelihood estimator; multiple linear regression; principal components analysis & multiple correspondence analysis; neural networks; logarithms; probability distributions; integrals; multivariate optimization; matrix notation, eigen-math, and matrix decomposition; infinite power series & Taylor series.

Second Year of Major: Apply

Fall

• DS 3022 – Data Engineering
  • Moves deeper into current best practices around data engineering in industry. Topics will review basic data collection, ingestion, processing, and storage, moving beyond to data governance, security, pipeline orchestration, monitoring and maintenance, optimization, and documentation. Relies heavily on DevOps principles of automation, continuous improvement, and an understanding of the entire software/data lifecycle.
• DS 4021 – Analytics II: Machine Learning
  • Critique models and adapt them to a variety of data sets. Gain a deeper understanding of core ML concepts. Build towards neural networks (latent index models, more complex linear models with non-linear transformations of the data). Compare new methods to kNN, clustering, linear models from ML1 to discuss performance differences as complex and predictive power increases. How mathematical concepts are present in the models presented.

Spring

• DS 3026 – Principles of Inference & Prediction  
  • Explore mathematical foundations of inferential and prediction frameworks, with emphasis on computation, used to learn from data. Frequentist, Bayesian, and Likelihood viewpoints are all considered. Topics: principles of estimation, optimality, bias, variance, consistency, sampling distributions, estimating equations, information, bootstrap methods, ROC curves, shrinkage, large sample theory, prediction optimality versus estimation optimality.
• DS 4320 – Data by Design
  • Comprehensive exploration of the multifaceted aspects of data creation, emphasizing the symbiotic relationship between design and data. Students will gain insight into the intentional and unintentional mechanisms that contribute to data creation, including human input, technological processes, environmental factors, and systemic influences.
• DS 4024 – Value II: Explainable AI
  • Explainable artificial intelligence (XAI) is a subfield of machine learning that provides transparency for complex models to connect the technical meaning to social interpretation. Explore interpretability, transparency, and black-box machine learning methods. Covers definitions, decision support, trust, and ethical considerations, and the latest advances in creating reliable and transparent AI models.

Third Year of Major: Analyze, Evaluate, Create

Fall

• Concentration Course  
• Concentration Course
• Concentration Course

Spring

• DS 4022 – Data Science Project
  • Will allow students to take the knowledge gained throughout the major and deploy a data driven system. Students will work in groups and will need to propose their own projects. Upon completion of the course, students will be required to present their results and publish project content to an open forum.
• 2nd Concentration Course (Optional)
• 2nd Concentration Course (Optional) 

*AB or BC Calculus with a score of 4 or 5, or IB HL Mathematics with a score of 5, 6, or 7 will also meet this requirement. 

Tailor your degree to match your interests and career goals with one or more dynamic concentrations. These concentrations not only prepare you for industry roles and research opportunities, but also empower you to make your degree as unique as  your ambitions. 

All students will select at least one core concentration from the School of Data Science. Students may elect to pursue multiple concentrations, including adding a collaborative concentration. All concentrations require 3 classes/ 9 credits; students may not double-count courses across concentrations.

School of Data Science Core Concentrations
All students are required to select one of the following:

• Analytics
• Systems
• Design
• Value

Collaborative Concentrations
Students may elect to add one or more of the following collaborative concentrations:

• Accounting Analytics
• Astronomy
• Educational Analytics
• Environmental Science
• Human Movement and Physiology
• Mathematics
• Neuroscience

Visit the Concentrations page for more information.
 

First-year prospective BSDS students are advised to stay on track with the curricular requirements of their home school; any course completed that does not count toward the School of Data Science's General Education Requirements will count toward overall degree credits. Additionally, we strongly encourage first-year students to take their First Writing Requirement and Calculus I. Refer to the BSDS FAQs for more information. The Undergraduate Record is the official repository for all academic information, including the full list of BSDS General Education Requirements. 

According to the U.S. Bureau of Labor Statistics, employment of data scientists is expected to grow by 36% over the next decade with about 20,800 annual job openings. The B.S. in Data Science prepares students to become experts in the field, work at the cutting edge of a new discipline, and thrive in a data-centric world.

In addition to UVA Career Center, students in the BSDS have access to the School of Data Science's career resources, including: 

• One-on-one career coaching
• Workshops to prepare for the internship and job search
• Industry-led technical talks
• Career development series specific to BSDS students
• Access to professional development funds
• Faculty mentoring

...and more! Visit SDS Career Services for more information.