These requirements are effective for all declarations beginning on August 1, 2024.
Requirements for declarations through July 31, 2024 can be found on this page.
The Bachelor of Science (B.S.) in Statistics provides students with a deep grounding in the field of statistics, both applications and theory. A deep understanding of data analysis and statistics plays a critical role in many aspects of modern society. Students completing this major will have a wide range of options available upon graduation.
Students completing Bachelor of Science (B.S.) in Statistics will be well prepared to design experimental studies, clean and analyze data, modify existing statistical methods guided by underlying theory, and effectively communicate results to stakeholders. They will also be well prepared to enter graduate programs (M.S. or Ph.D.) in statistics and related fields. With a modest amount of advance planning students are able to complete an M.S. in Statistics at UVa with one additional year of study. Students interested in the B.A./M.S. program should see this web link.
Program Requirements: B.S. in Statistics
The B.S. in Statistics requires ten core courses and five restricted elective courses. In total the B.S. in Statistics requires 46 credit hours, plus prerequisite courses. There are two lists of restricted elective courses, those that focus on data analysis and those that are more computational. Of the five restricted elective courses, at least three must be taken from the Data Analysis list. A grade of C- or higher is required for all prerequisite and major courses.
Prerequisites to Declare the B.S. in Statistics
Students must have completed all prerequisite courses to declare the major. Students may use AP credit to meet prerequisite requirements.
- Calculus II (one of MATH 1320, APMA 2120)
- Introductory Statistics (one of STAT 1100, STAT 1120, STAT 2020, STAT 2120, APMA 3110, APMA 3120)
- Introductory Programming (one of STAT 1601, STAT 1602, CS 1110, CS 1111, CS 1112, CS 1113)
- MATH 3100: Probability
- STAT 3220: Introduction to Regression Analysis
*Note: SEAS students may use equivalent APMA courses for the MATH courses listed above.
Core Courses: B.S. in Statistics
- STAT 3080: Data to Knowledge
- MATH 3100: Probability
- STAT 3120: Mathematical Statistics
- STAT 3130: Sample Surveys
- STAT 3220: Introduction to Regression Analysis
- MATH 3310: Basic Real Analysis
- MATH 3351: Linear Algebra OR MATH 3350: Applied Linear Algebra
- STAT 4120: Linear Models
- STAT 4160: Experimental Design
- STAT 4996: Capstone
*Note: SEAS students may use equivalent APMA courses for the MATH courses listed above.
Restricted Electives: B.S. in Statistics
Students must take five restricted electives, with at least three from the Data Analysis list. At most two of the five restricted electives may be drawn from a non-STAT pneumonic.
Data Analysis Restricted Electives: B.S. in Statistics
- STAT 3480: Nonparametric and Rank-Based Statistics
- STAT 4130: Multivariate Statistics
- STAT 4170: Financial Times Series and Forecasting
- STAT 4220: Applied Analytics for Business
- STAT 4630: Statistical Machine Learning
- STAT 4800: Advanced Sports Analytics I
- STAT 5140: Survival Analysis and Reliability Theory
- STAT 5170: Applied Time Series
- STAT 5310: Clinical Trials Methodology
- STAT 5330: Data Mining
- STAT 5390: Exploratory Data Analysis
- STAT 5630: Statistical Machine Learning
- SOC 5110: Survey Research Methods
Computational Restricted Electives: B.S. in Statistics
- STAT 3250: Data Analysis with Python
- STAT 3280: Data Visualization and Management
- ASTR 4140: Research Methods in Astrophysics
- COMM 3220: Database Management Systems and Business Intelligence
- CS 4444: Parallel Computing
- CS 4740: Cloud Computing
- CS 4750: Databases
- PHYS 5630: Computational Physics I
Course Duplication Limitations
- Only one of STAT 4630 and STAT 5630 will satisfy the major requirements, as these are both versions of a machine learning course.
- Only one of STAT 4260, ASTR 4140, COMM 3220, and CS 4750 will satisfy the major requirements, as these are all versions of a database course.
Description of Capstone
For the capstone, students will work in teams of 3 or 4 to complete an extensive data analysis project. The students and capstone faculty will work collaboratively to develop a hands-on project for each team to demonstrate knowledge and skill in data analysis, interpretation, and communication. Each project will require the team to determine the nature of the questions of interest; prepare data for analysis; select and perform the appropriate analysis; determine conclusions; and present the results. The capstone project will provide an opportunity to observe how students work through all aspects of a statistical analysis.
Students will be guided and evaluated by the capstone faculty. The capstone experience will culminate with the submission of a final report and a formal presentation. If a student fails the capstone course, the Director of Undergraduate Programs will meet with the student to determine a set of revisions and/or alternative academic activities to complete their project. A student who fails to complete their project may retake the course in a subsequent semester.