The Bachelor of Arts (B.A.) in Applied Statistics provides students with a solid grounding the field of statistics, with particular attention paid to applications. Knowledge of statistics is becoming increasingly important in many fields, so that students completing this major will have many options available upon graduation.
Students completing this major will be well prepared to design experimental studies, analyze data, and communicate results in a wide range of subject areas. They will also be well prepared to enter MS programs 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.
Students who declare the B.A. in Applied Statistics have the option of choosing one of three concentrations within the major. These concentrations are Finance and Business, Biostatistics, and Data Science. The details of these concentrations are given below. The prerequisites to declare any of the concentrations are those listed below.
Program Requirements: B.A. in Applied Statistics
The B.A. in Applied Statistics requires five core courses and five restricted elective courses. In total the B.A. in Applied Statistics requires 30 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: B.A. in Applied 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 1220, MATH 1320, or APMA 1110)
- 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)
- STAT 3110: Foundations of Statistics
(MATH 3100 can also be used when combined with MATH 3350 or MATH 3351) - STAT 3220: Introduction to Regression Analysis
*Note: SEAS students may use equivalent APMA courses for the MATH courses listed above.
Core Courses: B.A. in Applied Statistics
- STAT 3080: Data to Knowledge
- STAT 3110: Foundations of Statistics OR
- MATH 3100: Probability AND
- MATH 3350: Applied Linear Algebra OR MATH 3351: Elementary Linear Algebra
- STAT 3120: Mathematical Statistics
- STAT 3220: Introduction to Regression Analysis
- STAT 4996: Capstone
- Five restricted elective courses, at least three from the Data Analysis list.
*Note: SEAS students may use equivalent APMA courses for the MATH courses listed above.
Restricted Electives: B.A. in Applied Statistics
Students must take five restricted electives, with at least three from the Data Analysis list. At most one of the five restricted electives may be drawn from a non-STAT pneumonic.
B.A. in Applied Statistics Concentrations
Those declaring the B.A. in Applied Statistics have the option of choosing a major concentration. The concentrations are Finance and Business, Biostatistics, and Data Science. The requirements for these concentrations are given below. The prerequisites to declare any of the concentrations are the same as described earlier.
Note: Students choosing a major concentration cannot use the same course as both a core course and a restricted elective; a course can be used to satisfy only one requirement of the B.A. in Applied Statistics.
Biostatistics concentration
Eight core courses (listed below) and two restricted elective courses, at least one from the Data Analysis list.
- STAT 3080: Data to Knowledge
- STAT 3110: Foundations of Statistics OR
- MATH 3100: Probability AND
- MATH 3350: Applied Linear Algebra OR MATH 3351: Elementary Linear Algebra
- STAT 3120: Mathematical Statistics
- STAT 3130: Sample Surveys
- STAT 3220: Introduction to Regression Analysis
- STAT 3280: Data Visualization and Management
- STAT 4160: Experimental Design
- STAT 4996: Capstone
*Note: SEAS students may use equivalent APMA courses for the MATH courses listed above.
Data Science concentration
Eight core courses (listed below) and two restricted elective courses, at least one from the Data Analysis list.
- STAT 3080: Data to Knowledge
- STAT 3110: Foundations of Statistics OR
- MATH 3100: Probability AND
- MATH 3350: Applied Linear Algebra OR MATH 3351: Elementary Linear Algebra
- STAT 3120: Mathematical Statistics
- STAT 3220: Introduction to Regression Analysis
- STAT 3250: Data Analysis with Python
- STAT 3280: Data Visualization and Management
- STAT 4630: Statistical Machine Learning
- STAT 4996: Capstone
*Note: SEAS students may use equivalent APMA courses for the MATH courses listed above.
Finance and Business concentration
Seven core courses (listed below) and three restricted elective courses, at least one from the Data Analysis list.
- STAT 3080: Data to Knowledge
- STAT 3110: Foundations of Statistics OR
- MATH 3100: Probability AND
- MATH 3350: Applied Linear Algebra OR MATH 3351: Elementary Linear Algebra
- STAT 3120: Mathematical Statistics
- STAT 3220: Introduction to Regression Analysis
- STAT 4170: Financial Time Series and Forecasting OR STAT 5170: Applied Time Series
- STAT 4220: Applied Analytics for Business
- STAT 4996: Capstone
*Note: SEAS students may use equivalent APMA courses for the MATH courses listed above.
Data Analysis Restricted Electives: B.A. in Applied Statistics
- STAT 3130: Sample Surveys
- STAT 3480: Nonparametric and Rank-Based Statistics
- STAT 4120: Applied Linear Models
- STAT 4130: Multivariate Statistics
- STAT 4160: Experimental Design
- 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 5330: Data Mining
- STAT 5390: Exploratory Data Analysis
- ECON 3720: Econometrics
- ECON 4720: Econometric Methods
- SOC 5110: Survey Research Methods
- SYS 4021: Linear Statistical Models
Computational Restricted Electives: B.A. in Applied 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 4170 and STAT 5170 will satisfy the major requirements, as these are both versions of a time series 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.
- Only one of STAT 4120, ECON 3720, ECON 4720, and SYS 4021 will satisfy the major requirements, as these are all versions of a regression 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.