Statistics, B.S.

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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 the 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.S./M.S. program should visit the Department of Statistics website.

Universal Curriculum Requirements

To be awarded a degree from the College of Arts and Sciences, students are required to complete universal curriculum requirements in addition to the program requirements provided below. The school universal curriculum requirements can be found on the school Degree Programs page .

Program Requirements

The BS in Statistics requires nine core courses and six restricted elective courses. In total the BS 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: 16 - 17 credit hours

Students must have completed all prerequisite courses to declare the major. Students may use AP credit to meet the prerequisite requirements.

Calculus II: Fulfilled by one of the following courses:
MATH 1320 - Calculus II Credits: 4
APMA 2120 - Multivariable Calculus Credits: 4
Introductory Statistics: Fulfilled by one of the following courses:
STAT 1100 - Chance: An Introduction to Statistics Credits: 3
STAT 1120 - Introduction to Statistics Credits: 3
STAT 2020 - Statistics for Biologists Credits: 4
STAT 2120 - Introduction to Statistical Analysis Credits: 4
APMA 3110 - Applied Statistics and Probability Credits: 3
APMA 3120 - Statistics Credits: 3
Introductory Programming: Fulfilled by one of the following courses:
STAT 1601 - Introduction to Data Science with R Credits: 3
STAT 1602 - Introduction to Data Science with Python Credits: 3
CS 1110 - Introduction to Programming Credits: 3
CS 1111 - Introduction to Programming Credits: 3
CS 1112 - Introduction to Programming Credits: 3
CS 1113 - Introduction to Programming Credits: 3
EACH OF THE FOLLOWING:
MATH 3100 - Introduction to Probability Credits: 3
STAT 3220 - Introduction to Regression Analysis Credits: 3

Core Courses: 31 credit hours

STAT 3080 - From Data to Knowledge Credits: 3
STAT 3120 - Introduction to Mathematical Statistics Credits: 3
STAT 3130 - Design and Analysis of Sample Surveys Credits: 3
STAT 3220 - Introduction to Regression Analysis Credits: 3
MATH 3310 - Basic Real Analysis Credits: 4
STAT 4120 - Applied Linear Models Credits: 3
STAT 4160 - Experimental Design Credits: 3
STAT 4996 - Capstone Credits: 3
MATH 3100 - Introduction to Probability Credits: 3
MATH 3350 - Applied Linear Algebra Credits: 3
OR
MATH 3351 - Elementary Linear Algebra Credits: 3

Restricted Electives: 15 credit hours

The Data Analysis restricted electives and the Computational restricted electives are listed below. 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: Minimum of 9 credit hours

STAT 3480 - Nonparametric and Rank-Based Statistics Credits: 3
STAT 4130 - Applied Multivariate Statistics Credits: 3
STAT 4160 - Experimental Design Credits: 3
STAT 4170 - Financial Time Series and Forecasting Credits: 3
STAT 4220 - Applied Analytics for Business Credits: 3
STAT 4630 - Statistical Machine Learning Credits: 3
STAT 4800 - Advanced Sports Analytics I Credits: 3
STAT 5140 - Survival Analysis and Reliability Theory Credits: 3
STAT 5170 - Applied Time Series Credits: 3
STAT 5310 - Clinical Trials Methodology Credits: 3
STAT 5330 - Data Mining Credits: 3
STAT 5390 - Exploratory Data Analysis Credits: 3
STAT 5630 - Statistical Machine Learning Credits: 3
SOC 5110 - Survey Research Methods Credits: 3

Computational Restricted Electives

STAT 3250 - Data Analysis with Python Credits: 3
STAT 3280 - Data Visualization and Management Credits: 3
ASTR 4140 - Research Methods in Astrophysics Credits: 3
COMM 3220 - Data Management for Decision Making Credits: 3
CS 4444 - Introduction to Parallel Computing Credits: 3
CS 4740 - Cloud Computing Credits: 3
CS 4750 - Database Systems Credits: 3
PHYS 5630 Computational Physics Credits: 3

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 Captsone

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.

Additional Information

For more information contact the Department of Statistics, 103 Halsey Hall, P.O. Box 400135, Charlottesville, Virginia 22904-4135; (434) 924-3222; www.stat.virginia.edu.