Program Overview
Program Type
Minor
Credit Hours
15
The minor in Statistics and Data Science gives students experience manipulating, summarizing, and visualizing data and applying statistical and machine learning methods. Students are exposed to the principles of and tools for conducting reproducible data analysis and are taught to think critically about relevant ethical issues (e.g., data privacy, misrepresentation of findings). The program bolsters students' training in statistics and data science to enhance their preparation in their major field as well as strengthen their position to enter the workforce or graduate school.
SDS majors are not eligible to receive credit for the minor. However, students who drop the major may be able to count certain courses toward the minor (see below).
Prerequisites
The SDS minor is open to all UT undergraduate students. To be successful in the minor, students should have sufficient background in mathematics such that they could succeed in a collegelevel calculus course. For example, they should have earned an appropriate score on the Mathematics placement exam to place into calculus, or completed Mathematics 305G with a grade of at least B, or a mathematics exam (e.g., AP, CLEP) credit accepted by UT Austin, or a course transfer credit accepted by UT Austin for a calculus course or Math 305G equivalent. While calculus is not a prerequisite for the minor, it may be required for some of the elective courses.
Requirements
The minor requires 15 semester hours of coursework with a grade of at least a C in each course.
No transfer credits, creditbyexam, or CR courses may be used to fulfill minor course requirements. At least nine of the hours used towards the minor cannot also satisfy major degree requirements. The following courses are required:

1
Three core courses in each of the following areas (9 credit hours total):
Statistics

SDS 320E Elements of Statistics/320H Honors Statistics
Note: creditbyexam for AP statistics, SDS 301, and SDS 302F may not be used to fulfill this requirement.
Data Science
Programming


2
Two additional elective courses, one each from any two of the following areas (6 credit hours total):
Regression/Statistical Modeling
Machine Learning
 SDS 326E Elements of Statistical Machine Learning or SDS 323 Statistical Learning and Inference (no longer offered)
Databases
Probability/Mathematical Statistics/Statistical Inference
 SDS 321 Introduction to Probability and Statistics, M 362 K Probability I or M 378K Mathematical Statistics
Data Visualization

SDS 366 Data Visualization in R or SDS 375 Topic: Data Visualization in R (no longer offered)
Note: Students who have taken SDS major restricted courses including SDS 313, SDS 315, SDS 341, SDS 334, and SDS 336 and who are no longer pursuing the SDS major may use those courses to fulfill minor requirements.