5. B.A in Statistics & Data Science
Undergraduate Requirements Archive → 2025/2026 Undergraduate Requirements - under construction → B.A in Statistics & Data Science
The information and requirements given here apply to the 2025–2026 catalog. For other catalog years, please consult the archive.
Statistics and Data Science Supporting Requirements
The following requirements are part of the B.A. (Bachelor of Arts) degree, but as supporting requirements, do not count in the major units or GPA.
Language Requirement
Students must demonstrate fourth semester proficiency in a second language for the B.A. degree.
Minor Requirement
SDS majors are required to complete a minor in any subject outside of the math department. A second major outside our department may fulfill this requirement provided it is declared within the same B.A. degree.
Supporting Python Programming Course Requirement
All SDS majors are required to complete a computer programming course in Python, regardless of the degree selected. Enrollment in DATA 363 requires successful completion of a Python programming course, so students have programming background to help them pick up R and to ensure they are getting Python experience along the way. Choose one course:(*)
(*)Either CSC 110 or ISTA 130 is recommended for most students; CSC 120 or CSC 250 will also satisfy the requirement if available. As an alternative, qualified students may complete both ((ECE 175 or ECE 101) AND BE 205) or both ((ECE 175 or ECE 101) AND CHEE 205). Contact the Math Center if you need any of these alternative courses pulled into your advisement report.
Supporting Data Management/SQL Requirement
SDS graduates applying for jobs will find that SQL and Data Management skills are important. Enrollment in DATA 363 requires completion of or concurrent enrollment in a course that teaches SQL and Data Management. These skills will be helpful as students work on their projects in DATA 363. Take one course:
- ISTA 322— Data Engineering
Statistics & Data Science Major Requirements
Core Courses
- MATH 122A AND MATH 122B (1) or MATH 125— Calculus I
- MATH 129— Calculus II
- DATA 201 — Foundations of Data Science(*)
- MATH 263 — Introduction to Statistics and Biostatistics
- MATH 313 — Introduction to Linear Algebra (2)
- DATA 363— Introduction to Statistical Methods (3)
- DATA 375— Introduction to Statistical Computing
- DATA 467 — Applied Linear Models (4)
- DATA 474— Introduction to Statistical Machine Learning
- DATA 498A — Capstone for Statistics and Data Science (5)
- One statistics major elective (upper or lower-division)
- Two upper-division statistics major electives.
(*)DATA 201 is a new Building Connections General Education course. Up to 3 courses may count to fulfill General Education Exploring Perspectives or Building Connections requirements as well as major, pre-major, minor, and/or certificate requirements.
(1)MATH 122A and MATH 122B are a single-semester sequence of courses that cover Calculus I.
(2) Either MATH 313 or MATH 310 may be used to fulfill this requirement in the SDS major. However, please note that 310 is no longer offered at UArizona. Students who completed MATH 215 prior to fall 2015 or who have transfer credit equivalent to MATH 215 will still fulfill this requirement, though they will not earn upper-division credit for the course.
(3) As of Fall 2024, the enrollment requirements for DATA 363 have changed. Students must complete MATH 129 and a Python course (CSC 110 or ISTA 130) prior to 363. In addition, MATH 313 and an SQL course (ISTA 322) must either be complete or in-progress in the same term as 363.
(4) DATA 467 is offered in-person in fall semesters. Provided we have sufficient enrollment, we plan to offer it online each spring.
(5) DATA 498A will usually be taken in a fall semester.
Statistics & Data Science Major Elective Courses
The SDS major requires three elective courses, at least two of which must be upper-division. The courses that will be accepted toward this requirement are listed below.
Course options:
- DATA 367— Statistical Methods in Sports Analytics
- DATA 396T— Topics in Undergraduate Statistics & Data Science(1)
- DATA 412 — Linear Algebra for Data Science
- DATA 439 — Statistical Natural Language Processing
- DATA 462— Financial Math
- DATA 468— Applied Stochastic Processes
- DATA 496T— Advanced Topics in Undergraduate Statistics & Data Science(1)
- DATA 498H — Honors Thesis(2)
- GEOG 457 — Statistical Techniques in Geography, Regional Development and Planning
- INFO 402— Data Ethics
- ISTA 320— Applied Data Visualization
- ISTA 321— Data Mining and Discovery
- ISTA 410— Bayesian Modeling and Inference
- MATH 223— Vector Calculus
- MATH 464— Theory of Probability
- MATH 466— Theory of Statistics
- PHIL 206 — Ethics of Artificial Intelligence(3)
- PHIL 346 — Minds, Brains and Computers
- PHIL 455 — Philosophy and Artificial Intelligence
- SIE 440— Survey of Optimization Methods
- WFSC 223 — Dealing With Data in the Wild(3)