3.13. Major elective options for SDS
DATA Elective Courses
The SDS major requires students to choose at least one course from the following list of elective courses (see below for more detailed information about prerequisites, etc). We recommend that students discuss their choice of elective with their SDS faculty advisor.
- DATA 367 - Statistical Methods in Sports Analytics
- DATA 396T - Topics in Undergraduate Statistics & Data Science
- DATA 462 - Financial Math
- DATA 468 - Applied Stochastic Processes
- DATA 496T - Advanced Topics in Undergraduate Statistics & Data Science
- DATA 498H - Honors Thesis
- SIE 440 - Survey of Optimization Methods
These courses are generally available in spring semester, though we sometimes are able to offer 462 in summer.
Q. Can I substitute an upper-division course from another department as my DATA elective?
A. Unless it is SIE 440, then no. We have an agreement with SIE to utilize this course, but it is the only one. Note that many other related courses on campus may fulfill application course requirements for the SDS major.
Q. Can I substitute a graduate level course (500-level) from another department as my DATA elective?
Maybe. First, you would need to be eligible to take the course. Second, you would need to have the course looked at for appropriateness of level and content. Email the Math Center with specifics on the course and we'll help sort this out.
DATA 367 - Statistical Methods in Sports Analytics
Usually offered: Spring
Prerequisite: MATH 129 or above
Description: This course will introduce statistical methods and training in statistical consulting aimed to analyze sports by using observational data on players and teams. With an emphasis on statistical inference and modeling, the students will learn how to analyze a sports related problem, utilize statistical tools to find a solution and interpret those results to sports professionals. The course will also offer the opportunity to focus on a semester long sports analytics project in partnership with a University of Arizona athletics team.
DATA 396T - Topics in Undergraduate Statistics & Data Science and DATA 496T - Advanced Topics in Undergraduate Statistics & Data Science
Offered occasionally in spring (Topics course offerings depend upon proposals from faculty and availability of resources to run them).
Prerequisites vary; 496T will typically require at least 313 or 363, while 396T would usually have a lower prerequisite.
When a topics course has been approved, information will be posted on our website at https://www.math.arizona.edu/academics/courses/ugtopics
DATA 462 - Financial Math
Usually offered: Spring; sometimes offered in summer.
Prerequisite: MATH 223
Description: Analysis of cash flows from an actuarial viewpoint. Interest theory, annuities, bonds, loans, and related fixed income portfolios, rate of return, yield, duration, immunization, and related concepts.
DATA 468 - Applied Stochastic Processes
Usually offered: Spring
Prerequisite: MATH 464
Description: Applications of Gaussian and Markov processes and renewal theory; Wiener and Poisson processes, queues.
DATA 498H - Honors Thesis
Note: the honors thesis, 498H, is completely unrelated to DATA 498A. DATA 498A is a standard class run by one faculty member with a classroom full of students that has a project as its centerpiece; DATA 498H has no classroom component and is arranged by the individual student with a faculty member of their choosing as supervisor. It is not possible to substitute 498H for 498A.
Usually offered: Fall, Spring; can also be taken in Summer if needed.
Prerequisite: admission to the Franke Honors College; submission of thesis prospectus; the thesis is to be completed during the student's final two semesters of study.
See also our Honors Thesis page.
SIE 440 - Survey of Optimization Methods
Usually offered: Spring
Prerequisite: UAccess enrollment requires that students have advanced standing within Engineering and completion of SIE 340. However, interested students with appropriate background may contact the SIE advisor to request enrollment. Students should have background in optimization/linear programming. We have heard from SDS students in the past that some of the techniques that are considered prerequisites for this course may have been taught using different terminology; our students have felt sufficiently prepared for the course, and also reported that it was "appropriately challenging".
Description: Survey of methods including network flows, integer programming, nonlinear programming, and dynamic programming. Model development and solution algorithms are covered.