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2023-2024 Graduate Bulletin

Graduate Program in

Data Science

College of Arts and Sciences


Director: Gordon Erlebacher; Professors: Burmester, Chakraborty, Liu, Schwartz, Tyson, Wang, Yang, Zhao (Computer Science); Bertram, Gallivan, Mio (Mathematics); Beerli, Erlebacher, Meyer-Baese, Shanbhag, Wang (Scientific Computing); Barbu, Huffer, Niu, Slate (Statistics); Assistant Professors: Gubanov, Liu, Mallory, Wang (Computer Science); Lee, Needham (Mathematics); Ward (Philosophy); Dexter, Zavala Romero (Scientific Computing); Loyal (Statistics)

Program Overview

The Florida State University College of Arts and Sciences and the Departments of Computer Science, Mathematics, Scientific Computing, and Statistics offer a Master's of Science Degree in Interdisciplinary Data Science (MS-IDS) that provides students a unique and broad educational experience across the four foundational areas of Data Science. The program consists of 1) a common core of 18-credit course work, and 2) at least four additional three- or four-credit electives that define a major in one of the participating areas. The program requires a minimum of 30 credits and can be completed in three academic semesters. Additional information can be found at and on the individual departmental websites.

Admission Requirements

Students interested in applying to this program are encouraged to review all University and college-wide degree requirements summarized in the "College of Arts and Sciences" chapter of this Graduate Bulletin.

The MS-IDS graduate program will appeal directly to students with undergraduate degrees in math, computer science, computational science, or statistics. It will attract students with less traditional backgrounds, e.g., physics or engineering. Therefore, the admissions requirements are designed to select students with solid training in mathematics, statistics, and computer science common across a broad range of undergraduate degrees. In addition to meeting all the University and College admission requirements for graduate study, each applicant for the MS-IDS program must:

  • Have earned a Bachelor's degree from an accredited institution and possess a minimum background consisting of mathematics through Calculus 2 (MAC 2312 or equivalent), Introductory Statistics (STA 2122 or equivalent), and experience with at least one object-oriented programming language, preferably Python or R. Previous coursework in linear algebra is desirable but not mandatory;
  • Have a minimum expected GRE score of 146 Verbal and 155 Quantitative;
  • Have a minimum of 3.0 GPA (B or better average) on the last 60 hours of undergraduate credits;
  • Be in good standing at the institution of higher learning last attended; and
  • Provide two letters of recommendation discussing the student's aptitude for graduate study.

Enrollment in some elective courses may require additional background beyond these admission requirements. Students must demonstrate that background in their undergraduate transcripts or additional coursework may be required. Further information is available on the MS-IDS web site, in the Bulletin Sections of the participating Departments, and on the four departmental websites: Computer Science, Mathematics, Scientific Computing, and Statistics.

Graduation Requirements

All students in this course-based Master's degree program will complete 30 credit hours consisting of 18 hours of core courses and 12 additional hours of coursework that define a specific major. The 18 hours of core courses consist of:

MAD 5196 (3 credits, Mathematics for Data Science)

COP 5768 (3 credits, Introduction to Data Science)

STA 5207 (3 credits, Applied Regression Methods)

STA 5635 (3 credits, Machine Learning)

CAP 5771 (3 credits, Data Mining)

PHI 5699 (2 credits, Data Ethics)

STA 5910 (1 credit, Professional Development Seminar)

The 12-hour additional coursework consists of at least four major-specific graduate courses. Course descriptions and their prerequisites, along with departmental electives, are found in the Bulletin entry for the department that offers them (Computer Science, Mathematics, Scientific Computing, and Statistics).