The Interdisciplinary Data Science Master's Degree Program has five majors: computational linguistics, computational science, computer science, mathematics and statistics. The M.S. degree can open up a wealth of career options, enabling graduates to enter a rapidly growing and vibrant sector of the economy with a job market that offers a breadth of opportunities. Our students will acquire skills in high demand by governmental agencies and a range of industries seeking to capitalize on the era of big data, machine learning and artificial intelligence.
The mathematics major in data science emphasizes the mathematical underpinnings of data science along with data analysis, computation and visualization. Students will learn the mathematical background necessary to understand how and why many machine learning and data analysis algorithms work. A mathematical perspective on the subject leads to a deeper understanding of the methodology and tools of data science and will prepare students to analyze and visualize data, design new tools and adapt existing methods and algorithms when necessary. In addition to a set of core courses, the curriculum includes advanced courses on topics such as graphs and networks, numerical linear algebra, numerical optimization and topological data analysis.
The data science M.S. degree with a major in mathematics requires 30 credit hours of coursework. There is a core curriculum of 18 credit hours all data science students take. In addition, students who major in mathematics take at least 9 credit hours from mathematics. The rest of the credit hours can be taken from the participating departments.
Students interested in taking an elective class that is not listed here should contact Tom Needham.
Course Requirements
All data science graduate students must pass the following courses:
Core Requirements: (18 credit hours)
- MAP 5196 - Mathematics for Data Science (3 Hrs, Fall)
- CAP 5768 - Introduction to Data Science (3 Hrs, Fall)
- STA 5207 - Applied Regression Methods (3 Hrs, Fall)
- STA 5910 - Professional Development Seminar (1 Hr, Fall)
- STA 5635 - Machine Learning (3 Hrs, Spring)
- CAP 5771 - Data Mining (3 Hrs, Spring)
- PHI 5699 - Data Ethics (2 Hrs, Spring)
Math Major Requirements: (At least 9 credit hours. Any substitutions to the courses below require the approval of associate chair for graduate studies.)
- MAD 5XXX - Principles and Foundations of Machine Learning (3 Hrs, Fall)
- MAD 5XXX - Numerical Linear Algebra (3 hrs, Fall or Spring)
- MAD 5420 - Numerical Optimization (3 Hrs, even Fall)
- MAD 5306 - Graphs and Networks (3 Hrs, Fall)
- MAP 5345 - Elementary Partial Differential Equations I (3 Hrs, Fall or Spring)
- MTG 5356 - Topological Data Analysis (3 Hrs, Spring)
- MAP 5XXX - Stochastic Computing and Optimization (3 Hrs, Spring)
- MAP 5611 - Introduction to Computational Finance (3 Hrs, Spring)
After satisfying the core and math major requirements, students who need additional credit hours can take more courses from the above list or the electives below:
- CAP 5769 - Advanced Topics in Data Science (3 hrs)
- ISC 5318 - High Performance Computing (3 hrs)
- STA 5326 - Distribution Theory and Inference (3 hrs)
- STA 5166 - Statistics in Application I (3 hrs)
- STA 5167 - Statistics in Application II (3 hrs)
- MAD 5403 - Foundations of Computational Mathematics (3 hrs)
- MAD 5404 - Foundations of Computational Mathematics II (3 hrs)
A schedule to complete the degree in three semesters:
Fall Year 1
- Mathematics for Data Science (3) MAP 5196
- Introduction to Data Science (3) CAP 5768
- Applied Regression Methods (3) STA 5207
- Professional Development Seminar (1) STA 5910
Spring Year 1
- Machine Learning (3) STA 5635
- Data Mining (3) CAP 5771
- Data Ethics (2) PHI 5699
- Math elective (3)
Fall Year 2
- Math elective (3)
- Math elective (3)
- Math elective (3)
Required Background
Students must have earned a bachelor's degree from an accredited institution and have a minimum of 3.0 GPA (B or better average) on the last 60 hours of undergraduate credits. Students must be in good standing at the institution of higher learning last attended.
The following math courses are required prerequisites for admission to the math major.
- Calculus I, II and III (multivariate calculus)
- Differential equations
- Linear algebra
- Probability and statistics
Students should also have some experience in a programming language, such as C, C++, Fortran, Python, Julia, Matlab.
Recommended Background
In addition to the required background, students are recommended to study numerical methods of mathematics, such as a course on numerical methods, numerical analysis or scientific computing.
As a part of their application, students will provide a statement of intent and CV, or résumé; and identify three recommenders who will write recommendation letters discussing the student's aptitude for graduate study. Visit the Office of Admissions website to apply.
The mathematics department is a sponsoring member of the Erdős Institute, an organization which provides resources for STEM graduate students and postdocs interested in utilizing data science tools in their research and/or jobs in industry. Graduate students and postdocs in the mathematics department have the opportunity to enroll in the Erdős Institute programming, which allows access to many excellent resources for learning about data science, career planning and networking. Erdős Institute programming includes: data science boot camps, providing training in general data science techniques in Python; minicourses on specialized data science topics; career exploration seminars; 1-on-1 career coaching; behavioral and technical interview prep workshops; and employer and alumni career connections. Contact Tom Needham, tneedham@fsu.edu, the Erdős Institute faculty representative at FSU and the faculty advisor for the math-data science program, for details.