FSU’s Interdisciplinary Data Science Master’s Degree Program curriculum is delivered exclusively at the university’s Tallahassee campus. Students will complete a series of core courses that provide a solid starting point in mathematics, machine learning, statistics, data ethics, and data science, along with electives that support a specific major area of study.
All students take 18 credits of core coursework and 12 credits of elective courses, selected by the student together with their advisory committee, to define a specific major — computational science, computer science, mathematics or statistics.
Interdisciplinary Data Science Core Coursework
 Mathematics for Data Science (3)
 Introduction to Data Science (3)
 Applied Regression Methods (3)
 Machine Learning (3)
 Data Mining (3)
 Data Ethics (2)
 Professional Development Seminar (1)
The remaining 12 credits are elective courses, which effectively define the different majors. Elective choices are listed for each major.
Interdisciplinary Data Science: Computational Science
Required Electives:
 None
Restricted Electives (Choose two or more courses from the following):
 MonteCarlo Methods (3)
 Scientific Visualization (3)
 Scientific Programming (3)
 Applied Computational Science I (4)
 HighPerformance Computing (3)
 Cloud Computing (3)
 Probabilistic Programming (3)
 Neural Differential Equations (3)
Free Electives:

Two courses from among the electives offered by the other majors participating in the IDS program.
Interdisciplinary Data Science: Computer Science
Required Electives:
 Advanced Topics in Data Science (3)
 Advanced Data Mining (3)
Restricted Electives:
One course in Cybersecurity chosen from the following, based on student background:
 Computer Security Fundamentals for Data Science (3)
 Computer Security (3)
One course from the following:
 Deep and Reinforcement Learning (3)
 Artificial Intelligence (3)
 Parallel and Distributed Systems (3)
 Computer Architectures (3)
 Data and Computer Communications (3)
 Computer and Network Administration (3)
 Concurrent, Parallel, and Distributed Programming (3)
 Advanced Operating Systems (3)
 Database Systems (3)
 Advanced Algorithms (3)
 High Performance Computing (3)
Free Electives:
 None
Interdisciplinary Data Science: Mathematics
Required Electives:
 None
Restricted Electives (Choose at least three courses from the following):
 Principles and Foundations of Machine Learning (3)
 Numerical Linear Algebra (3)
 Numerical Optimization (3)
 Graphs and Networks (3)
 Topological Data Analysis (3)
Free Electives:
 Advanced Topics in Data Science (3)
 Distribution Theory and Inference (3)
 Statistics in Application I (3)
 Statistics in Application II (3)
 Foundations of Computational Mathematics (3)
 Foundations of Computational Mathematics II (3)
Interdisciplinary Data Science: Statistics
Required Electives:
 None
Restricted Electives (Choose three courses from the following):
 Data Management and Analysis with SAS I (3)
 Advanced Data Management and Analysis with SAS (3)
 Computational Methods in Statistics I (3)
 Statistics in Applications I (3)
 Applied Logistic Regression (3)
 Distribution Theory and Inference (3)
 Applied Nonparametric Statistics (3)
 Applied Multivariate Analysis (3)
 Time Series and Forecasting Methods (3)
 Introduction to Statistical Consulting (3)
 Object Data Analysis (3)
Free Electives (Choose one course from the following):
 Data Visualization (3)
 Cloud Computing (3)
 Artificial Intelligence (3)
 Graphs and Networks (3)