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 linguistics, computational science, computer science, mathematics or statistics.
Interdisciplinary Data Science Core Coursework
- MAP 5196 - Mathematics for Data Science (3)
- CAP 5768 - Introduction to Data Science (3)
- STA 5207 - Applied Regression Methods (3)
- STA 5635 - Machine Learning (3)
- CAP 5771 - Data Mining (3)
- PHI 5699 - Data Ethics (2)
- STA 5910 - 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 Linguistics
Minimum of 12 credit hours of electives.
Required Electives:
- None
Restricted Electives (Choose at least two from the following):
- LIN 5XXX. Corpus Linguistics, Text Analysis, and Deep Learning (3)
- LIN 5727. Quantitative Methods in Language Research (3)
- LIN 5xxx. Computational Linguistics (3)
Free Electives (Choose at least one course from the following):
- LIN 5305. Patterns of Sounds (3)
- LIN 5937. Seminar on Language Invention (3)
- LIN 5703. Psycholinguistics I: Sentence Processing (3)
- LIN 5695. Psycholinguistics II: Lexical Processing (3)
- LIN 5xxx. Eye-tracking methodology (3)
In consultation with the advisor, other courses may count toward the Free Electives. These include, but are not limited to:
- Artificial Intelligence (3)
- Deep and Reinforcement Learning (3)
- High Performance Computing (3)
Interdisciplinary Data Science: Computational Science
Required Electives:
- None
Restricted Electives (Choose two or more courses from the following):
- ISC 5228 - Monte-Carlo Methods (3)
- ISC 5307 - Scientific Visualization (3)
- ISC 5305 - Scientific Programming (3)
- ISC 5315 - Applied Computational Science I (4)
- ISC 5318 - High-Performance Computing (3)
- ISC 5308 – Data Assimilation (3)
- ISC 5935 - Computational Probabilistic Modeling (3)
- ISC 5935 - Data Science for Health (3)
- ISC 5935 – AI Methods and Application (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 (Choose two courses from the following):
- CAP 5769 - Advanced Topics in Data Science (3)
- CAP 5778 - Advanced Data Mining (3)
- CAP 5XXX – Projects in Data Science (3)
Restricted Electives:
One course in Cybersecurity chosen from the following, based on student background:
- CIS 5379 - Computer Security Fundamentals for Data Science (3)
- CIS 5370 - Computer Security (3)
One course from the following:
- CAP 5619 - Deep and Reinforcement Learning (3)
- CAP 5605 - Artificial Intelligence (3)
- CDA 5125 - Parallel and Distributed Systems (3)
- CDA 5155 - Computer Architectures (3)
- CNT 5505 - Data and Computer Communications (3)
- CNT 5605 - Computer and Network Administration (3)
- COP 5570 - Concurrent, Parallel, and Distributed Programming (3)
- COP 5611 - Advanced Operating Systems (3)
- COP 5725 - Database Systems (3)
- COT 5405 - Advanced Algorithms (3)
- ISC 5318 - High Performance Computing (3)
Interdisciplinary Data Science: Mathematics
Required Electives:
- None
Restricted Electives (Choose at least three courses from the following):
- MAD 5XXX - Principles and Foundations of Machine Learning (3)
- MAD 5XXX - Numerical Linear Algebra (3)
- MAD 5420 - Numerical Optimization (3)
- MAD 5306 - Graphs and Networks (3)
- MTG 5356 - Topological Data Analysis (3)
- MAP 5345 – Partial Differential Equations (3)
Free Electives:
- CAP 5769 - Advanced Topics in Data Science (3)
- STA 5326 - Distribution Theory and Inference (3)
- STA 5166 - Statistics in Application I (3)
- STA 5167 - Statistics in Application II (3)
- MAD 5403 - Foundations of Computational Mathematics (3)
- MAD 5404 - Foundations of Computational Mathematics II (3)
- MAP 5345 - Partial Differential Equations (3)
Interdisciplinary Data Science: Statistics
Required Electives:
- None
Restricted Electives (Choose three courses from the following):
- STA 5066 - Data Management and Analysis with SAS I (3)
- STA 5067 - Advanced Data Management and Analysis with SAS (3)
- STA 5106 - Computational Methods in Statistics I (3)
- STA 5107 - Computational Methods in Statistics II (3)
- STA 5166 - Statistics in Applications I (3)
- STA 5238 - Applied Logistic Regression (3)
- STA 5326 - Distribution Theory and Inference (3)
- STA 5327 – Statistical Inference (3)
- STA 5507 - Applied Nonparametric Statistics (3)
- STA 5707 - Applied Multivariate Analysis (3)
- STA 5856 - Time Series and Forecasting Methods (3)
- STA 5939 - Introduction to Statistical Consulting (3)
- STA 6557 - Object Data Analysis (3)
Free Electives (Choose one course from the following):
- ISC 5305 – Scientific Programming (3)
- ISC 5307 - Scientific Visualization (3)
- ISC 5318 - High Performance Computing (3)
- CAP 5605 - Artificial Intelligence (3)
- MAD 5306 - Graphs and Networks (3)
Internship Course
IDS 5945* – Students must enroll in this course in order to get credit for internships. This is a zero-credit course that provides for proof of internship on the transcript.
*Graduate Zero Semester-Hour Courses and Fees
Students registered for zero semester hour graduate level courses and additional courses will not be charged for the zero-credit hour course. When registering for a zero-credit hour course only, the student will be charged for one semester hour at the Florida Resident tuition-rate of the course level. Zero credit courses do not count toward the 12 hours of elective credit, but will be reflected on the final transcript.