Coursework

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)

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.