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 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):

  • Monte-Carlo Methods (3)
  • Scientific Visualization (3)
  • Scientific Programming (3)
  • Applied Computational Science I (4)
  • High-Performance 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)
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