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Data Science

college of arts and sciences

course work

FSUS Interdisciplinary Data Science Master Study Curriculum is supplied exclusively on the University Tallahassee campus. Students will fill out a number of core courses that provide a solid starting point in mathematics, machine learning, statistics, data ethics and data science, as well as electives, along with a specific main area of ??the study.

All students take 18 credits of core coursework and 12 credits of election courses, which are selected by the student together with their advisory committee to define a specific growth science, computer science, mathematics or statistics.

Interdisciplinary Data Science Core Courses

  • Mathematics for Data Science (3)
  • Introduction to Data Science (3)
  • applied regression methods (3)
  • Learn Machine (3)
  • Data Mining (3)
  • Data ethics (2)
  • Professional Development Seminar (1)

The remaining 12 credits are election courses that effectively define the different majors. Choices are listed for each major.

Interdisciplinary Data Science: Accountability Science

Required elective compartments:

Restricted elective compartments (select 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)
  • Neuronal Differential Equations (3)

Free elective compartments:

Two courses from the voters offered by the others who participate in the IDS program.

Interdisciplinary Data Science: Computer Science

Required elective compartments:

  • advanced topics in data science (3)
  • Advanced Data Mining (3)

Restricted elective compartments:

A course in Cybersecurity, selected from the following, based on student backgrounds:

  • computer security bases for data science (3)
  • Computer Security (3)

A course from the following:

  • deep and reinforcing learning (3)
  • artificial intelligence (3)
  • parallel and distributed systems (3)
  • Computer Architectures (3)
  • Data and Computer Communication (3)
  • Computer and Network Management (3)
  • simultaneous, parallel and distributed programming (3)
  • advanced operating systems (3)
  • Database Systems (3)
  • Advanced Algorithms (3)
  • High Performance Computing (3)

Free elective compartments:

Interdisciplinary Data Science: Mathematics

Required elective compartments:

Restricted elective compartments (select at least three courses from the following):

  • principles and basics of machine learning (3)
  • numerical linear algebra (3)
  • numerical optimization (3)
  • Graphs and Networks (3)
  • Topological Data Analysis (3)

Free elective compartments:

  • advanced topics in data science (3)
  • Distributheorie 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 elective compartments:

Restricted elective compartments (select from the following courses):

  • Data Management and Analysis with SAS I (3)
  • Advanced Data Management and Analysis with SAS (3)
  • calculation methods in Statistics I (3)
  • Statistics in Applications i (3)
  • applied logistical regression (3)
  • Distributheorie and Inference (3)
  • Nonparametric Statistics (3)
  • applied multivariate analysis (3)
  • Time series and forecasting methods (3)
  • Introduction to Statistical Advice (3)
  • Object Data Analysis (3)

Free elective compartments (select a course from the following):

  • Data Visualization (3)
  • Cloud Computing (3)
  • artificial intelligence (3)
  • Graphs and Networks (3)

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