Curriculum

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Master of Science in Data Science

The M.S. in Data Science curriculum consists of four components: core courses, data science tools courses, data science applications courses, and an internship or capstone project. Students must complete at least 30 credits of graduate level courses to complete the degree. Their advisor must approve each student’s selection of courses.

The core courses and the Data Science Tool courses are discipline independent courses that teach the fundamental skills of data science. The Data Science Applications courses are specific to the various interdisciplinary domains supporting the program. Each academic unit offers courses relevant to their discipline, and students who are focused on applications will be advised to take a selection of courses that develops skills in one application area.

The internship or capstone project may be taken for 3 or 6 credits, depending on the scope of the project. Available internships span a variety of disciplines in data science, many of which are offered through the Miami Institute for Data Science and Computing’s Industrial Advisory Board.  Projects are done within one or two semesters, supervised by a faculty in an appropriate academic unit within the program. The project culminates with a report detailing the work done and knowledge gained, and a presentation to faculty and students in the program.

Technical Prerequisite

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  • CSC 6XX - Computing Mathematics for Data Science

    Computing and Mathematics for Data Science is a summer prerequisite course for students from a non-technical undergraduate backgrounds.  Calculus I and Statistics I content will be covered, as well as other foundational computing skills related to Data Science.

Curriculum

The M.S. in Data Science has four available concentrations.  However, declaring a concentration is not required.  For students who elect not to declare a concentration, the plan of study is as follows:

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  • Core - 9 Credits

    • 3 credits in Machine Learning or Data Mining
    • 3 credits in Data Visualization
    • 3 credits in Statistics

  • Data Science Tools - 12 Credits

    • 3 credits in Programming 
    • 9 credits in Database Systems, Data Visualization, Machine Learning and Data Mining, or Mathematics and Statistics

  • Data Science Applications - 6-9 Credits

    At least 6 credits must be taken in specified data science applications courses.

  • Internship, Project, or Capstone - 3-6 Credits

    Internship: This is a three- or six-month internship. Three-month internships are for 3 credits, and are done in either semester or the summer. Six-month internships are for 6 credits, and are done either from spring to summer or from summer to fall. The academic unit responsible for the student coordinates the internship with the program coordinator. The student is assigned an internship supervisor in the academic unit and also at the location of the internship. The internship culminates with a report detailing the work done and knowledge gained, and a presentation to faculty and students in the program. Appropriate courses codes will be created.

    Project: This is a semester long individual or small group project for 3 or 6 credits, depending on the scope of the project. Projects are done within one or two semesters. The student will have one or more supervisors within an appropriate academic unit in the program. The project culminates with a report detailing the work done and knowledge gained, and a presentation to faculty and students in the program. Appropriate courses codes will be created.

    Capstone: This is a 3 credit culminating course, integrating the knowledge and experience gained in the more specific courses of a track. The course will normally be offered by one of the units that provides the track. It may include lectures, surveys, project work, and other components.

Concentrations

The Master of Science in Data Science (MSDS) program offers four areas of concentration for students wishing to narrow their field of study. Click here to learn more.