2024-2025 Academic Catalog 
    
    Nov 14, 2024  
2024-2025 Academic Catalog

Data Science, MSDS


Program Description

The MSDS program develops knowledge and skills across the disciplines of computer science and statistics. Graduates will understand the theory and design of statistical methods and models, as well as computing systems and algorithms, empowering them with the ability to make informed decisions in their exploration and testing of data. They will devise methods, systems, and architectures for information discovery, and have the foundations to learn and understand state-of-the-art developments in this rapidly evolving field.

Admission Requirements

Students must have a Bachelor of Science or Arts in any degree from a regionally accredited academic institution. The transcripts of the student must convey a 3.0 GPA average across two semesters of calculus-based mathematics courses, one semester of programming, one semester of statistics at any level, and one semester in applied linear algebra. An overall undergraduate GPA of 3.0 is also required for regular graduate status. Students may be admitted conditionally if they have an undergraduate grade point average of 2.7 or above or are lacking two semesters of calculus-based mathematics, one semester of programming, one semester of statistics, or one semester of applied linear algebra. Students may also replace missing prerequisite courses or demonstrate competency in the face of a low GPA, with a verified certificate of completions in calculus, programming, statistics, linear algebra, or foundations of data science from a massively open online course hosted by Coursera, EdX, or Udacity. Students conditionally admitted by lack of background courses will be required to complete some additional course work beyond the requirements of the MSDS degree program.

Learning Outcomes

Students graduating from this program will have the ability:

  • To process and analyze data effectively
  • Develop models to represent the data and understand its nature
  • Gain insight into the data
  • Utilize a variety of tools to analyze data including machine learning and visualization

Project Option (6 Credit Hours)


Thesis Option (9 Credit Hours)


Co-op or Internship (6 Credit Hours)