Data Science MSDS
Degree Awarded: MS in Data Science (MSDS)
Minimum Required Credits: 30.0
Co-op Option: Available for full-time, on-campus master's-level students
Classification of Instructional Programs (CIP) code: 30.7001
Standard Occupational Classification (SOC) code: 27-3043
About the Program
The Master of Science in Data Science (MSDS) Program is designed for students with strong undergraduate grades and technical skills coming from diverse disciplinary backgrounds, and it prepares students to meet the challenges presented by the explosive growth of very large scale and complex data sources. Data from commercial activities or sources such as social media or scientific instrumentation constantly create new problems requiring data-driven solutions, while simultaneously opening opportunities for innovation. The MSDS Program helps students develop the knowledge and skills needed to address these opportunities that benefit individuals, organizations, and society.
Additional Information
For more information about this program, please contact the School of Computer and Information Sciences
Degree Requirements
| Core Courses | ||
| IS 5310 | Introduction to Data Science | 3.0 |
| IS 5320 | Principles of Data Analysis | 3.0 |
| IS 5321 | Principles of Optimization and Inference | 3.0 |
| IS 5330 | Applied Machine Learning | 3.0 |
| IS 6311 | Data Workflow Automation and Pre-Processing | 3.0 |
| IS 6825 | Information Ethics and Policy | 3.0 |
| Electives | ||
| Select four (4) courses from the following options: | 12.0 | |
Any IS (Information Science) course in the ranges 5300-5499, 6300-6499, 7300–7499, or 8300–8499 | ||
Any IS (Information Science), CS (Computer Science), or SE (Software Engineering) course at the 5000-7999 level with IS department approval | ||
Any independent study, special topics, research, or thesis coursework with IS department approval | ||
| Optional Co-op Experience | ||
| Co-op is an option for this degree for full-time on-campus students. Students choosing this option will be required to complete COOP 5000 as preparation for their co-op experience. | ||
| Total Credits | 30.0 | |
Program Learning Outcomes
- Use data to provide quantitative insights on questions of scientific, organizational, and social interest.
- Collaborate, communicate, and function effectively on data science projects in multidisciplinary teams.
- Apply knowledge of Data Science (DS) fundamentals to analyze and solve complex problems.
- Identify, formulate, and design efficient and effective solutions using appropriate DS processes and paradigms.
- Design, implement, test, and maintain different DS components, systems, or programs to meet desired needs.
- Identify, formulate, and solve DS problems with the techniques, skills, and modern DS tools necessary in practice for application domains.
- Engage in continuous learning to keep pace with evolving tools, technologies, and methodologies in DS