Data Science (Graduate Certificate)
This certificate program prepares students for the data science field in business and government, which is expanding locally and globally. The field continues to have high job growth, and many job opportunities are available in the Washington metropolitan region.
Upon successful completion of the program, students will be able to:
- Evaluate the data management associated with Web-based applications.
- Critically evaluate available scripts and modify and enhance them.
- Analyze relevant factors to select and utilize data science techniques in extracting and identifying useful information and knowledge.
Admission Requirements: In addition to the university's requirements for graduate school admission, the applicant should hold a bachelor’s degree in a field such as information technology, computer science, or a related field, or hold a degree in an unrelated field with at least two years of experience in an information technology position. Title IV federal financial aid is not available for the certificate program unless it is included in the M.S. in emerging technology program.
Application information for current graduate students: Current graduate College of BILT students pursuing graduate master’s BILT programs at the University may apply to add a certificate to their program of study through the Dean’s Office.
Course Substitution Policy: If a student can demonstrate a depth of prior experience, industry certification, or graduate data science courses, 500-level courses can be substituted by higher-level courses.
Program Completion Requirements: Twelve (12) credits of coursework must be completed at Marymount University within three years of the date of matriculation.
Certificate Requirements
12 credits
Required Courses
| IT 540 | Enterprise Data Management and Analysis | 3 |
| IT 546 | Principles of Data Science | 3 |
| IT 566 | Computer Scripting Techniques | 3 |
Electives
Take one (1) elective from the following:
| IT 556 | Data Visualization | 3 |
| IT 576 | Natural Language Processing (NLP) Techniques | 3 |
| IT 586 | Machine Learning | 3 |