- Overview
- Learning Outcomes
- Requirements
- Curriculum Details
Program Purpose
The Master of Science in Data Science program seeks to produce students with advanced theory and methods of data science, with the ability to apply their knowledge and methods to solve practical problems in their field of interest.
Educational Objectives
The Master of Science in Data Science program aims to:
- Have in-depth understanding of the key theories and methodologies in data science, with focus on the areas of statistics, data mining, and machine learning.
- Be fluent in statistical programming languages and big data tools through coursework, projects and applied research.
- Be able to analyze problems and make data-driven decisions with professionalism in real world settings.
Faculty
Program Learning Outcomes
After completing this program, students will:
- Have in-depth understanding of the key theories and methodologies in data science, with focus on the areas of statistics, data mining, and machine learning.
- Be proficient in statistical programming languages and big data tools through coursework, projects, and applied research.
- Be able to analyze problems and make data-driven decisions with professionalism in real world settings.
Admissions Requirement
Institutional-wide Admission Criteria
- Completion of undergraduate degree
- Official Transcript: allow for evaluation of academic performance, relevant coursework, and overall readiness for college-level study.
- Personal Statement: helps reviewers understand the applicant’s motivations and aspirations to pursue the program of study.
- CV: presents the academic and professional history of the applicant
- Letters of Recommendation: Letters of recommendation from teachers, mentors, or professionals familiar with the applicant’s abilities and potential, and additional insights into the applicant’s character, work ethic, and potential for success in the program.
Program-specific Criteria
All applicants to the MS in Data Science are required to have an undergraduate degree in data science, statistics, computer science, applied mathematics, or another major with adequate quantitative background. Prior quantitative coursework (calculus, linear algebra, statistics, etc.) and prior computer programming and theory coursework are required.
Graduation Requirement
- The academic requirements for graduation are the successful completion of the curriculum with a grade point average of no less than 2.7.
- In addition, a graduate must have taken at least 50% of all courses from FTC Northern.
Curriculum Overview
The MS in Data Science is a 36-semester credit curriculum with three major components: core requirements, electives, and a capstone project.
| Area | Credits |
|---|---|
| Core Requirements | 15-21 |
| Electives | 9-15 |
| Capstone | 6 |
| Total Required Credits for Graduation | 36 |
Curriculum Details
The program requirements are comprised of foundations for Data Science (6 credits), statistics (3 credits), data analytical tools (3 credits), data mining and machine learning (6 credits), ethics in Data Science (3 credits), electives, and a capstone project.
Course List for MS in Data Science
| Code | Course Title | Credits | Prerequisite(s) |
|---|---|---|---|
| Core Requirements (15-21 cr) | |||
| DAS501 | Mathematical Foundation for Data Science* | 3 | None |
| COS501 | Computational Foundation for Data Science* | 3 | None |
| DAS502 | Probability for Data Science | 3 | DAS501, COS501 |
| DAS522 | Exploratory Data Analysis and Visualization | 3 | DAS501, COS501 |
| DAS541 | Data Mining for Business | 3 | DAS501, COS501 |
| COS536 | Applied Machine Learning | 3 | DAS541 |
| DAS548 | Ethics in Computer and Data Science | 3 | None |
| Electives (9-15 cr) Select from the following | |||
| DAS512 | Statistical Inference and Modeling | 3 | DAS502 |
| COS531 | Modern Applied Statistical Learning | 3 | DAS502 |
| COS541 | Big Data and Data Engineering | 3 | None |
| COS643 | Computer Vision and Natural Language Processing | 3 | COS536 |
| STA511 | Advanced Regression Analysis | 3 | DAS501 |
| DAS631 | Generative AI: Foundation and Application | 3 | COS536 |
| STA521 | Design and Analysis of Experiments | 3 | DAS502 |
| STA541 | Survival Analysis | 3 | DAS512 |
| Capstone Project (6 cr) | |||
| DAS761 | Capstone Project | 6 | Department Approval |
| Total Credits Required for Graduation | 36 | ||
* Can be exempt upon meeting certain criteria and with permission from the department. For each exemption one extra elective needs to be taken to meet the requirement for graduation.
