Program Overview
The Master of Data Science provides advanced training in statistical learning theory, advanced machine learning, big data analytics, and data science leadership. Students develop expertise in causal inference, Bayesian methods, advanced optimization, and research methodologies. The program emphasizes strategic analytics, data science architecture, ethical considerations, and innovation in data-driven decision making, preparing graduates for senior data science roles, research positions, analytics leadership, or doctoral studies in data science and related fields.
To develop data science leaders and researchers who can advance analytics capabilities, conduct rigorous research, architect enterprise data science solutions, and drive organizational strategy through advanced analytical insights while maintaining ethical standards and methodological rigor.
- Total Credit Hours: 36
- Core Courses: 24 credit hours
- Concentration/Electives: 9 credit hours
- Capstone/Thesis: 3 credit hours
- Delivery: 100% Online with research mentorship
- Duration: 12-24 months
- Format: Asynchronous with advanced research projects
Program Educational Objectives
Within 3-5 years of graduation, MDS graduates will:
- Hold senior positions as principal data scientists, analytics directors, or chief data officers
- Lead complex data science initiatives and research projects
- Publish research findings in academic journals and industry conferences
- Architect enterprise-scale data science platforms and solutions
- Drive analytics strategy and data-driven transformation in organizations
- Mentor junior data scientists and lead analytics teams
- Pursue doctoral education or become recognized thought leaders in data science
- Contribute to advancing state-of-the-art in statistical learning and data science methods
Program Curriculum
Course Code | Course Title | Credit Hours |
Core Courses (24 Credit Hours) | ||
MDS 601 | Advanced Statistical Learning Theory | 3 |
MDS 610 | Bayesian Data Analysis | 3 |
MDS 620 | Advanced Causal Inference | 3 |
MDS 630 | Deep Learning for Data Science | 3 |
MDS 640 | Advanced Time Series and Forecasting | 3 |
MDS 650 | Data Science Research Methodologies | 3 |
MDS 660 | Scalable Machine Learning and Big Data | 3 |
MDS 670 | Data Science Strategy and Leadership | 3 |
Concentration/Electives (9 Credit Hours) | ||
MDS 710 | Advanced Natural Language Processing | 3 |
MDS 720 | Reinforcement Learning for Decision Systems | 3 |
MDS 730 | Optimization Methods for Data Science | 3 |
Capstone/Thesis (3 Credit Hours) | ||
MDS 799 | Data Science Research Thesis | 3 |
Student Learning Outcomes
Upon completion of the Master of Data Science, graduates will be able to:
- Conduct independent data science research contributing novel methodological insights
- Apply advanced statistical learning theory to complex analytical problems
- Implement Bayesian methods for inference and decision making under uncertainty
- Design and analyze experiments for causal inference
- Develop scalable machine learning solutions for big data environments
- Architect enterprise data science platforms and infrastructure
- Lead data science teams and mentor junior practitioners
- Publish research findings in peer-reviewed venues
- Drive data science strategy aligned with organizational objectives
- Evaluate ethical implications of data collection, analysis, and deployment
Career Opportunities
Graduates of the Master of Data Science program may pursue positions including:
- Principal Data Scientist
- Data Science Research Scientist
- Director of Analytics
- Chief Data Officer
- Machine Learning Architect
- Quantitative Researcher
- Data Science Strategy Consultant
- Analytics and AI Manager
- Senior Statistical Analyst
- Data Science Product Manager
- Head of Data Science
- Research and Development Lead (Analytics)
