Master of Science in Operations Research and System Analytics

Program Overview

Syracuse University's Master of Science in Operations Research and System Analytics combines mathematical modeling, computer programming, data science, and business analytics to solve complex problems. The program emphasizes hands-on experience through a capstone project and fosters diversity and interdisciplinary collaboration. Graduates develop exceptional analytical and problem-solving skills, preparing them for careers in various domains.

Program Outline

Degree Overview:

The Master of Science in Operations Research and System Analytics from Syracuse University uniquely combines mathematical modeling, computer programming, data science, and business analytics to solve significant problems in various domains. The program emphasizes hands-on experience through a required capstone project and is designed for students with undergraduate degrees in any STEM field, fostering diversity and interdisciplinary collaboration. Its applied operations research focus with a computer science and artificial intelligence flavor provides graduates with exceptional analytical and problem-solving skills.

Objectives:

• To equip students with the ability to apply operations research models and methods to identify, formulate, and solve complex problems in engineering systems.
• To foster an understanding of mathematical programming principles for informed holistic decision-making with consideration for societal, economic, and environmental impact.
• To develop proficiency in scientific tools for efficient systems operation with uncertainty and performance prediction.
• To impart skills in descriptive, predictive, and prescriptive analytics using data-driven approaches and effective communication of outcomes.
• To integrate concepts from mathematics, programming, and engineering for system design and optimization, trade-off analysis, and result interpretation for engineering practice.

Outline:

Program Curriculum:

• Mathematical Background (2 courses):
• Linear Algebra and Applications (ELE 603)
• Introduction to Probabilistic Models (ELE 606)
• Operations Research Topics (2 courses):
• Optimization Techniques (MAE 630)
• Stochastic Modeling and Applications (ECS 629)
• Machine Learning Topics (1 course):
• Machine Learning Algorithms (CIS 662)
• Electives (4 courses):
• Students can choose from a range of courses in areas such as artificial intelligence, machine learning, engineering management, and industrial engineering to tailor the program to their interests and skills.
• Industrial Project Experience (1 course):