M.S. in Data Science in Operations Research
The M.S. in Data Science in Operations Research (DSOR) program emphasizes fundamentals in the areas of optimization, statistics, computing, data analysis, and communication. The program enrolls students with backgrounds in engineering, applied or pure mathematics, computer science, statistics, or basic sciences. Students in the DSOR program learn how to use data to generate practical business insights and make decisions.
Data Science in Operations Research (DSOR) Curriculum
The goal of learning from data is to make better decisions, and this objective lies at the heart of our M.S. in Data Science in Operations Research program. In addition to descriptive and predictive methods, our master's program emphasizes prescriptive methods that help decision-makers select appropriate courses of action.
The core curriculum is grounded in the methodology of Operations Research/Industrial Engineering and includes courses from Computer Science and Statistics, as well as a flexible choice of electives. Students receive training from engaged, energetic faculty in a full range of skills to implement, analyze, and improve data-oriented engineering and business processes.
Students enter the three-semester (16-month) program in the fall semester and typically finish the program in the subsequent fall semester. Through rigorous coursework, students build a strong foundation in data-driven methodologies for model building, decision making, and communication of results.
Each student in the DSOR program puts these skills to use in a real-world setting by completing an exciting semester-long Capstone project. Projects are completed in small teams of two to four students. Each project has an industry sponsor, and is supervised by both an industry advisor and a faculty advisor. Projects allow students to interact closely with their two advisors and the industry sponsor.
Program Requirements
The M.S. in Data Science in Operations Research degree requires 30 credits of coursework. The required courses are IE 5531, IE 5532, IE 5561, IE 5773, IE 5801, STAT 5302, and CSCI 5521 or CSCI 5523. Non-native English speakers must also take ESL 5008, which is not counted toward the 30-credit requirement. The recommended course sequence is listed below.
Approved Electives for M.S. in DSOR
| IE 5441—Financial Decision Making | STAT 5303—Designing Experiments |
| IE 5522—Quality Engineering and Reliability | STAT 5401—Applied Multivariate Methods |
| IE 5541—Project Management | STAT 5421—Analysis of Categorical Data |
| IE 5545—Decision Analysis | STAT 5511—Time Series Analysis |
| IE 5551—Production and Inventory Systems | STAT 5601—Nonparametric Methods |
| IE 5553—Simulation | CSCI 5521—Machine Learning Fundamentals |
| IE 5571—Reinforcement Learning and Dynamic Programming | CSCI 5523—Introduction to Data Mining |
| PUBH 7461—Exploring and Visualizing Data in R | CSCI 5525—Machine Learning: Analysis and Methods |
| PUBH 7475—Statistical Learning and Data Mining | CSCI 5707—Principles of Database Systems |
| MSBA 6321—Data Management, Databases, and Data Warehousing | CSCI 5751—Big Data Engineering and Architecture |
| MSBA 6331—Big Data Analytics | MABA 6321—Data Management and Big Data |
Note: Subject to departmental approval, other elective courses can be selected.
Ready to apply?
Review our admission requirements and start your application today!
Questions?
For more information, please contact Graduate Program Coordinator at [email protected].