Master of Operations Research (MOR)
The Master of Operations Research prepares you for a career as an OR practitioner in the private or public sector.
Last Updated: 12/19/2025 | All information is accurate and still up-to-date
The Master of Operations Research (MOR) prepares you for a career as an OR practitioner in the private or public sector. Specifically, the MOR degree requires completing 31 credit hours in OR and related courses. Consequently, this degree equips you with the skills needed for success in various industries. Ultimately, our program ensures you are ready for the challenges in both the private and public sectors. According to the U.S. Bureau of Labor Statistics, employment of operations research analysts is projected to increase by 23% from 2022 to 2032, surpassing the average growth rate for all occupations.
Master of Operations Research
Core Courses (10 hours)
To earn the Master of Operations Research degree, you must complete OR 601 Seminar in Operations Research for one credit hour. You must attend the seminar throughout your program. The other nine credit hours will come from selecting one core course from three categories:
- Optimization/Deterministic Models
- Stochastic Models
- Data Science and AI (OR PRO TIP: Other graduate-level CSC, STAT or ISE courses may be substituted with approval)
| Core Courses 1 | |
|---|---|
| Optimization/ Deterministic Models | |
| OR 501 Introduction to Operations Research | |
| OR 504 Introduction to Mathematical Modeling | |
| OR 505 Linear Programming | |
| OR 506 Algorithmic Methods in Nonlinear Programming | |
| OR 531 Dynamic Systems and Multivariable Control I | |
| OR 565 Graph Theory | |
| OR 706 Nonlinear Programming | |
| OR 708 Integer Programming | |
| OR 709 2 Dynamic Programming | |
| OR 719 Vector Space Methods in System Optimization | |
| OR 731 Dynamic Systems and Multivariable Control II | |
| OR 766 Network Flows | |
| Stochastic Models | |
| MA/ST 546 Probability and Stochastic Processes I | |
| MA/ST 747 Probability and Stochastic Processes II | |
| OR 560 Stochastic Models in Industrial Engineering | |
| OR 562 Simulation Modeling | |
| OR 709 2 Dynamic Programming | |
| OR 760 Applied Stochastic Models in Industrial Engineering | |
| OR 761 Queues and Stochastic Service Systems | |
| OR 762 Computer Simulation Techniques | |
| OR 772 Stochastic Simulation Design and Analysis | |
| Data Science and AI | |
| CE 537 Computer Methods and Applications | |
| CSC 505 Design and Analysis of Algorithms | |
| MA 540 Uncertainty Quantification | |
| ISE 519 Database Applications in Industrial and Systems Engineering | |
| ISE 535 Python Programming for Industrial and Systems Engineers | |
| ISE 537 Statistical Models for Systems Analytics in Industrial Engineering | |
| ISE 538 Practical Machine Learning for Engineering Analytics | |
| OR 579 Introduction to Computer Performance Modeling | |
| ST 516 Experimental Statistics For Engineers II | |
| ST 554 Analysis of Big Data | |
| ST 558 Data Science for Statisticians |
1 Special topics courses (such as OR 591/791, ISE 589/789 or MA 591/791) may be counted as appropriate core categories with DGP approval.
2 OR 709 cannot be double-counted for the deterministic and stochastic courses categories
Electives (21 hours)
You should choose seven additional elective courses from mathematics, engineering, statistics, computer science, or other STEM disciplines. For example, you can select courses in econometrics or data science. Some business courses are also acceptable as electives. OR PRO TIP: Courses used to satisfy the required core courses requirement cannot be used to satisfy the elective course requirement. Direct any questions about electives to the OR Program Specialist, your faculty advisor or academic committee.
| Area * | Electives |
|---|---|
| Data Analytics, Stats and Computer Science | |
| BAE 555 R Coding for Data Management and Analysis | |
| ECE 542/CSC 542 Neural Networks | |
| ISE 519 Database Applications in Industrial and Systems Engineering | |
| ISE 535 Python Programming for Industrial and Systems Engineers | |
| ISE 748 Quality Engineering | |
| ST 555 Statistical Programming I | |
| ST 556 Statistical Programming II | |
| ST 558 Data Science for Statisticians | |
| ST 563 Introduction to Statistical Learning | |
| Supply Chain and Logistics | |
| ISE 511 Supply Chain Economics and Decision Making | |
| ISE 513 Humanitarian Logistics | |
| ISE 533 Service Systems Engineering | |
| ISE 552 Design and Control of Production and Service Systems | |
| ISE 553 Modeling and Analysis of Supply Chains | |
| MBA 544 Operations Analysis (Warsing's course) | |
| MBA 548 Analytical Supply Chain Management (Heese's course) | |
| Business/Operations Management | |
| ISE 510 Applied Engineering Economy | |
| ISE 511 Supply Chain Economics and Decision Making | |
| *Courses may appear in multiple areas. Be sure to check special topics (e.g. OR 591, OR 791, ISE 589, ISE 789) which vary semester by semester. | |
Total Hours (31 hours)
OR PRO TIP: Must have at least 12 credits of OR prefix courses.
Sample MOR Pathways
Stochastics
| Semester | Course #1 | Course #2 | Course #3 | Course #4 |
|---|---|---|---|---|
| Fall | OR 601 (1 credit) | ISE 535 | OR 501 | OR 560 |
| Spring | OR 504 | OR 562 | OR 709 | ST 515 |
| Fall | EM 538 | MA 540 | ST 516 |
Optimization
| Semester | Course #1 | Course #2 | Course #3 | Course #4 |
|---|---|---|---|---|
| Fall | OR 601 (1 credit) | MA 405 | OR 501 | ST 555 |
| Spring | OR 504 | OR 560 | ST 556 | |
| Fall | ISE 537 | OR 505 | OR 506 | OR 565 |
Supply Chain and Logistics
| Semester | Course #1 | Course #2 | Course #3 | Course #4 |
|---|---|---|---|---|
| Fall | OR 601 (1 credit) | EM 538 | ISE 511 | OR 501 |
| Spring | ISE 552 | ISE 553 | OR 562 | |
| Fall | ISE 519 | ISE 754 | OR 506 | OR 579 |
Data Science and AI
| Semester | Course #1 | Course #2 | Course #3 | Course #4 |
|---|---|---|---|---|
| Fall | OR 601 (1 credit) | ISE 535 | ISE 537 | OR 504 |
| Spring | ISE 519 | MEM 538 | OR 562 | ST 554 |
| Fall | ECE 542 | OR 506 | OR 565 |