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Master of Science in Industrial & Systems Engineering

Program Overview

The online Master of Science in Industrial and Systems Engineering program provides a strong combination of methods and applications. Students can take advantage of faculty expertise in three areas: aerospace and defense, health and medical systems, and critical infrastructures. The online graduate program centers around preparing students to solve real-world problems to optimize processes, systems, investments, and organizations in nearly every industry. Students develop technical capabilities as well as professional skills including leadership, teamwork, and communication.

The online Master of Science in Industrial and Systems Engineering is a 30-credit, 24 month online program designed for working professionals.

Fast-Paced, Growing Industry

Industrial Engineering is one of the largest growing engineering fields and expected to grow by 10% in the workforce by 2029*.
*Industrial Engineers Occupational Outlook Handbook, (2022).

Join an In-Demand Network

Industrial and Systems Engineers (ISEs) are utilized in many specialty areas such as aerospace and defense, healthcare, finance, cybersecurity, supply chain, transportation, and more.

Learn From Experts in the Field

Study with faculty who incorporate real-world insight into their courses. Learn from professors who specialize in infrastructure systems, cognitive engineering, operations research, and more.

Strong Methods & Application

Students are prepared to take advantage of faculty expertise in three areas: aerospace and defense, health and medical systems, and critical infrastructures.

Flexible, Online Format

This program is flexible to fit student’s busy lifestyles. Students take classes from anywhere in the world, often while continuing to work full-time.

Join the Global Sooner Network

With a rich tradition and history, graduates of the University of Oklahoma can network with Sooners in the industrial and systems industry through both formal and informal networks.

Program Requirements

Program Cost

Tuition for the online M.S. in Industrial and Systems Engineering program is $32,505* for the entire program ($985/credit hour). Fees associated with the program are included in this cost. Books and materials are additional.

Once accepted into the M.S. program, students are required to submit a $350 non-refundable deposit within two weeks. Deposits are applied toward tuition expenses and secure a student’s place in the upcoming class.

*Tuition and fees are subject to change at the discretion of the Oklahoma State Regents for Higher Education.

Admissions Requirements

To apply to the online Master of Science in Industrial and Systems Engineering program, students must hold a bachelor’s degree from a regionally accredited college of university (or the international equivalent).

Students are required to have a background in Engineering Calculus I and II. A Linear Algebra course is recommended, though not required. Students are encouraged to hold a bachelor’s degree in a Science, Technology, Engineering, and/or Mathematics (STEM) discipline but not required. Admissions will occur on a rolling basis and is at the discretion of the admissions committee. Transfer credits will be accepted at the discretion of the admissions committee.

Students who wish to apply must:

Program Courses

Designed for working professionals, the Master of Accountancy (MAcc) from OU can be completed in as little as 21 months. The program helps students prepare for the CPA exam in a convenient online format.

Required Courses

This course provides fundamental concepts in probability and statistical inference, with application to engineering contexts. Probability topics include counting methods, discrete and continuous random variables, and their associated distributions. Statistical inference topics include sampling distributions, point estimation, confidence intervals and hypothesis testing for single- and two- sample experiments, nonparametric statistics, and goodness-of-fit testing. Excel will be used to demonstrate how to solve some class examples, and you’ll be expected to use Excel to solve some homework problems. The statistical software package R will be introduced to address basic statistics problems and to prepare you for future coursework. Course prerequisites include calculus (differentiation and integration).
Explores and applies advanced techniques for addressing complex decision problems. Focus is on developing and employing problem solving strategies using advanced methods in the context of Data Science and Analytics. Topics include both continuous and combinatorial optimization with an emphasis on traditional techniques such as mathematical programming as well as modern heuristics such as simulated annealing, evolutionary algorithms, and swarm optimization. Students will use programming skills to implement algorithms and solve problems.
This course provides the fundamentals of decision analysis and explores how analyzing risk can be incorporated into good decision making. Normative approaches to making decisions when uncertainty exists are central to this course. Topics covered include structuring decision problems, developing alternatives, single and multiple objectives, utility theory, risk tolerance, data-driven and subjective probability, and psychological pitfalls, among others. Computer programs and graphical tools such as influence diagrams will be discussed. Principles are applied to decisions in engineering, with other potential applications in business, medicine, and public policy, among others.
This course serves as capstone for ISE Online, and will enable the application of the systems engineering process to an industry or academic topic. It will apply knowledge learned throughout the ISE On-line curriculum, exhibit high quality writing skills through the completion of a formal project report, exhibit high quality oral and visual communications skills through the presentation of the project, and provide useful systems engineering insights.
This course will cover the definition and refinement of the system and its elements and discuss all integrated functional and performance requirements. Configuration control and management will be taught with suitable example analysis. System design and development will be taught that integrates the needs of system users, and system elements to meet the operational mission requirements within specified cost and schedule constraints. The interactions among various functions to achieve balanced requirements will be analyzed and discussed with respect to user specifications.
This course will explore modeling to support system requirements, design, analysis, and verification and validation, from conceptual design through development to the other aspects of the entire life cycle. The INCOSE definition of MBSE will be studied beginning with a simplified version of a concept, phenomenon, relationship, structure of a system; representing it graphically, mathematically, or physically; and abstracting reality by eliminating unnecessary components. The objectives of modeling are to aid in decision making, through consideration of behavioral analysis, system architecture, requirement traceability, and performance analysis.
Provides a comprehensive review of the organization and structure of the market for energy assets and commodities: including trading platforms, pricing issues, forecasting, role and linkage with associated futures, forwards and options contracts, “basis” and spreads, hedging strategies, the principles governing the valuation of these “derivative” securities, and the ways in which these securities can be used effectively.
This course describes the use of statistical methods for quality control and improvement in product and process environments, as well as introductory applied probability for component and system reliability. Topics include philosophies of quality management, control chart theory and application, process capability, and performance metrics of reliability. Focus is given to decision making in engineering systems.
This course develops the skills and competencies necessary for planning and controlling projects and understanding interpersonal issues that drive successful project outcomes. This course is divided into three parts: Part I covers the technical dimensions of project management, Part II delves into the sociocultural dimensions of project management, and Part III investigates risk management.
This course is designed to provide a basic introduction to leadership by focusing on what it means to be a good leader. Emphasis in the course is on the practice of leadership. The course will examine topics such as: the nature of leadership, recognizing leadership traits, developing leadership skills, creating a vision, setting the tone, listening to out-group members, handling conflict, overcoming obstacles, and addressing ethics in leadership. Attention will be given to helping students to understand and improve their own leadership performance.
This course provides the fundamentals of decision analysis and explores how analyzing risk can be incorporated into good decision making. Normative approaches to making decisions when uncertainty exists are central to this course. Topics covered include structuring decision problems, developing alternatives, single and multiple objectives, utility theory, risk tolerance, data-driven and subjective probability, and psychological pitfalls, among others. Computer programs and graphical tools such as influence diagrams will be discussed. Principles are applied to decisions in engineering, with other potential applications in business, medicine, and public policy, among others.

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