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Gallogly College of Engineering

Mewbourne College Of Earth And Energy

College of Atmospheric and Geographic Sciences

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MASTER OF SCIENCE IN INDUSTRIAL & SYSTEMS ENGINEERING

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Program Overview

The online M.S. in Industrial and Systems Engineering is a 30-credit program that can be completed in as little as 24 months. The program centers around preparing students to solve real-world problems to optimize processes, systems, investments, and organizations in nearly every industry.

Online MS in Sustainable Architecture

100%
online delivery
15
months
to complete
30
credit hours

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.

Online Application

Submit an official transcript from your undergraduate institution and any graduate institution you have attended.

Submit resume: Include professionally formatted documentation of your past education and work experience.

Write and submit a personal statement on your career goals and reasons for applying to the program.

GRE scores are optional and not required for admission, but they may be required by some potential faculty sponsors to be considered for a Qualifying Graduate Assistantship. International students are required to take the TOEFL exam.

Careers

Earn an online M.S. in Industrial and Systems Engineering at the University of Oklahoma. Our interdisciplinary faculty ensures practical skills aligned with current trends and employer needs for immediate career application.

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Program Courses

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.
Accordion Content
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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