GPA and percentages are weighted by recorded letter grades. UW
comparisons use the same terms. Cross-listed courses count once within
this department and once across UW; department totals should not be
added together. Grade counts are not unique students. Offering counts
reflect captured records, not a complete historical schedule.
Spring 2027
Recorded this term
4545courses with offerings
4545instructors recorded
Grades have not been recorded for this term. Historical results remain
in the overview above.
Occupational Ergonomics and Biomechanics introduces engineers to designing manufacturing and industrial operations where people play a significant role, maximizing human capabilities while minimizing physical stress and optimizing workload.
Weekly workload consisted of one quiz with unlimited attempts, one homework assignment, and one in-class assignment, alongside two exams and a final project.
Introduction to Optimization covers mathematical optimization from a modeling and solution perspective, focusing on formulating discrete and continuous optimization problems and equilibrium models.
Past student reviews · Michael Ferris, Laurent Lessard
Homework assignments were difficult and time-consuming, with one student noting they would ruin their social life, while another found them helpful for learning.
Linear Optimization covers problems with linear inequality constraints, emphasizing formal proofs, the simplex method's theory, duality, and theorems of the alternatives.
Homeworks were extremely hard at times, though the total volume was limited to eight assignments. The workload is demanding due to the difficulty of the problems rather than the quantity.
Cooperative Education Program: Apply classroom theory and practical knowledge of engineering-related operations and functions in a professional setting through full-time workplace experience.
Introduction to industrial engineering subject matter, problem types, and design/analysis approaches with emphasis on formulation and design alternatives.
There were no exams or homework, only mandatory attendance and a group project. One student estimated the project took an hour, while another noted it was easy.
Introductory industrial statistics course covering probability, descriptive statistics, inference, and regression applied to engineering problems using Microsoft Excel.
Past student reviews · Amanda Smith, Hannah Silber
Weekly homework assignments and quizzes formed a substantial part of the workload, often covering current material. A group project required consistent effort, though criteria were clearly defined.
Covers fundamentals of data management and analysis for industrial engineers, including database strategies, preprocessing, visualization, and modeling using MySQL and R.
Homework was similar to in-class problems, and one student found it easy to get extra credit for attendance. Exam difficulty varied by review, with some finding them easy and others describing them as decently hard with little preparation guidance.
Engineering Economic Analysis covers financial accounting, cost systems, and the use of accounting reports for economic decision-making, including DCF, CVP, and capital budgeting.
Homeworks were straightforward and the group project was mostly graded on completion. Exams were open note, allowing students to refer to materials during testing.
Production Planning and Control covers techniques for designing inventory, production, quality, and project-planning systems, including computer applications.
The course project was described as really time-consuming. The textbook was essential for the final exam, and while lectures were tough to get through, discussion sections helped clarify practical application.
Simulation Modeling Laboratory involves computer exercises in random variables, queuing system spreadsheets, and simulation software, concluding with a project.
Operations Research-Deterministic Modeling covers basic techniques for modeling and optimizing deterministic systems with emphasis on linear programming, including computer solutions and applications to production, logistics, and service.
Students reported that homework took a long time and exams included difficult true and false questions. Success required understanding how to solve homework problems independently and attending lectures.
Introduction to Human Factors Engineering Laboratory provides hands-on experience applying ISYE/PSYCH 349 concepts through a three-part design project.
The reviewer found the hardest part was figuring out what to do, as directions were unclear, nonexistent, or posted twice with significant differences.
Fundamentals of Industrial Data Analytics covers using data analytics for data-driven decisions, emphasizing application to industrial engineering problems via modeling strategies.
The course features weekly homework with 4 to 5 questions covering lecture material. Students noted that the exams align precisely with the instructor's outlines, making preparation straightforward for those who study consistently.
Industrial Engineering Design II is a team-based course where students collaborate with external clients to design or redesign systems/processes using ISyE methodologies for analysis, solution development, and implementation planning.
Machine Learning in Action for Industrial Engineers covers principles, algorithms, and predictive analytics for improving decision-making in healthcare, transportation, and public sectors.
Homework and quizzes were considered fair, but project directions were vague and super open-ended, requiring students to talk to the instructor to stay on track.
Human Factors Engineering Design and Evaluation covers ergonomics, human factors, and sociotechnical systems. Students collect instrument-based and user survey data to improve human performance and complete a design project.
Production Systems Control is an intermediate to advanced course applying operations research techniques to production planning, scheduling, and inventory control.
Covers strategic quality planning, change management, problem solving, process improvement, and performance evaluation to build business and decision-making skills for quality systems.
Advanced Optimization Modeling covers translating problems into optimization models and implementing them in software, focusing on discrete decisions and uncertainty.
Stochastic Modeling Techniques covers modeling systems with uncertainty, emphasizing application over derivation, using case studies from logistics, production, and service industries.