Undergraduate

BS Industrial Engineering Required & Core Courses

Below are the courses required to earn a bachelor's of science in industrial engineering from the College of Engineering at the University of South Florida. Information is based on the .

Download PDF: Bachelor of Science in Industrial Engineering (BSIE) Prerequisite and Core Courses

Prerequisite courses to be completed prior to department admission
Course  Description
An introduction to concepts of probability and statistical analysis with special emphasis on critical interpretation of data, comparing and contrasting claims, critical thinking, problem solving, and writing.
Presents basic economic models used to evaluate engineering project investments with an understanding of the implications of human and cultural diversity on financial decisions through lectures, problem solving, and critical writing.
Study and application of matrix algebra, differential equations and calculus of finite differences.
An introductory course covering the principles of technical drawing by employing traditional and Computer-Aided-Drafting (CAD) techniques using AutoCAD. Students will also learn to apply these concepts to civil design and engineering plans preparation.

Core Courses
Course Description
The study of basic manufacturing processes and precision assembly. CAD/CAM including NC programming.
This course covers basic techniques for modeling and optimizing deterministic systems with emphasis on linear programming, network optimization, basic mixed integer programming, and nonlinearprogramming. Students learn how to compute solutions of various optimization problems. Applications to production, logistics, and service systems are discussed.
EIN 4312C - Work Analysis  
A problem based approach to describing programming concepts using Visual Basic for Applications and MS Excel.
Planning and control of production systems. Includes: forecasting and inventory control models, scheduling and sequencing, MRP, CPM/PERT, and resource requirements.
This course will present data driven theory and methods of quality monitoring including process capability, control charts, acceptance sampling, quality engineering, and quality design.
Probabilistic models in Operations Research. Discrete and continuous time processes, queuing models, inventory models, simulation models, Markovian decision process and decision analysis.
This course introduces the fundamentals of database management systems. The basic concepts for the design, use, and implementation of database systems will be presented in this course.
With the rapid advancement of sensing technology and information systems, massive amounts of data are being generated in various fields, ranging from engineering to applied science. There is an increasing need for data scientists and analysts who have skills and knowledge to analyze and interpret such data in order to extract patterns and gain insights for problem solving and decision making.
Activity forecasting models and control. Design and use of inventory control models, both designs applicable to engineering analyses. Analysis of variance and regression.
A study of the development and analysis of computer simulation models: Monte Carlo, time-slice, and next-event. Introduction to special purpose simulation languages.
Introduction to the practices and concepts of automation as applied to material handling, inventory storage, material transfer, industrial processes and quality control.
Future-based projections are an integral part of our lives. We make these projections/decisions either with tangible data or with intuition and experience. Massive amounts of data are being generated in various fields, ranging from engineering to applied science. This course focuses on the practice of predictive and prescriptive analytics by developing models using statistical learning tools and quantifying their prediction accuracy on unseen data.