Master of Science in Industrial Engineering

The School of Industrial and Systems Engineering (ISYE) offers eight master's degrees:

  • Master of Science in Industrial Engineering (MS IE);
  • Master of Science in Operations Research (MS OR);
  • Master of Science in Supply Chain Engineering (MS SCE);
  • Master of Science in Statistics (MS STAT);
  • Master of Science in Health Systems (MS HS);
  • Master of Science in Quantitative and Computational Finance (MS QCF);
  • Master of Science in International Logistics (MS IL) that is part of the executive program; and
  • Master of Science in Computational Science and Engineering (MS CSE).

Three of these programs are interdisciplinary:

  • MS QCF (joint with School of Mathematics, College of Business),
  • MS STAT (joint with School of Mathematics) and
  • MS SCE (joint with College of Computing, School of Mathematics).

All proposed master's degree programs require thirty semester credit hours with the exception of MS IL and MS QCF (thirty-six credit hours) and MS HS (thirty-three credit hours). None of these MS programs contains a thesis option.

A student seeking a master's degree must have a bachelor's degree and typically one earned in engineering, science, mathematics, or some other field that provides an adequate background for the successful completion of one of ISyE's programs. Students having backgrounds from unaccredited degree programs or in programs that are found lacking in relative substance can expect to first take preliminary coursework in order to elevate their preparation to the level required. The prerequisite coursework for the various master's degrees includes strong performance in probability, statistics, linear algebra, and calculus.

Every MS curriculum is based on core classes offered from the School of ISyE, as well as electives offered by ISyE and other Georgia Tech schools in engineering and science. The MS SCE, MS QCF, and MS IL are professional degree programs with separate curriculums from the other regular MS degrees.

 Program Requirements

Core
ISYE 6201Manufacturing Systems3
ISYE 6202Warehousing Systems3
ISYE 6203Transportation and Supply Chain Systems3
Breadth (Choose 3):9
Advanced Engineering Economy
Introduction to Financial Engineering
Statistical Modeling and Regression Analysis
Probabilistic Models and Their Applications
Deterministic Optimization
Technical Electives (Choose 2)6
Advanced Engineering Economy
Economic Decision Analysis
Scheduling Theory
Public Impact Applications of Operations Research and Management Science
Time Series Analysis
Nonparametric Data Analysis
Statistical Methods for Manufacturing Design and Improvement
Design and Analysis of Experiments
Statistical Modeling and Regression Analysis
Computational Statistics
Introduction to Theory and Practice of Bayesian Statistics
Biostatistics
Simulation
Probabilistic Models and Their Applications
Linear Optimization
Discrete Optimization
Nonlinear Optimization
Stochastic Optimization
Deterministic Optimization
Computational Methods in Optimization
Energy Technology and Policy
Computational Data Analysis: Learning, Mining, and Computation
Stochastic Processes I
Stochastic Processes II
Reliability Engineering
Systems Monitoring and Prognostics
Simulation Theory and Methods
Data Mining and Statistical Learning
Free Electives (Choose 2)6
6000-level or higher courses 1
Total Credit Hours30

Practicum Track Requirements

Core
ISYE 6201Manufacturing Systems3
ISYE 6202Warehousing Systems3
ISYE 6203Transportation and Supply Chain Systems3
Breadth (Choose 3):9
Advanced Engineering Economy
Introduction to Financial Engineering
Statistical Modeling and Regression Analysis
Probabilistic Models and Their Applications
Deterministic Optimization
Technical Electives (Choose 2)6
Advanced Engineering Economy
Scheduling Theory
Public Impact Applications of Operations Research and Management Science
Time Series Analysis
Nonparametric Data Analysis
Statistical Methods for Manufacturing Design and Improvement
Design and Analysis of Experiments
Statistical Modeling and Regression Analysis
Computational Statistics
Introduction to Theory and Practice of Bayesian Statistics
Biostatistics
Simulation
Probabilistic Models and Their Applications
Linear Optimization
Discrete Optimization
Nonlinear Optimization
Stochastic Optimization
Deterministic Optimization
Computational Methods in Optimization
Energy Technology and Policy
Computational Data Analysis: Learning, Mining, and Computation
Stochastic Processes I
Stochastic Processes II
Reliability Engineering
Systems Monitoring and Prognostics
Simulation Theory and Methods
Data Mining and Statistical Learning
Free Electives (Choose 1)3
6000-level or higher courses 1
Internship Preparation Elective 23
Public Impact Applications of Operations Research and Management Science
Simulation
Energy Technology and Policy
Practicum
COOP/INTN/ISYE Practicum
Total Credit Hours30