Twin Cities campus

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Twin Cities Campus

Industrial and Systems Engineering M.S.I.SY.E.

Industrial and Systems Engineering
College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Industrial and Systems Engineering Graduate Program, University of Minnesota, 100 Union Street SE, Minneapolis, MN 55455 (612-624-1582; fax 612-624-0944)
Email: isye@umn.edu
Website: https://cse.umn.edu/isye/
  • Program Type: Master's
  • Requirements for this program are current for Fall 2021
  • Length of program in credits: 30 to 32
  • This program does not require summer semesters for timely completion.
  • Degree: Master of Science in Industrial & Systems Engr
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
The Industrial and Systems Engineering (ISyE) MS program offers coursework and research in industrial and systems engineering, operations research, and human factors. Special emphasis is on methodologies for design, planning, and management of service and manufacturing systems. Examples of research applications include logistics, transportation, healthcare delivery systems, revenue management, and supply chain management. MS students can pursue one of three tracks: Analytics, Industrial Engineering, or Systems Engineering.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
A baccalaureate degree in engineering or a closely related field is required.
Special Application Requirements:
All application materials are submitted electronically through the Graduate Admissions Office. The application must include the intended track. Analytics Track: · The GRE is required. · Personal statement · Three letters of recommendation (required for financial aid consideration only) · Students are admitted fall semester only. The application deadline is February 15. Industrial Engineering Track: · The GRE is required. · Personal statement · Three letters of recommendation (required for financial aid consideration only) · Application deadlines are February 15 for fall semester and October 15 for spring semester. Systems Engineering Track: · The GRE is not required. · A minimum two years of professional work experience in a technical field is required; however, promising candidates with less experience will be considered under exceptional circumstances · Personal statement · Three letters of recommendation · Application deadlines are February 15 for fall semester and October 15 for spring semester.
Applicants must submit their test score(s) from the following:
  • GRE
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 79
    • Internet Based - Writing Score: 21
    • Internet Based - Reading Score: 19
  • IELTS
    • Total Score: 6.5
Key to test abbreviations (GRE, TOEFL, IELTS).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan A: Plan A requires 14 major credits, 6 credits outside the major, and 10 thesis credits. The final exam is oral.
Plan B: Plan B requires 24 major credits and 6 credits outside the major. The final exam is oral.
Plan C: Plan C requires 24 to 30 major credits and 0 to 6 credits outside the major. There is no final exam.
This program may be completed with a minor.
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 2.80 is required for students to remain in good standing.
Courses offered on both the A-F and S/N grading basis must be taken A-F. In order to fulfill the University's graduate education policy regarding research ethics training, students are required to take an online research ethics training course through the CITI program, or a qualifying equivalent. Analytics track: Non-native English speakers must take ESL 5008 (2 credits). ESL 5008 cannot be applied to degree requirements.
Joint- or Dual-degree Coursework:
MSISyE/MS-Civil Engineering (Transportation Engineering Focus) Students may take a total of 15 credits in common among the academic programs.
Program Sub-plans
Students are required to complete one of the following sub-plans.
Students may not complete the program with more than one sub-plan.
Analytics
This sub-plan is limited to students completing the program under Plan C.
Required Courses (24 credits)
Take the following courses. Select CSCI 5521 or CSCI 5523 in consultation with the advisor. Students not in the Integrated B.ISyE/M.S.ISyE program take IE 5532. Students in the Integrated B.ISyE/M.S.ISyE program take IE 5545.
IE 5531 - Engineering Optimization I (4.0 cr)
IE 5561 - Analytics and Data-Driven Decision Making (4.0 cr)
IE 5773 - Practice-focused Seminar (1.0 cr)
IE 5801 - Capstone Project (4.0 cr)
STAT 5302 - Applied Regression Analysis (4.0 cr)
IE 5532 - Stochastic Models (4.0 cr)
or IE 5545 - Decision Analysis (4.0 cr)
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
or CSCI 5523 - Introduction to Data Mining (3.0 cr)
Electives (6 credits)
Select 6 credits from the following in consultation with the advisor. Other credits may be chosen with advisor and director of graduate studies approval.
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5751 - Big Data Engineering and Architecture (3.0 cr)
IE 5441 - Financial Decision Making (4.0 cr)
IE 5522 - Quality Engineering and Reliability (4.0 cr)
IE 5541 - Project Management (4.0 cr)
IE 5545 - Decision Analysis (4.0 cr)
IE 5551 - Production and Inventory Systems (4.0 cr)
IE 5553 - Simulation (4.0 cr)
PUBH 7461 - Exploring and Visualizing Data in R (2.0 cr)
PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
STAT 5303 - Designing Experiments (4.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
STAT 5421 - Analysis of Categorical Data (3.0 cr)
STAT 5511 - Time Series Analysis (3.0 cr)
STAT 5601 - Nonparametric Methods (3.0 cr)
Industrial Engineering
Required Courses (12 to 16 credits)
Take the following courses:
IE 5531 - Engineering Optimization I (4.0 cr)
IE 5532 - Stochastic Models (4.0 cr)
Plan A students select 4 credits, and Plan B and Plan C students select at least 8 credits from the following in consultation with the advisor:
IE 5511 - Human Factors and Work Analysis (4.0 cr)
IE 5545 - Decision Analysis (4.0 cr)
IE 5551 - Production and Inventory Systems (4.0 cr)
Seminar (1 credit)
Select 1 of the following seminars in consultation with the advisor. Other seminars may be chosen with advisor approval.
IE 8773 - Graduate Seminar (1.0 cr)
IE 8774 - Graduate Seminar (1.0 cr)
Outside Courses (6 credits)
Select at least 6 credits from the following in consultation with the advisor. Other courses may be chosen with advisor and director of graduate studies approval.
APEC 8001 - Applied Microeconomic Analysis of Consumer Choice and Consumer Demand (2.0 cr)
APEC 8002 - Applied Microeconomic Analysis of Production and Choice Under Uncertainty (2.0 cr)
APEC 8206 - Dynamic Optimization: Applications in Economics and Management (3.0 cr)
APEC 8211 - Econometric Analysis I (2.0 cr)
CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
CSCI 5801 - Software Engineering I (3.0 cr)
ECON 8117 - Noncooperative Game Theory (2.0 cr)
HINF 5502 - Python Programming Essentials for the Health Sciences (1.0 cr)
MATH 5615H - Honors: Introduction to Analysis I (4.0 cr)
MATH 5616H - Honors: Introduction to Analysis II (4.0 cr)
MATH 8651 - Theory of Probability Including Measure Theory (3.0 cr)
MBA 6031 - Financial Accounting (3.0 cr)
MBA 6221 - Supply Chain & Operations (3.0 cr)
MGMT 6004 - Negotiation Strategies (2.0 cr)
MILI 6985 - The Health Care Marketplace (2.0 cr)
MKTG 8810 - Consumer Behavior Special Topics (2.0 cr)
MOT 5001 - Technological Business Fundamentals (2.0 cr)
MOT 5002 - Creating Technological Innovation (3.0 cr)
PUBH 6325 - Data Processing with PC-SAS (1.0 cr)
PUBH 7461 - Exploring and Visualizing Data in R (2.0 cr)
PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
SCO 6041 - Project Management (2.0 cr)
SCO 6056 - Managing Supply Chain Operations (4.0 cr)
SCO 6059 - Quality Management and Lean Six Sigma (4.0 cr)
SCO 6072 - Managing Technologies in the Supply Chain (2.0 cr)
STAT 5021 - Statistical Analysis (4.0 cr)
STAT 5302 - Applied Regression Analysis (4.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
Electives
Plan A students select credits as needed to meet the 20 course credits required for the degree, Plan B students select credits as needed to meet the 30-credit minimum, and Plan C students select credits as needed to meet the 32-credit minimum. The number of Plan B credits will be dependent upon how the 4-credit Plan B Project requirement is satisfied. Credits are selected in consultation with the advisor.
IE 5080 - Topics in Industrial Engineering (1.0-4.0 cr)
IE 5111 - Systems Engineering I (2.0 cr)
IE 5113 - Systems Engineering II (4.0 cr)
IE 5441 - Financial Decision Making (4.0 cr)
IE 5511 - Human Factors and Work Analysis (4.0 cr)
IE 5513 - Engineering Safety (4.0 cr)
IE 5522 - Quality Engineering and Reliability (4.0 cr)
IE 5541 - Project Management (4.0 cr)
IE 5545 - Decision Analysis (4.0 cr)
IE 5551 - Production and Inventory Systems (4.0 cr)
IE 5553 - Simulation (4.0 cr)
IE 5561 - Analytics and Data-Driven Decision Making (4.0 cr)
IE 5773 - Practice-focused Seminar (1.0 cr)
IE 5801 - Capstone Project (4.0 cr)
IE 8521 - Optimization (4.0 cr)
IE 8531 - Discrete Optimization (4.0 cr)
IE 8532 - Stochastic Processes and Queuing Systems (4.0 cr)
IE 8533 - Advanced Stochastic Processes and Queuing Systems (4.0 cr)
IE 8534 - Advanced Topics in Operations Research (1.0-4.0 cr)
IE 8552 - Advanced Topics in Production, Inventory, and Distribution Systems (4.0 cr)
Plan Options
Plan A
Thesis Credits
Take 10 master's thesis credits.
IE 8777 - Thesis Credits: Master's (1.0-18.0 cr)
-OR-
Plan B
Project Credits (0 to 4 credits)
Complete the Plan B project/paper. Take 0-4 credits of the following in consultation with the advisor.
IE 8794 - Industrial Engineering Research (1.0-6.0 cr)
Systems Engineering
This sub-plan is limited to students completing the program under Plan C.
Required Courses (14 credits)
Take the following courses:
IE 5111 - Systems Engineering I (2.0 cr)
IE 5113 - Systems Engineering II (4.0 cr)
IE 5541 - Project Management (4.0 cr)
IE 5553 - Simulation (4.0 cr)
Outside Courses (6 credits)
Select at least 6 credits from the following in consultation with the advisor. Other courses may be chosen with advisor and director of graduate studies approval.
APEC 8001 - Applied Microeconomic Analysis of Consumer Choice and Consumer Demand (2.0 cr)
APEC 8002 - Applied Microeconomic Analysis of Production and Choice Under Uncertainty (2.0 cr)
APEC 8206 - Dynamic Optimization: Applications in Economics and Management (3.0 cr)
APEC 8211 - Econometric Analysis I (2.0 cr)
CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
CSCI 5801 - Software Engineering I (3.0 cr)
ECON 8117 - Noncooperative Game Theory (2.0 cr)
HINF 5502 - Python Programming Essentials for the Health Sciences (1.0 cr)
MATH 5615H - Honors: Introduction to Analysis I (4.0 cr)
MATH 5616H - Honors: Introduction to Analysis II (4.0 cr)
MATH 8651 - Theory of Probability Including Measure Theory (3.0 cr)
MBA 6031 - Financial Accounting (3.0 cr)
MBA 6221 - Supply Chain & Operations (3.0 cr)
MGMT 6004 - Negotiation Strategies (2.0 cr)
MILI 6985 - The Health Care Marketplace (2.0 cr)
MKTG 8810 - Consumer Behavior Special Topics (2.0 cr)
MOT 5001 - Technological Business Fundamentals (2.0 cr)
MOT 5002 - Creating Technological Innovation (3.0 cr)
PUBH 6325 - Data Processing with PC-SAS (1.0 cr)
PUBH 7461 - Exploring and Visualizing Data in R (2.0 cr)
PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
SCO 6041 - Project Management (2.0 cr)
SCO 6056 - Managing Supply Chain Operations (4.0 cr)
SCO 6059 - Quality Management and Lean Six Sigma (4.0 cr)
SCO 6072 - Managing Technologies in the Supply Chain (2.0 cr)
STAT 5021 - Statistical Analysis (4.0 cr)
STAT 5302 - Applied Regression Analysis (4.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
Electives
Select credits as needed from the following, in consultation with the advisor, to complete the 30-credit minimum.
IE 5080 - Topics in Industrial Engineering (1.0-4.0 cr)
IE 5441 - Financial Decision Making (4.0 cr)
IE 5513 - Engineering Safety (4.0 cr)
IE 5522 - Quality Engineering and Reliability (4.0 cr)
IE 5531 - Engineering Optimization I (4.0 cr)
IE 5532 - Stochastic Models (4.0 cr)
IE 5545 - Decision Analysis (4.0 cr)
IE 5551 - Production and Inventory Systems (4.0 cr)
IE 5561 - Analytics and Data-Driven Decision Making (4.0 cr)
IE 5773 - Practice-focused Seminar (1.0 cr)
IE 5801 - Capstone Project (4.0 cr)
IE 8521 - Optimization (4.0 cr)
IE 8531 - Discrete Optimization (4.0 cr)
IE 8532 - Stochastic Processes and Queuing Systems (4.0 cr)
IE 8533 - Advanced Stochastic Processes and Queuing Systems (4.0 cr)
IE 8534 - Advanced Topics in Operations Research (1.0-4.0 cr)
IE 8552 - Advanced Topics in Production, Inventory, and Distribution Systems (4.0 cr)
Integrated B.I.Sy.E./M.S.I.Sy.E.
This sub-plan is optional and does not fulfill the sub-plan requirement for this program.
This sub-plan is limited to students completing the program under Plan C.
The Department of Industrial and Systems Engineering offers an integrated bachelor's/master's degree program. The program makes it possible for students to earn both a bachelor's degree (BISyE) and a master's degree (MSISyE–Analytics track) in Industrial and Systems Engineering in five years. The program has several benefits: a streamlined admissions process from the undergraduate to the graduate program; graduate student status granted in the senior year; eligibility for teaching and research assistantships; and flexibility in fulfilling required courses for both degrees in the last two years of study. Applicants must be enrolled in the ISyE undergraduate program at the University of Minnesota–Twin Cities and have a minimum cumulative GPA of at least 3.4 or a strong letter of recommendation from an ISyE faculty member. The following IE courses must be completed or in progress at the time of application: 1101, 2021, 3011, 3012, 3521, 3522, 4011, and 4551. The BISyE and MSISyE degrees must be completed in their entirety, with no courses shared between them. The graduate degree cannot be earned before the undergraduate requirements are satisfied. Admitted students who decide not to complete the MSISyE degree are permitted to count credits originally planned for the graduate program toward their undergraduate technical electives.
 
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IE 5531 - Engineering Optimization I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Linear programming, simplex method, duality theory, sensitivity analysis, interior point methods, integer programming, branch/bound/dynamic programming. Emphasizes applications in production/logistics, including resource allocation, transportation, facility location, networks/flows, scheduling, production planning. prereq: Upper div or grad student or CNR
IE 5561 - Analytics and Data-Driven Decision Making
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Hands-on experience with modern methods for analytics and data-driven decision making. Methodologies such as linear and integer optimization and supervised and unsupervised learning will be brought together to address problems in a variety of areas such as healthcare, agriculture, sports, energy, and finance. Students will learn how to manipulate data, build and solve models, and interpret and visualize results using a high-level, dynamic programming language. Prerequisites: IE 3521 or equivalent; IE 3011 or IE 5531 or equivalent; proficiency with a programming language such as R, Python, or C.
IE 5773 - Practice-focused Seminar
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall
Industry and academic speakers, topics relevant to analytics practice.
IE 5801 - Capstone Project
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Students work on ISyE Analytics Track capstone project in small teams of two or three. Projects are supervised by industry mentor and faculty adviser. Projects involve application of techniques from Analytics Track curriculum. Prerequisites: ISyE Analytics Track MS Student; IE 5531; IE 5561; Stat 5302; CSci 5521 or 5523.
STAT 5302 - Applied Regression Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.
IE 5532 - Stochastic Models
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to stochastic modeling and stochastic processes. Probability review, random variables, discrete- and continuous-time Markov chains, queueing systems, simulation. Applications to industrial and systems engineering including production and inventory control. prereq: Undergraduate probability and statistics. Familiarity with computer programming in a high level language.
IE 5545 - Decision Analysis
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Single-person and group decision problems. Structuring of decision problems arising in personal, business, and public policy contexts. Decision-making under uncertainty, value of information, games of complete information and Nash equilibrium, Bayesian games, group decision-making and distributed consensus, basics of mechanism design. prereq: 3521 or equiv
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5751 - Big Data Engineering and Architecture
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Big data and data-intensive application management, design and processing concepts. Data modeling on different NoSQL databases: key/value, column-family, document, graph-based stores. Stream and real-time processing. Big data architectures. Distributed computing using Spark, Hadoop or other distributed systems. Big data projects. prereq: 4041, 5707, or instructor consent.
IE 5441 - Financial Decision Making
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Cash flow streams, interest rates, fixed income securities. Evaluating investment alternatives, capital budgeting, dynamic cash flow process. Mean-variance portfolio selection, Capital Asset Pricing Model, utility maximization, risk aversion. Derivative securities, asset dynamics, basic option pricing theory. prereq: CSE upper div or grad student
IE 5522 - Quality Engineering and Reliability
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 3522/IE 5522
Typically offered: Periodic Fall & Spring
Quality engineering/management, economics of quality, statistical process control design of experiments, reliability, maintainability, availability. prereq: [4521 or equiv], [upper div or grad student or CNR]
IE 5541 - Project Management
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 4541/IE 5541
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Introduction to engineering project management. Analytical methods of selecting, organizing, budgeting, scheduling, and controlling projects, including risk management, team leadership, and program management. prereq: Upper div or grad student
IE 5545 - Decision Analysis
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Single-person and group decision problems. Structuring of decision problems arising in personal, business, and public policy contexts. Decision-making under uncertainty, value of information, games of complete information and Nash equilibrium, Bayesian games, group decision-making and distributed consensus, basics of mechanism design. prereq: 3521 or equiv
IE 5551 - Production and Inventory Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Inventory control, supply chain management, demand forecasting, capacity planning, aggregate production and material requirement planning, operations scheduling, and shop floor control. Quantitative models used to support decisions. Implications of emerging information technologies and of electronic commerce for supply chain management and factory operation. prereq: CNR or upper div or grad student
IE 5553 - Simulation
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 3553/IE 5553
Typically offered: Periodic Fall & Spring
Discrete event simulation. Using integrated simulation/animation environment to create, analyze, and evaluate realistic models for various industry settings, including manufacturing/service operations and systems engineering. Experimental design for simulation. Selecting input distributions, evaluating simulation output. prereq: Upper div or grad student; familiarity with probability/statistics recommended
PUBH 7461 - Exploring and Visualizing Data in R
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.
PUBH 7475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended
STAT 5303 - Designing Experiments
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Analysis of variance. Multiple comparisons. Variance-stabilizing transformations. Contrasts. Construction/analysis of complete/incomplete block designs. Fractional factorial designs. Confounding split plots. Response surface design. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.
STAT 5421 - Analysis of Categorical Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Varieties of categorical data, cross-classifications, contingency tables. Tests for independence. Combining 2x2 tables. Multidimensional tables/loglinear models. Maximum-likelihood estimation. Tests for goodness of fit. Logistic regression. Generalized linear/multinomial-response models. prereq: STAT 3022 or 3032 or 3301 or 5302 or 4051 or 8051 or 5102 or 4102
STAT 5511 - Time Series Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102
STAT 5601 - Nonparametric Methods
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: Stat classes 3032 or 3022 or 4102 or 5021 or 5102 or instr consent
IE 5531 - Engineering Optimization I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Linear programming, simplex method, duality theory, sensitivity analysis, interior point methods, integer programming, branch/bound/dynamic programming. Emphasizes applications in production/logistics, including resource allocation, transportation, facility location, networks/flows, scheduling, production planning. prereq: Upper div or grad student or CNR
IE 5532 - Stochastic Models
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to stochastic modeling and stochastic processes. Probability review, random variables, discrete- and continuous-time Markov chains, queueing systems, simulation. Applications to industrial and systems engineering including production and inventory control. prereq: Undergraduate probability and statistics. Familiarity with computer programming in a high level language.
IE 5511 - Human Factors and Work Analysis
Credits: 4.0 [max 4.0]
Course Equivalencies: HumF 5211/IE 5511/ME 5211
Grading Basis: A-F or Aud
Typically offered: Every Fall
Human factors engineering (ergonomics), methods engineering, and work measurement. Human-machine interface: displays, controls, instrument layout, and supervisory control. Anthropometry, work physiology and biomechanics. Work environmental factors: noise, illumination, toxicology. Methods engineering, including operations analysis, motion study, and time standards. prereq: Upper div CSE or grad student
IE 5545 - Decision Analysis
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Single-person and group decision problems. Structuring of decision problems arising in personal, business, and public policy contexts. Decision-making under uncertainty, value of information, games of complete information and Nash equilibrium, Bayesian games, group decision-making and distributed consensus, basics of mechanism design. prereq: 3521 or equiv
IE 5551 - Production and Inventory Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Inventory control, supply chain management, demand forecasting, capacity planning, aggregate production and material requirement planning, operations scheduling, and shop floor control. Quantitative models used to support decisions. Implications of emerging information technologies and of electronic commerce for supply chain management and factory operation. prereq: CNR or upper div or grad student
IE 8773 - Graduate Seminar
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Recent developments.
IE 8774 - Graduate Seminar
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Recent developments. prereq: 8773
APEC 8001 - Applied Microeconomic Analysis of Consumer Choice and Consumer Demand
Credits: 2.0 [max 2.0]
Course Equivalencies: ApEc 8001/Econ 8001/Econ 8101
Grading Basis: A-F or Aud
Typically offered: Every Fall
The course provides a rigorous mathematical treatment of cost-benefit analysis in terms of the theory of how prices, income, preferences, and other factors affect consumer choices and the demand for goods and services. The optimization theories and economic models are developed with and without uncertainty. Part of four-course, year-long sequence (APEC 8001-2-3-4) prereq: [[5151 or ECON 3101 or ECON 5151 or intermediate microeconomic theory], [[MATH 2243, MATH 2263] or equiv]] or instr consent
APEC 8002 - Applied Microeconomic Analysis of Production and Choice Under Uncertainty
Credits: 2.0 [max 2.0]
Course Equivalencies: ApEc 8002/Econ 8002/Econ 8102
Grading Basis: A-F or Aud
Typically offered: Every Fall
The course provides a rigorous mathematical treatment of cost-benefit analysis in terms of the theory of how prices, technology, and other important factors affect producer decisions, the supply of goods and services, and the demand for productive resources. The optimization theories and economic models are developed with and without uncertainty. The course also explores the theory of price determination in competitive, monopoly, and monopsony markets. Part of four-course, year-long sequence (APEC 8001-2-3-4) prereq: [[8001 or ECON 8001 or ECON 8101], [[MATH 2243, MATH 2263] or equiv]] or instr consent
APEC 8206 - Dynamic Optimization: Applications in Economics and Management
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Formulation and solution of dynamic optimization problems using optimal control theory and dynamic programming. Analytical and numerical solution methods to solve deterministic and stochastic problems for various economic applications. prereq: 5151 or equiv or instr consent
APEC 8211 - Econometric Analysis I
Credits: 2.0 [max 4.0]
Typically offered: Every Fall
The course introduces the foundations for statistical economic (econometric) models, linear econometric models, and inference with linear econometric models when observations are independent and the sample size is large. it shows how linear models can be used to evaluate and quantify theoretical relationships and forecast counterfactual economic outcomes. Part of four-course, year-long sequence (APEC 8211-2-3-4). prereq: ApEc 5031 or equiv OR Ph.D. student OR instr consent
CSCI 5421 - Advanced Algorithms and Data Structures
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
CSCI 5801 - Software Engineering I
Credits: 3.0 [max 3.0]
Prerequisites: 2041 or #
Typically offered: Every Fall
Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance. prereq: 2041 or instr consent
ECON 8117 - Noncooperative Game Theory
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Solution concepts for noncooperative games in normal form, including Nash and perfect equilibrium and stable sets of equilibria. Extensive form games of perfect and incomplete information, sequential equilibrium, and consequences of stability for extensive form. Applications including bargaining and auctions. Seven-week course. prereq: Math 5616 or equiv or instr consent
HINF 5502 - Python Programming Essentials for the Health Sciences
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Computer programming essentials for health sciences/health care applications using Python 3. Intended for students with limited programming background, or students wishing to obtain proficiency in Python programming language. prereq: Junior or senior or grad student or professional student or instr consent
MATH 5615H - Honors: Introduction to Analysis I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Axiomatic treatment of real/complex number systems. Introduction to metric spaces: convergence, connectedness, compactness. Convergence of sequences/series of real/complex numbers, Cauchy criterion, root/ratio tests. Continuity in metric spaces. Rigorous treatment of differentiation of single-variable functions, Taylor's Theorem. prereq: [[2243 or 2373], [2263 or 2374], [2283 or 3283]] or 2574
MATH 5616H - Honors: Introduction to Analysis II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Rigorous treatment of Riemann-Stieltjes integration. Sequences/series of functions, uniform convergence, equicontinuous families, Stone-Weierstrass Theorem, power series. Rigorous treatment of differentiation/integration of multivariable functions, Implicit Function Theorem, Stokes' Theorem. Additional topics as time permits. prereq: 5615
MATH 8651 - Theory of Probability Including Measure Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Probability spaces. Distributions/expectations of random variables. Basic theorems of Lebesque theory. Stochastic independence, sums of independent random variables, random walks, filtrations. Probability, moment generating functions, characteristic functions. Laws of large numbers. prereq: 5616 or instr consent
MBA 6031 - Financial Accounting
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Basic principles of financial accounting, involving the consecution/interpretation of corporate financial statements. prereq: MBA or Mgmt Sci MBA Student
MBA 6221 - Supply Chain & Operations
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Supply chain and operations are at the core of how organizations deliver value to their customers. Effectively matching supply and demand is key to the success of any organization and world-class operations can lead to a significant and enduring competitive advantage. In contrast, poorly managed operations and supply chains can result in low customer satisfaction and diminished profit margins, ultimately leading to company failure in the long run. Beyond generating profits, companies around the world are also facing increasing pressure to perform well on the other two dimensions that constitute the ?triple bottom line?, namely people and the planet. By taking an ?end-to-end? view, we will explore a variety of topics related to managing today?s global supply chains, including environmental and social responsibility. The specific questions this course will address include: How can supply chain and operations help firms succeed? What are the issues and trade-offs confronting supply chain and operations managers? What tools and frameworks can managers use to tackle these challenges and develop and sustain a competitive advantage? What are the emergent environmental and social responsibility challenges facing supply chain managers and how should they address them? Topics covered: operations strategy, process analysis, statistical process control, lean operations, forecasting, inventory management under certain demand, sourcing, environmental and social responsibility in supply chains prereq: MBA student
MGMT 6004 - Negotiation Strategies
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
At its core, negotiation is the art and science of getting what you want in a world of innumerable interests, possibilities, and standards of fairness---a world in which we must often compete or cooperate with others to do anything from picking a restaurant to transforming markets. The objective of this course is to equip students with a simple, ready-to-use framework from which we can prepare for and engage in negotiations. Topics include interest-based bargaining, psychological biases, multiparty negotiations, and hard tactics. Regular cases and exercises reinforce our negotiation framework and provide students a safe forum to thoughtfully reflect on their experiences and improve. prereq: MBA or Mgmt Sci MBA student
MILI 6985 - The Health Care Marketplace
Credits: 2.0 [max 2.0]
Course Equivalencies: MILI 5990/6990/3585/5585
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Survey of trillion dollar medical industry. Physician/hospital services, insurance, pharmaceuticals, medical devices, information technology. Scale, interactions, inter-relationships, market opportunities, barriers. prereq: MBA student
MKTG 8810 - Consumer Behavior Special Topics
Credits: 2.0 [max 8.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Topics related to the fundamentals of consumer behavior such as attitudes, behavioral research methods, branding, consumer well-being, decision making, information processing, and perceptions. See "Class Notes" for details. prereq: Doctoral student or [master's program student, instr consent]
MOT 5001 - Technological Business Fundamentals
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Provides scientists and engineers with a working knowledge of the broader business context in which science and technology ideas are translated into solutions that address market needs and generate economic value. This two-unit course will broaden students? business knowledge and project leadership abilities, enabling technical professionals to increase their business impact and career success. The three modules of the course will build practical knowledge and skills in (1) project leadership, professionalism, teamwork, and effective communication, (2) the process of innovation (i.e., transforming technical ideas into value-creating solutions) and (3) business acumen fundamentals. prereq: Degree seeking or non-degree graduate students
MOT 5002 - Creating Technological Innovation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This hands-on, project-based course provides students the perspective of a Technology Leader of an organization or product team. Details the innovation process, from an idea's inception through impact in the economy, regardless of organizational setting. Explores how solutions are developed to become ready for broader market deployment. Includes testing and development of the problem-solution fit, probing of solutions for robustness, and testing of both technical and operational scaling of proposed solutions. Examines the human aspects of innovation, specifically issues of team building and readiness. Considers the broader system for innovation, including the role of key stakeholders in shaping its success in order to arrive at an impactful solution. Addresses intellectual property, the effect of regulations and social and cultural differences across varied global markets, and the personal skills necessary to align and manage these issues. prereq: Degree seeking or non-degree graduate students.
PUBH 6325 - Data Processing with PC-SAS
Credits: 1.0 [max 1.0]
Typically offered: Every Spring
Introduction to methods for transferring/processing existing data sources. Emphasizes hands-on approach to pre-statistical data processing and analysis with PC-SAS statistical software with a Microsoft Windows operating system.
PUBH 7461 - Exploring and Visualizing Data in R
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.
PUBH 7475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended
SCO 6041 - Project Management
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Companies in a wide-range of industries (such as agri-business, aerospace, construction, manufacturing, and medical technology) use Project Management for New Product Development, implementing strategic initiatives, and other business objectives. In the course of your career, those in business, government, and even non-profit organizations will spend a significant amount of their professional career either participating in, or leading projects. While every project is by definition unique in scope, some concepts and tools are considered industry best practices and are internationally recognized via the certification programs of the Project Management Institute. The course will focus on scheduling and critical path analysis, time management, cost estimating, resource utilization, and risk management. Specific tools will include Earned Value Management and the quantitative techniques for estimating schedule risk. The latter will include estimating task durations and the probabilities for project completion by specific time periods. The course will conclude an introduction of Agile Methodologies and Scrum.
SCO 6056 - Managing Supply Chain Operations
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Decisions/trade-offs managers face when directing operations of supply chain. How supply chain operations are coordinated within manufacturing, distribution, and retail organizations. prereq: [MBA 6220 or equiv], MBA student
SCO 6059 - Quality Management and Lean Six Sigma
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Management/technical aspects of process improvement. Organizational performance and financial measures as they relate to process improvement. Strategy, improvement tools/methods. prereq: [MBA 6220 or equiv], MBA student
SCO 6072 - Managing Technologies in the Supply Chain
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Course prepares students to develop capabilities for (i) making well-informed technology choice decisions; (ii) effectively managing the development and implementation of technologies; and (iii) collaboratively engaging in crisis management and problem solving during technology development and implementation. The central question around which the course is organized is: How can existing and emerging technologies (e.g., IoT, automation, artificial intelligence, 3D printing, block chains) and the related process and people issues be managed to design and sustain reliable, responsive, resilient, and responsible supply chains? Analytic methods covered in the course to inform decisions related to the development and implementation of technologies include statistical methods (e.g., multivariate regression, time-series analysis, hazard models), risk analysis methods (e.g., decision trees) and predictive analytic methods (e.g., random forest). Through a combination of operations analysis case studies and hands-on exercises, students learn to evaluate the potential upside and downside risks of existing and emerging technologies. The final course project involves designing and testing of prototype systems for evaluating the development and implementation in supply chain and operations settings of companies.
STAT 5021 - Statistical Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Intensive introduction to statistical methods for graduate students needing statistics as a research technique. prereq: college algebra or instr consent; credit will not be granted if credit has been received for STAT 3011
STAT 5302 - Applied Regression Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.
IE 5080 - Topics in Industrial Engineering
Credits: 1.0 -4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Topics vary each semester.
IE 5111 - Systems Engineering I
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Overview of systems-level thinking/techniques in context of an integrated, design-oriented framework. Elements of systems engineering process, including lifecycle, concurrent, and global engineering. Framework for engineering large-scale, complex systems. How specific techniques fit into framework. prereq: CSE upper div or grad student
IE 5113 - Systems Engineering II
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Systems engineering thinking/techniques presented in 5111. Hands-on techniques applied to specific problems. Topics pertinent to effectiveness of design process. Practices and organizational/reward structure to support collaborative, globally distributed design team.
IE 5441 - Financial Decision Making
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Cash flow streams, interest rates, fixed income securities. Evaluating investment alternatives, capital budgeting, dynamic cash flow process. Mean-variance portfolio selection, Capital Asset Pricing Model, utility maximization, risk aversion. Derivative securities, asset dynamics, basic option pricing theory. prereq: CSE upper div or grad student
IE 5511 - Human Factors and Work Analysis
Credits: 4.0 [max 4.0]
Course Equivalencies: HumF 5211/IE 5511/ME 5211
Grading Basis: A-F or Aud
Typically offered: Every Fall
Human factors engineering (ergonomics), methods engineering, and work measurement. Human-machine interface: displays, controls, instrument layout, and supervisory control. Anthropometry, work physiology and biomechanics. Work environmental factors: noise, illumination, toxicology. Methods engineering, including operations analysis, motion study, and time standards. prereq: Upper div CSE or grad student
IE 5513 - Engineering Safety
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Occupational, health, and product safety. Standards, laws, and regulations. Hazards and their engineering control, including general principles, tools and machines, mechanics and structures, electrical safety, materials handling, fire safety, and chemicals. Human behavior and safety, procedures and training, warnings and instructions. prereq: Upper div CSE or grad student
IE 5522 - Quality Engineering and Reliability
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 3522/IE 5522
Typically offered: Periodic Fall & Spring
Quality engineering/management, economics of quality, statistical process control design of experiments, reliability, maintainability, availability. prereq: [4521 or equiv], [upper div or grad student or CNR]
IE 5541 - Project Management
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 4541/IE 5541
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Introduction to engineering project management. Analytical methods of selecting, organizing, budgeting, scheduling, and controlling projects, including risk management, team leadership, and program management. prereq: Upper div or grad student
IE 5545 - Decision Analysis
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Single-person and group decision problems. Structuring of decision problems arising in personal, business, and public policy contexts. Decision-making under uncertainty, value of information, games of complete information and Nash equilibrium, Bayesian games, group decision-making and distributed consensus, basics of mechanism design. prereq: 3521 or equiv
IE 5551 - Production and Inventory Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Inventory control, supply chain management, demand forecasting, capacity planning, aggregate production and material requirement planning, operations scheduling, and shop floor control. Quantitative models used to support decisions. Implications of emerging information technologies and of electronic commerce for supply chain management and factory operation. prereq: CNR or upper div or grad student
IE 5553 - Simulation
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 3553/IE 5553
Typically offered: Periodic Fall & Spring
Discrete event simulation. Using integrated simulation/animation environment to create, analyze, and evaluate realistic models for various industry settings, including manufacturing/service operations and systems engineering. Experimental design for simulation. Selecting input distributions, evaluating simulation output. prereq: Upper div or grad student; familiarity with probability/statistics recommended
IE 5561 - Analytics and Data-Driven Decision Making
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Hands-on experience with modern methods for analytics and data-driven decision making. Methodologies such as linear and integer optimization and supervised and unsupervised learning will be brought together to address problems in a variety of areas such as healthcare, agriculture, sports, energy, and finance. Students will learn how to manipulate data, build and solve models, and interpret and visualize results using a high-level, dynamic programming language. Prerequisites: IE 3521 or equivalent; IE 3011 or IE 5531 or equivalent; proficiency with a programming language such as R, Python, or C.
IE 5773 - Practice-focused Seminar
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall
Industry and academic speakers, topics relevant to analytics practice.
IE 5801 - Capstone Project
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Students work on ISyE Analytics Track capstone project in small teams of two or three. Projects are supervised by industry mentor and faculty adviser. Projects involve application of techniques from Analytics Track curriculum. Prerequisites: ISyE Analytics Track MS Student; IE 5531; IE 5561; Stat 5302; CSci 5521 or 5523.
IE 8521 - Optimization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Theory and applications of linear and nonlinear optimization. Linear optimization: simplex method, convex analysis, interior point method, duality theory. Nonlinear optimization: interior point methods and first-order methods, convergence and complexity analysis. Applications in engineering, economics, and business problems. prereq: Familiarity with linear algebra and calculus.
IE 8531 - Discrete Optimization
Credits: 4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Topics in integer programming and combinatorial optimization. Formulation of models, branch-and-bound. Cutting plane and branch-and-cut algorithms. Polyhedral combinatorics. Heuristic approaches. Introduction to computational complexity.
IE 8532 - Stochastic Processes and Queuing Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to stochastic modeling and processes. Random variables, discrete and continuous Markov chains, renewal processes, queuing systems, Brownian motion, and elements of reliability and stochastic simulation. Applications to design, planning, and control of manufacturing and production systems. prereq: 4521 or equiv
IE 8533 - Advanced Stochastic Processes and Queuing Systems
Credits: 4.0 [max 4.0]
Typically offered: Periodic Spring
Renewal and generative processes, Markov and semi-Markov processes, martingales, queuing theory, queuing networks, computational methods, fluid models, Brownian motion. prereq: 8532 or instr consent
IE 8534 - Advanced Topics in Operations Research
Credits: 1.0 -4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Special topics determined by instructor. Examples include Markov decision processes, stochastic programming, integer/combinatorial optimization, and queueing networks.
IE 8552 - Advanced Topics in Production, Inventory, and Distribution Systems
Credits: 4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Cutting edge research issues in production, inventory, distribution systems. Stochastic models of manufacturing systems, stochastic inventory theory, multi-echelon inventory systems/supply chains, supplier-retailer/supplier-manufacturer coordination, supplier/warehouse networks, business logistics, transportation. prereq: 5551
IE 8777 - Thesis Credits: Master's
Credits: 1.0 -18.0 [max 50.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
(No description) prereq: Max 18 cr per semester or summer; 10 cr total required (Plan A only)
IE 8794 - Industrial Engineering Research
Credits: 1.0 -6.0 [max 10.0]
Typically offered: Every Fall, Spring & Summer
Directed research. prereq: instr consent
IE 5111 - Systems Engineering I
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Overview of systems-level thinking/techniques in context of an integrated, design-oriented framework. Elements of systems engineering process, including lifecycle, concurrent, and global engineering. Framework for engineering large-scale, complex systems. How specific techniques fit into framework. prereq: CSE upper div or grad student
IE 5113 - Systems Engineering II
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Systems engineering thinking/techniques presented in 5111. Hands-on techniques applied to specific problems. Topics pertinent to effectiveness of design process. Practices and organizational/reward structure to support collaborative, globally distributed design team.
IE 5541 - Project Management
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 4541/IE 5541
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Introduction to engineering project management. Analytical methods of selecting, organizing, budgeting, scheduling, and controlling projects, including risk management, team leadership, and program management. prereq: Upper div or grad student
IE 5553 - Simulation
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 3553/IE 5553
Typically offered: Periodic Fall & Spring
Discrete event simulation. Using integrated simulation/animation environment to create, analyze, and evaluate realistic models for various industry settings, including manufacturing/service operations and systems engineering. Experimental design for simulation. Selecting input distributions, evaluating simulation output. prereq: Upper div or grad student; familiarity with probability/statistics recommended
APEC 8001 - Applied Microeconomic Analysis of Consumer Choice and Consumer Demand
Credits: 2.0 [max 2.0]
Course Equivalencies: ApEc 8001/Econ 8001/Econ 8101
Grading Basis: A-F or Aud
Typically offered: Every Fall
The course provides a rigorous mathematical treatment of cost-benefit analysis in terms of the theory of how prices, income, preferences, and other factors affect consumer choices and the demand for goods and services. The optimization theories and economic models are developed with and without uncertainty. Part of four-course, year-long sequence (APEC 8001-2-3-4) prereq: [[5151 or ECON 3101 or ECON 5151 or intermediate microeconomic theory], [[MATH 2243, MATH 2263] or equiv]] or instr consent
APEC 8002 - Applied Microeconomic Analysis of Production and Choice Under Uncertainty
Credits: 2.0 [max 2.0]
Course Equivalencies: ApEc 8002/Econ 8002/Econ 8102
Grading Basis: A-F or Aud
Typically offered: Every Fall
The course provides a rigorous mathematical treatment of cost-benefit analysis in terms of the theory of how prices, technology, and other important factors affect producer decisions, the supply of goods and services, and the demand for productive resources. The optimization theories and economic models are developed with and without uncertainty. The course also explores the theory of price determination in competitive, monopoly, and monopsony markets. Part of four-course, year-long sequence (APEC 8001-2-3-4) prereq: [[8001 or ECON 8001 or ECON 8101], [[MATH 2243, MATH 2263] or equiv]] or instr consent
APEC 8206 - Dynamic Optimization: Applications in Economics and Management
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Formulation and solution of dynamic optimization problems using optimal control theory and dynamic programming. Analytical and numerical solution methods to solve deterministic and stochastic problems for various economic applications. prereq: 5151 or equiv or instr consent
APEC 8211 - Econometric Analysis I
Credits: 2.0 [max 4.0]
Typically offered: Every Fall
The course introduces the foundations for statistical economic (econometric) models, linear econometric models, and inference with linear econometric models when observations are independent and the sample size is large. it shows how linear models can be used to evaluate and quantify theoretical relationships and forecast counterfactual economic outcomes. Part of four-course, year-long sequence (APEC 8211-2-3-4). prereq: ApEc 5031 or equiv OR Ph.D. student OR instr consent
CSCI 5421 - Advanced Algorithms and Data Structures
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Fundamental paradigms of algorithm and data structure design. Divide-and-conquer, dynamic programming, greedy method, graph algorithms, amortization, priority queues and variants, search structures, disjoint-set structures. Theoretical underpinnings. Examples from various problem domains. prereq: 4041 or instr consent
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
CSCI 5801 - Software Engineering I
Credits: 3.0 [max 3.0]
Prerequisites: 2041 or #
Typically offered: Every Fall
Advanced introduction to software engineering. Software life cycle, development models, software requirements analysis, software design, coding, maintenance. prereq: 2041 or instr consent
ECON 8117 - Noncooperative Game Theory
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Solution concepts for noncooperative games in normal form, including Nash and perfect equilibrium and stable sets of equilibria. Extensive form games of perfect and incomplete information, sequential equilibrium, and consequences of stability for extensive form. Applications including bargaining and auctions. Seven-week course. prereq: Math 5616 or equiv or instr consent
HINF 5502 - Python Programming Essentials for the Health Sciences
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Computer programming essentials for health sciences/health care applications using Python 3. Intended for students with limited programming background, or students wishing to obtain proficiency in Python programming language. prereq: Junior or senior or grad student or professional student or instr consent
MATH 5615H - Honors: Introduction to Analysis I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Axiomatic treatment of real/complex number systems. Introduction to metric spaces: convergence, connectedness, compactness. Convergence of sequences/series of real/complex numbers, Cauchy criterion, root/ratio tests. Continuity in metric spaces. Rigorous treatment of differentiation of single-variable functions, Taylor's Theorem. prereq: [[2243 or 2373], [2263 or 2374], [2283 or 3283]] or 2574
MATH 5616H - Honors: Introduction to Analysis II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Rigorous treatment of Riemann-Stieltjes integration. Sequences/series of functions, uniform convergence, equicontinuous families, Stone-Weierstrass Theorem, power series. Rigorous treatment of differentiation/integration of multivariable functions, Implicit Function Theorem, Stokes' Theorem. Additional topics as time permits. prereq: 5615
MATH 8651 - Theory of Probability Including Measure Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Probability spaces. Distributions/expectations of random variables. Basic theorems of Lebesque theory. Stochastic independence, sums of independent random variables, random walks, filtrations. Probability, moment generating functions, characteristic functions. Laws of large numbers. prereq: 5616 or instr consent
MBA 6031 - Financial Accounting
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Basic principles of financial accounting, involving the consecution/interpretation of corporate financial statements. prereq: MBA or Mgmt Sci MBA Student
MBA 6221 - Supply Chain & Operations
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Supply chain and operations are at the core of how organizations deliver value to their customers. Effectively matching supply and demand is key to the success of any organization and world-class operations can lead to a significant and enduring competitive advantage. In contrast, poorly managed operations and supply chains can result in low customer satisfaction and diminished profit margins, ultimately leading to company failure in the long run. Beyond generating profits, companies around the world are also facing increasing pressure to perform well on the other two dimensions that constitute the ?triple bottom line?, namely people and the planet. By taking an ?end-to-end? view, we will explore a variety of topics related to managing today?s global supply chains, including environmental and social responsibility. The specific questions this course will address include: How can supply chain and operations help firms succeed? What are the issues and trade-offs confronting supply chain and operations managers? What tools and frameworks can managers use to tackle these challenges and develop and sustain a competitive advantage? What are the emergent environmental and social responsibility challenges facing supply chain managers and how should they address them? Topics covered: operations strategy, process analysis, statistical process control, lean operations, forecasting, inventory management under certain demand, sourcing, environmental and social responsibility in supply chains prereq: MBA student
MGMT 6004 - Negotiation Strategies
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
At its core, negotiation is the art and science of getting what you want in a world of innumerable interests, possibilities, and standards of fairness---a world in which we must often compete or cooperate with others to do anything from picking a restaurant to transforming markets. The objective of this course is to equip students with a simple, ready-to-use framework from which we can prepare for and engage in negotiations. Topics include interest-based bargaining, psychological biases, multiparty negotiations, and hard tactics. Regular cases and exercises reinforce our negotiation framework and provide students a safe forum to thoughtfully reflect on their experiences and improve. prereq: MBA or Mgmt Sci MBA student
MILI 6985 - The Health Care Marketplace
Credits: 2.0 [max 2.0]
Course Equivalencies: MILI 5990/6990/3585/5585
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Survey of trillion dollar medical industry. Physician/hospital services, insurance, pharmaceuticals, medical devices, information technology. Scale, interactions, inter-relationships, market opportunities, barriers. prereq: MBA student
MKTG 8810 - Consumer Behavior Special Topics
Credits: 2.0 [max 8.0]
Grading Basis: A-F or Aud
Typically offered: Periodic Fall & Spring
Topics related to the fundamentals of consumer behavior such as attitudes, behavioral research methods, branding, consumer well-being, decision making, information processing, and perceptions. See "Class Notes" for details. prereq: Doctoral student or [master's program student, instr consent]
MOT 5001 - Technological Business Fundamentals
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Provides scientists and engineers with a working knowledge of the broader business context in which science and technology ideas are translated into solutions that address market needs and generate economic value. This two-unit course will broaden students? business knowledge and project leadership abilities, enabling technical professionals to increase their business impact and career success. The three modules of the course will build practical knowledge and skills in (1) project leadership, professionalism, teamwork, and effective communication, (2) the process of innovation (i.e., transforming technical ideas into value-creating solutions) and (3) business acumen fundamentals. prereq: Degree seeking or non-degree graduate students
MOT 5002 - Creating Technological Innovation
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
This hands-on, project-based course provides students the perspective of a Technology Leader of an organization or product team. Details the innovation process, from an idea's inception through impact in the economy, regardless of organizational setting. Explores how solutions are developed to become ready for broader market deployment. Includes testing and development of the problem-solution fit, probing of solutions for robustness, and testing of both technical and operational scaling of proposed solutions. Examines the human aspects of innovation, specifically issues of team building and readiness. Considers the broader system for innovation, including the role of key stakeholders in shaping its success in order to arrive at an impactful solution. Addresses intellectual property, the effect of regulations and social and cultural differences across varied global markets, and the personal skills necessary to align and manage these issues. prereq: Degree seeking or non-degree graduate students.
PUBH 6325 - Data Processing with PC-SAS
Credits: 1.0 [max 1.0]
Typically offered: Every Spring
Introduction to methods for transferring/processing existing data sources. Emphasizes hands-on approach to pre-statistical data processing and analysis with PC-SAS statistical software with a Microsoft Windows operating system.
PUBH 7461 - Exploring and Visualizing Data in R
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.
PUBH 7475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended
SCO 6041 - Project Management
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Companies in a wide-range of industries (such as agri-business, aerospace, construction, manufacturing, and medical technology) use Project Management for New Product Development, implementing strategic initiatives, and other business objectives. In the course of your career, those in business, government, and even non-profit organizations will spend a significant amount of their professional career either participating in, or leading projects. While every project is by definition unique in scope, some concepts and tools are considered industry best practices and are internationally recognized via the certification programs of the Project Management Institute. The course will focus on scheduling and critical path analysis, time management, cost estimating, resource utilization, and risk management. Specific tools will include Earned Value Management and the quantitative techniques for estimating schedule risk. The latter will include estimating task durations and the probabilities for project completion by specific time periods. The course will conclude an introduction of Agile Methodologies and Scrum.
SCO 6056 - Managing Supply Chain Operations
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Decisions/trade-offs managers face when directing operations of supply chain. How supply chain operations are coordinated within manufacturing, distribution, and retail organizations. prereq: [MBA 6220 or equiv], MBA student
SCO 6059 - Quality Management and Lean Six Sigma
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Management/technical aspects of process improvement. Organizational performance and financial measures as they relate to process improvement. Strategy, improvement tools/methods. prereq: [MBA 6220 or equiv], MBA student
SCO 6072 - Managing Technologies in the Supply Chain
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
Course prepares students to develop capabilities for (i) making well-informed technology choice decisions; (ii) effectively managing the development and implementation of technologies; and (iii) collaboratively engaging in crisis management and problem solving during technology development and implementation. The central question around which the course is organized is: How can existing and emerging technologies (e.g., IoT, automation, artificial intelligence, 3D printing, block chains) and the related process and people issues be managed to design and sustain reliable, responsive, resilient, and responsible supply chains? Analytic methods covered in the course to inform decisions related to the development and implementation of technologies include statistical methods (e.g., multivariate regression, time-series analysis, hazard models), risk analysis methods (e.g., decision trees) and predictive analytic methods (e.g., random forest). Through a combination of operations analysis case studies and hands-on exercises, students learn to evaluate the potential upside and downside risks of existing and emerging technologies. The final course project involves designing and testing of prototype systems for evaluating the development and implementation in supply chain and operations settings of companies.
STAT 5021 - Statistical Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Intensive introduction to statistical methods for graduate students needing statistics as a research technique. prereq: college algebra or instr consent; credit will not be granted if credit has been received for STAT 3011
STAT 5302 - Applied Regression Analysis
Credits: 4.0 [max 4.0]
Typically offered: Every Fall, Spring & Summer
Simple, multiple, and polynomial regression. Estimation, testing, prediction. Use of graphics in regression. Stepwise and other numerical methods. Weighted least squares, nonlinear models, response surfaces. Experimental research/applications. prereq: 3032 or 3022 or 4102 or 5021 or 5102 or instr consent Please note this course generally does not count in the Statistical Practice BA or Statistical Science BS degrees. Please consult with a department advisor with questions.
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.
IE 5080 - Topics in Industrial Engineering
Credits: 1.0 -4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Topics vary each semester.
IE 5441 - Financial Decision Making
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Cash flow streams, interest rates, fixed income securities. Evaluating investment alternatives, capital budgeting, dynamic cash flow process. Mean-variance portfolio selection, Capital Asset Pricing Model, utility maximization, risk aversion. Derivative securities, asset dynamics, basic option pricing theory. prereq: CSE upper div or grad student
IE 5513 - Engineering Safety
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall & Spring
Occupational, health, and product safety. Standards, laws, and regulations. Hazards and their engineering control, including general principles, tools and machines, mechanics and structures, electrical safety, materials handling, fire safety, and chemicals. Human behavior and safety, procedures and training, warnings and instructions. prereq: Upper div CSE or grad student
IE 5522 - Quality Engineering and Reliability
Credits: 4.0 [max 4.0]
Course Equivalencies: IE 3522/IE 5522
Typically offered: Periodic Fall & Spring
Quality engineering/management, economics of quality, statistical process control design of experiments, reliability, maintainability, availability. prereq: [4521 or equiv], [upper div or grad student or CNR]
IE 5531 - Engineering Optimization I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Linear programming, simplex method, duality theory, sensitivity analysis, interior point methods, integer programming, branch/bound/dynamic programming. Emphasizes applications in production/logistics, including resource allocation, transportation, facility location, networks/flows, scheduling, production planning. prereq: Upper div or grad student or CNR
IE 5532 - Stochastic Models
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to stochastic modeling and stochastic processes. Probability review, random variables, discrete- and continuous-time Markov chains, queueing systems, simulation. Applications to industrial and systems engineering including production and inventory control. prereq: Undergraduate probability and statistics. Familiarity with computer programming in a high level language.
IE 5545 - Decision Analysis
Credits: 4.0 [max 4.0]
Typically offered: Periodic Fall & Spring
Single-person and group decision problems. Structuring of decision problems arising in personal, business, and public policy contexts. Decision-making under uncertainty, value of information, games of complete information and Nash equilibrium, Bayesian games, group decision-making and distributed consensus, basics of mechanism design. prereq: 3521 or equiv
IE 5551 - Production and Inventory Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Inventory control, supply chain management, demand forecasting, capacity planning, aggregate production and material requirement planning, operations scheduling, and shop floor control. Quantitative models used to support decisions. Implications of emerging information technologies and of electronic commerce for supply chain management and factory operation. prereq: CNR or upper div or grad student
IE 5561 - Analytics and Data-Driven Decision Making
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Hands-on experience with modern methods for analytics and data-driven decision making. Methodologies such as linear and integer optimization and supervised and unsupervised learning will be brought together to address problems in a variety of areas such as healthcare, agriculture, sports, energy, and finance. Students will learn how to manipulate data, build and solve models, and interpret and visualize results using a high-level, dynamic programming language. Prerequisites: IE 3521 or equivalent; IE 3011 or IE 5531 or equivalent; proficiency with a programming language such as R, Python, or C.
IE 5773 - Practice-focused Seminar
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall
Industry and academic speakers, topics relevant to analytics practice.
IE 5801 - Capstone Project
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall
Students work on ISyE Analytics Track capstone project in small teams of two or three. Projects are supervised by industry mentor and faculty adviser. Projects involve application of techniques from Analytics Track curriculum. Prerequisites: ISyE Analytics Track MS Student; IE 5531; IE 5561; Stat 5302; CSci 5521 or 5523.
IE 8521 - Optimization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Theory and applications of linear and nonlinear optimization. Linear optimization: simplex method, convex analysis, interior point method, duality theory. Nonlinear optimization: interior point methods and first-order methods, convergence and complexity analysis. Applications in engineering, economics, and business problems. prereq: Familiarity with linear algebra and calculus.
IE 8531 - Discrete Optimization
Credits: 4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Topics in integer programming and combinatorial optimization. Formulation of models, branch-and-bound. Cutting plane and branch-and-cut algorithms. Polyhedral combinatorics. Heuristic approaches. Introduction to computational complexity.
IE 8532 - Stochastic Processes and Queuing Systems
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to stochastic modeling and processes. Random variables, discrete and continuous Markov chains, renewal processes, queuing systems, Brownian motion, and elements of reliability and stochastic simulation. Applications to design, planning, and control of manufacturing and production systems. prereq: 4521 or equiv
IE 8533 - Advanced Stochastic Processes and Queuing Systems
Credits: 4.0 [max 4.0]
Typically offered: Periodic Spring
Renewal and generative processes, Markov and semi-Markov processes, martingales, queuing theory, queuing networks, computational methods, fluid models, Brownian motion. prereq: 8532 or instr consent
IE 8534 - Advanced Topics in Operations Research
Credits: 1.0 -4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Special topics determined by instructor. Examples include Markov decision processes, stochastic programming, integer/combinatorial optimization, and queueing networks.
IE 8552 - Advanced Topics in Production, Inventory, and Distribution Systems
Credits: 4.0 [max 8.0]
Typically offered: Periodic Fall & Spring
Cutting edge research issues in production, inventory, distribution systems. Stochastic models of manufacturing systems, stochastic inventory theory, multi-echelon inventory systems/supply chains, supplier-retailer/supplier-manufacturer coordination, supplier/warehouse networks, business logistics, transportation. prereq: 5551