Twin Cities campus
 
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
  • Program Type: Master's
  • Requirements for this program are current for Fall 2020
  • 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) 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. The Department of Industrial & Systems Engineering offers an MS degree with three tracks: Analytics; Industrial Engineering; or Systems Engineering. MS degree applicants must indicate which track they are applying for on the application form. Note that the admission requirements for the three tracks are different. In addition, the ISyE program also offers a dual MS in ISyE and Civil Engineering (Transportation Engineering focus) and an integrated bachelor's/master's program.
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.
Other requirements to be completed before admission:
Eligibility requirements for the integrated BS/MS program: -Students must be enrolled in the Industrial and Systems Engineering undergraduate program at the University of Minnesota Twin Cities. -Applicants must 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.
Special Application Requirements:
All application materials should be submitted electronically through the Graduate Admissions Office. Applicants to the systems engineering track are required to have at least two years of professional work experience in a technical field. Promising candidates with less experience will be considered under exceptional circumstances. Applicants must submit a personal statement and three letters of recommendation. In addition to the academic record, the professional record of the applicant and the letters of recommendation carry weight in admission decisions. A GRE score is not required for applicants to the systems engineering track. Applicants to the industrial engineering and analytics tracks must submit a GRE score. Letters of recommendation are not required, but are highly recommended if you want to be considered for financial aid. Applications for the analytics track are accepted for fall semester only. The 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 16 to 24 major credits and 6 to 14 credits outside the major. The final exam is oral.
Plan C: Plan C requires 14 to 26 major credits and 6 to 16 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 must be taken on the A/F grade basis, unless only offered S/N, with a minimum grade of B- earned for each course. The Master of Science in Industrial and Systems Engineering (M.S.I.Sy.E.) is offered with three tracks: Analytics, Industrial Engineering, or Systems Engineering. Students may replace a required course with a qualifying replacement course if they have taken the equivalent of the required course elsewhere.
Joint- or Dual-degree Coursework:
Dual M.S. in ISyE and 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.
The analytics track is a coursework-only option (Plan C) requiring 30-32 credits. Students proceed through the program and advance as a cohort. The program requires 24 credits in core courses and a minimum of 6 credits in elective courses. In addition, non-native English speakers are required to take the 2-credit course ESL 5008.
Required Courses (24 credits)
IE 5531 - Engineering Optimization I (4.0 cr)
IE 5532 - Stochastic Models (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)
CSCI 5521 - Introduction to Machine Learning (3.0 cr)
or CSCI 5523 - Introduction to Data Mining (3.0 cr)
Electives (6 credits)
In consultation with adviser, select a minimum of 6 credits from the following. Additional courses may be approved by the Director of Graduate Studies.
CSCI 5521 - Introduction to Machine Learning (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 Planning and Inventory Control (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)
English Proficiency (0-2 credits)
Non-native English speakers are required to take the following:
ESL 5008 - Speaking for Professional Settings (2.0 cr)
Industrial Engineering
The industrial engineering track has three options: -Plan A (thesis) requires 30 credits: 14 in the major, 6 outside IE, and 10 thesis credits. -Plan B (project) requires 30 credits: 17 in the major, which may include 4 credits for the Plan B project course, 6 outside IE, and 7 additional elective credits. -Plan C (coursework) requires 32 credits: 17 in the major, 6 outside IE, and 9 additional elective credits.
Required Courses (13-17 credits)
All students take IE 5531 and 5532 for 8 credits.
IE 5531 - Engineering Optimization I (4.0 cr)
IE 5532 - Stochastic Models (4.0 cr)
In addition, Plan A students choose a minimum of 1 course from the following, and Plan B and C students, choose a minimum of 2 courses.
IE 5511 - Human Factors and Work Analysis (4.0 cr)
IE 5545 - Decision Analysis (4.0 cr)
IE 5551 - Production Planning and Inventory Control (4.0 cr)
Seminar
Complete 1 seminar credit. The following may be used or consult with adviser for additional options.
IE 8773 - Graduate Seminar (1.0 cr)
IE 8774 - Graduate Seminar (1.0 cr)
Electives (7-15 credits)
In consultation with adviser, select courses from the following to complete the minimum course credit requirements. A minimum of 6 credits must be from non-IE courses. Additional courses may be approved by the Director of Graduate Studies.
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 - Introduction to Machine Learning (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5525 - Machine Learning (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)
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 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 5541 - Project Management (4.0 cr)
IE 5545 - Decision Analysis (4.0 cr)
IE 5551 - Production Planning and Inventory Control (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 (4.0 cr)
IE 8552 - Advanced Topics in Production, Inventory, and Distribution Systems (4.0 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 6030 - Financial Accounting (3.0 cr)
MBA 6220 - Supply Chain & Operations (3.0 cr)
MGMT 6004 - Negotiation Strategies (2.0 cr)
MILI 6990 - 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 (2.0 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)
Plan Options
Plan A (10 credits)
Complete 10 thesis credits.
IE 8777 - Thesis Credits: Master's (1.0-18.0 cr)
-OR-
Plan B (0-4 credits)
Plan B students must either take the Plan B course IE 8794 or complete one to three Plan B papers, determined in consultation with the adviser.
IE 8794 - Industrial Engineering Research (1.0-6.0 cr)
-OR-
Plan C
Plan C students do not have additional requirements.
Systems Engineering
This sub-plan is limited to students completing the program under Plan C.
The systems engineering track is a coursework-only option (Plan C) requiring 30 credits.
Required Courses (14 credits)
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)
Electives (16 credits)
In consultation with adviser, select courses from the following to complete course credit requirements. A minimum of 6 credits must be from non-IE courses. Additional courses may be approved by the Director of Graduate Studies.
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 - Introduction to Machine Learning (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5525 - Machine Learning (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)
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 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 5541 - Project Management (4.0 cr)
IE 5545 - Decision Analysis (4.0 cr)
IE 5551 - Production Planning and Inventory Control (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 (4.0 cr)
IE 8552 - Advanced Topics in Production, Inventory, and Distribution Systems (4.0 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 6030 - Financial Accounting (3.0 cr)
MBA 6220 - Supply Chain & Operations (3.0 cr)
MGMT 6004 - Negotiation Strategies (2.0 cr)
MILI 6990 - 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 (2.0 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)
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 (B.I.Sy.E.) and a master's degree (M.S.I.Sy.E.) 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 simultaneously in the last two years of study. The integrated program is available only for the Analytics Track. Both 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 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 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.
CSCI 5521 - Introduction to Machine Learning
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] or instr consent
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 - Introduction to Machine Learning
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] or instr consent
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: 01914 - 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: 01916
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 Planning and Inventory Control
Credits: 4.0 [max 4.0]
Course Equivalencies: 01917 - IE 4551/IE 5551
Typically offered: Every Fall & 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: 01915 - 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
ESL 5008 - Speaking for Professional Settings
Credits: 2.0 [max 2.0]
Typically offered: Every Fall & Spring
This course is designed for graduate students who are non-native speakers of English seeking to improve their English speaking skills for professional contexts. The course assumes that students already have a high level of proficiency in English; this course will help students refine their skills for specific professional situations. The course covers topics such as small talk, networking, interviewing, and presentation skills. Students will increase their confidence to communicate in a variety of settings including informal exchanges, career fairs, conference presentations, and job interviews. prereq: Graduate student
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: 01553
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 Planning and Inventory Control
Credits: 4.0 [max 4.0]
Course Equivalencies: 01917 - IE 4551/IE 5551
Typically offered: Every Fall & 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: 01840 - ApEc 8001/Econ 8001/Econ 8101
Grading Basis: A-F or Aud
Typically offered: Every Fall
Consumer behavior/demand. Introduction to welfare analysis. General equilibrium analysis in pure exchange economy. Part of four-course sequence (APEC 8001-8004). 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: 01841 - ApEc 8002/Econ 8002/Econ 8102
Grading Basis: A-F or Aud
Typically offered: Every Fall
Production, competitive markets, and choice under uncertainty. Technology and production, cost minimization and profit maximization, production duality, efficiency and technical change, general equilibrium of production. Part of four-course sequence (APEC 8001-8004). 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
Classical multiple linear regression, stochastic regressors, heteroscedasticity, autocorrelated disturbances, panel data, discrete dependent variables. 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 - Introduction to Machine Learning
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] or instr consent
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
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
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 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: 01914 - 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 5541 - Project Management
Credits: 4.0 [max 4.0]
Course Equivalencies: 01916
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 Planning and Inventory Control
Credits: 4.0 [max 4.0]
Course Equivalencies: 01917 - IE 4551/IE 5551
Typically offered: Every Fall & 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: 01915 - 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: Every 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: 4.0 [max 8.0]
Typically offered: Every Fall & Spring
Special topics determined by instructor. Examples include Markov decision processes, stochastic programming, integer/combinatorial optimization, and queueing networks. prereq: 5531, 8532
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
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 6030 - 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 Student
MBA 6220 - Supply Chain & Operations
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Introduction to fundamental operations management principles and concepts. The course takes a strategic view of operations in both a manufacturing and service context and stresses linkages to other functional areas. Many of the cases in the course take an international perspective. 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 student
MILI 6990 - The Health Care Marketplace
Credits: 2.0 [max 2.0]
Course Equivalencies: 02186 - MILI 5990/MILI 6990
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 only
Typically offered: Every Fall
Basics of operations, strategy, decision-making in technology-driven business. Market opportunity assessment, finance/financial decision-making, organizational roles. Work in teams to analyze aspects of business opportunity. prereq: Degree seeking or non-degree graduate students
MOT 5002 - Creating Technological Innovation
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Course provides students with techniques to create new ideas, and lead an organization to bring them successfully to market. It will include examples of the dynamics of technological industries, and technology strategies. Topics include effective practices to generate ideas, processes to move them to market, and intellectual property. Students will work in teams to develop a strategy to commercialize a new technology. 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
In the course of their careers, contemporary managers spend a significant amount of time either participating in or leading projects. Projects are frequently used as proving-grounds for high-potentials. The skills that are required in project management are often the very same attributes that are required for successfully managing a business. While every project is by definition unique, some concepts and tools (e.g., critical path method, time and cost tradeoffs, resource utilization, methods to deal with uncertainties) in project management apply to a wide range of different types of projects. The aim of this course is to equip students with these concepts and tools (e.g., Monte Carlo simulation, risk analysis) and to develop them into successful project managers, as well as team members.
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 will be organized is: How can technologies and the related process and people issues be managed to design and sustain reliable, responsive, resilient, and responsible supply chains? Contemporary topics such as big data analytic applications to supply chain management; technology project management as it relates to offshoring and near-shoring; managing technologies in the context of supply chains in emerging economies; and managing technologies for sustainable supply chains will be covered in the course. Implications of globalization for managing technologies in supply chains will be a theme that will run through the entire duration of the course.
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 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: 01916
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: 01915 - 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: 01840 - ApEc 8001/Econ 8001/Econ 8101
Grading Basis: A-F or Aud
Typically offered: Every Fall
Consumer behavior/demand. Introduction to welfare analysis. General equilibrium analysis in pure exchange economy. Part of four-course sequence (APEC 8001-8004). 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: 01841 - ApEc 8002/Econ 8002/Econ 8102
Grading Basis: A-F or Aud
Typically offered: Every Fall
Production, competitive markets, and choice under uncertainty. Technology and production, cost minimization and profit maximization, production duality, efficiency and technical change, general equilibrium of production. Part of four-course sequence (APEC 8001-8004). 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
Classical multiple linear regression, stochastic regressors, heteroscedasticity, autocorrelated disturbances, panel data, discrete dependent variables. 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 - Introduction to Machine Learning
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] or instr consent
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
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
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 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: 01914 - 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 5541 - Project Management
Credits: 4.0 [max 4.0]
Course Equivalencies: 01916
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 Planning and Inventory Control
Credits: 4.0 [max 4.0]
Course Equivalencies: 01917 - IE 4551/IE 5551
Typically offered: Every Fall & 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: 01915 - 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: Every 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: 4.0 [max 8.0]
Typically offered: Every Fall & Spring
Special topics determined by instructor. Examples include Markov decision processes, stochastic programming, integer/combinatorial optimization, and queueing networks. prereq: 5531, 8532
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
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 6030 - 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 Student
MBA 6220 - Supply Chain & Operations
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Introduction to fundamental operations management principles and concepts. The course takes a strategic view of operations in both a manufacturing and service context and stresses linkages to other functional areas. Many of the cases in the course take an international perspective. 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 student
MILI 6990 - The Health Care Marketplace
Credits: 2.0 [max 2.0]
Course Equivalencies: 02186 - MILI 5990/MILI 6990
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 only
Typically offered: Every Fall
Basics of operations, strategy, decision-making in technology-driven business. Market opportunity assessment, finance/financial decision-making, organizational roles. Work in teams to analyze aspects of business opportunity. prereq: Degree seeking or non-degree graduate students
MOT 5002 - Creating Technological Innovation
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Course provides students with techniques to create new ideas, and lead an organization to bring them successfully to market. It will include examples of the dynamics of technological industries, and technology strategies. Topics include effective practices to generate ideas, processes to move them to market, and intellectual property. Students will work in teams to develop a strategy to commercialize a new technology. 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
In the course of their careers, contemporary managers spend a significant amount of time either participating in or leading projects. Projects are frequently used as proving-grounds for high-potentials. The skills that are required in project management are often the very same attributes that are required for successfully managing a business. While every project is by definition unique, some concepts and tools (e.g., critical path method, time and cost tradeoffs, resource utilization, methods to deal with uncertainties) in project management apply to a wide range of different types of projects. The aim of this course is to equip students with these concepts and tools (e.g., Monte Carlo simulation, risk analysis) and to develop them into successful project managers, as well as team members.
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 will be organized is: How can technologies and the related process and people issues be managed to design and sustain reliable, responsive, resilient, and responsible supply chains? Contemporary topics such as big data analytic applications to supply chain management; technology project management as it relates to offshoring and near-shoring; managing technologies in the context of supply chains in emerging economies; and managing technologies for sustainable supply chains will be covered in the course. Implications of globalization for managing technologies in supply chains will be a theme that will run through the entire duration of the course.
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.