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, 1120 Mechanical Engineering, 111 Church Street S.E., Minneapolis, MN 55455 (612-625-2009; fax 612-624-2010)
  • Program Type: Master's
  • Requirements for this program are current for Fall 2018
  • 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 the Industrial Engineering track, the Systems Engineering track, and the Analytics track and a PhD degree. 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:
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 three letters of recommendation and a personal statement. 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.
Special Application Requirements:
All application materials should be submitted electronically through the ApplyYourself application system. 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. Additional information is available at www.isye.umn.edu/apply/
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
    • Paper Based - Total Score: 550
  • 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 16 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.
The Master of Science in Industrial and Systems Engineering (M.S.I.Sy.E.) is offered with three tracks. The industrial engineering track has three options. Plan A (thesis) and Plan B (project) require 30 credits and Plan C (coursework) requires 32 credits. Plan A requires a minimum of 14 course credits in the major field, and Plan B or Plan C requires 16 course credits in the major field. All plans must include a minimum of 6 course credits in a minor or related field outside ISyE and 1 credit of graduate seminar. The remaining credits may be taken in the major field or any supporting field. The systems engineering track is a coursework-only option (Plan C) requiring 30 credits. It requires a minimum of 14 course credits in the major field and 6 course credits in a minor or related field outside ISyE. The remaining 10 credits may be taken in the major or in any supporting field. 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. Students may replace a required course with a qualifying replacement course if they have taken the equivalent of the required course elsewhere. A list of qualifying replacements is available on the ISyE program web page.
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.
Industrial Engineering
Plan A
Required Courses
IE 5531 - Engineering Optimization I (4.0 cr)
IE 5532 - Stochastic Models (4.0 cr)
ME 8001 - Research Ethics and Professional Practice (0.0 cr)
Take 1 or more course(s) from the following:
· 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
Take 1 seminar credit. The following may be used or consult with advisor for further options.
IE 8773 - Graduate Seminar (1.0 cr)
or IE 8774 - Graduate Seminar (1.0 cr)
Thesis Credits
Take 10 credits
IE 8777 - Thesis Credits: Master's (1.0-18.0 cr)
Plan B or Plan C
Required Courses
IE 5531 - Engineering Optimization I (4.0 cr)
IE 5532 - Stochastic Models (4.0 cr)
ME 8001 - Research Ethics and Professional Practice (0.0 cr)
Take 2 or more course(s) from the following:
· 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
Take 1 seminar credit. The following may be used or consult with advisor for further options.
IE 8773 - Graduate Seminar (1.0 cr)
or IE 8774 - Graduate Seminar (1.0 cr)
Project Requirement
Plan B students must either take the Plan B courses IE 8951/8953 (3 credits), or complete one to three Plan B papers, determined in consultation with the advisor.
IE 8951 - Plan B Course (1.0 cr)
IE 8953 - Plan B (2.0 cr)
Systems Engineering
This sub-plan is limited to students completing the program under Plan C.
Required 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)
ME 8001 - Research Ethics and Professional Practice (0.0 cr)
Analytics
This sub-plan is limited to students completing the program under Plan C.
Required Courses
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
Additional courses may be approved by the Director of Graduate Studies.
Take 6 or more credit(s) from the following:
· 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)
· CSCI 5521 - Introduction to Machine Learning (3.0 cr)
· CSCI 5523 - Introduction to Data Mining (3.0 cr)
English Proficiency
Non-native English speakers are required to take the following:
ESL 5008 - Speaking for Professional Settings (2.0 cr)
Integrated B.M.E./M.S.I.SY.E.
This sub-plan is optional and does not fulfill the sub-plan requirement for this program.
The Department of Industrial and Systems Engineering and the Department of Mechanical Engineering offer an integrated bachelor's/master's degree program. The program makes it possible for students to earn a bachelor's degree in Mechanical Engineering (B.M.E.) and a master's degree in Industrial & Systems Engineering (M.S.I.SY.E.) in five years. The program has several benefits: a streamlined admissions process from the ME undergraduate program to the ISyE 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 Industrial Engineering Track. Both the BME 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. Eligibility Requirements: - Students must be enrolled in the Mechanical Engineering undergraduate program at the University of Minnesota, Twin Cities. - Students who are within 32 semester credits completing the requirements for the BME degree are eligible to apply. - Students with a GPA of 3.25 or greater are preferred. For students who have transferred from another institution, at least one semester must be completed at the University of Minnesota, Twin Cities before admission to the program will be granted.
 
More program views..
View college catalog(s):
· College of Science and Engineering

View PDF Version:
Search.
Search Programs

Search University Catalogs
Related links.

College of Science and Engineering

Graduate Admissions

Graduate School Fellowships

Graduate Assistantships

Colleges and Schools

One Stop
for tuition, course registration, financial aid, academic calendars, and more
 
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.
ME 8001 - Research Ethics and Professional Practice
Credits: 0.0 [max 0.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
Intellectual property, data management, social responsibility, authorship, and plagiarism, conflict of interest, and reporting misconduct. Case studies. Recent newspaper articles.
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
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 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.
ME 8001 - Research Ethics and Professional Practice
Credits: 0.0 [max 0.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
Intellectual property, data management, social responsibility, authorship, and plagiarism, conflict of interest, and reporting misconduct. Case studies. Recent newspaper articles.
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
IE 8951 - Plan B Course
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall
Structured environment in which students can complete M.S. Plan B project.
IE 8953 - Plan B
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Structured environment in which students can complete M.S. Plan B project. prereq: 8951
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
ME 8001 - Research Ethics and Professional Practice
Credits: 0.0 [max 0.0]
Grading Basis: No Grade
Typically offered: Every Fall, Spring & Summer
Intellectual property, data management, social responsibility, authorship, and plagiarism, conflict of interest, and reporting misconduct. Case studies. Recent newspaper articles.
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: 3022 or 4102 or 5021 or 5102 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
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
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
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