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STSCI—Statistical Science |
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STSCI 2110 - Statistical Methods for the Social Sciences II (crosslisted)
(also ILRST 2110 ) (MQR)
Fall, spring. 3 credits. Letter grades only.
Prerequisite: STSCI 2100 or equivalent introductory statistics course. Co-meets with ILRST 5110 /STSCI 5110 .
T. DiCiccio.
For description, see ILRST 2110 .
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STSCI 2150 - Introductory Statistics for Biology
(MQR)
Fall, spring. 4 credits.
Forbidden Overlap: Students may receive credit for only one course in the following group: AEM 2100 , ENGRD 2700 , ILRST 2100 /STSCI 2100 , MATH 1710 , NTRES 3130 /BTRY 3010 /STSCI 2200 , PAM 2100 , PAM 2101 , PSYCH 3500 , SOC 3010 , STSCI 2150.
Staff.
This course provides an introduction to data analysis and statistical inference illustrated with biological applications. The computer labs will teach graphical analysis and statistical computation using R. Topics include graphical display, populations and sampling, probability distributions, expectation and variance, estimation, testing, correlation, regression, contingency tables, and the design of experiments. Emphasis is on concepts and the careful modeling of biological data, so that statistical methods are applied properly, pitfalls are avoided, and sound conclusions are reached.
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STSCI 2160 - [Occupational Epidemiology] (crosslisted)
(also ILRST 2200 )
Fall. 3 credits. Letter grades only.
Prerequisite: ILRST 2100 /STSCI 2100 , or equivalent. Next offered 2014-2015.
M. E. Karns.
For description, see ILRST 2200 .
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STSCI 2200 - Biological Statistics I (crosslisted)
(also BTRY 3010 , NTRES 3130 ) (MQR)
Fall. 4 credits.
Forbidden Overlap: Students may receive credit for only one course in the following group: AEM 2100 , ENGRD 2700 , ILRST 2100 , MATH 1710 , NTRES 3130 /BTRY 3010 /STSCI 2200, PAM 2100 , PAM 2101 , PSYCH 3500 , SOC 3010 , STSCI 2100 , STSCI 2150 .
Prerequisite: one semester of calculus.
P. Sullivan.
For description and learning outcomes, see NTRES 3130 .
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STSCI 3030 - [Policy Analysis by the Numbers] (crosslisted)
(also ILRST 3030 )
Fall. 4 credits. Letter grades only.
Next offered 2014-2015.
M. E. Karns.
For description, see ILRST 3030 .
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STSCI 3080 - Probability Models and Inference (crosslisted)
(also BTRY 3080 , ILRST 3080 ) (MQR)
Fall, spring. 4 credits.
Forbidden Overlap: Students may receive credit for only one course in the following group: BTRY 3080 /ILRST 3080 /STSCI 3080, ECON 3110 /ILRST 3110 /STSCI 3110 , ECON 3125 (formerly 3210) , ECON 3130 (formerly 3190) , MATH 4710 .
F. Bunea.
This course provides an introduction to probability and parametric inference. Topics include: random variables, standard distributions, the law of large numbers, the central limit theorem, likelihood-based estimation, sampling distributions and hypothesis testing, as well as an introduction to Bayesian methods. Some assignments may involve computation using the R programming language.
Outcome 1: Students will be able to manipulate random variables and their distributions using differential and integral calculus.
Outcome 2: Students will be able to derive properties of standard probability.
Outcome 3: Students will be able to derive maximum likelihood estimators for standard probability distributions and discuss their properties.
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STSCI 3100 - Statistical Sampling (crosslisted)
(also BTRY 3100 , ILRST 3100 )
Fall. 4 credits.
Prerequisite: two semesters of statistics.
J. Bunge.
Theory and application of statistical sampling, especially in regard to sample design, cost, estimation of population quantities, and error estimation. Assessment of nonsampling errors. Discussion of applications to social and biological sciences and to business problems. Includes an applied project.
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STSCI 3510 - Introduction to Engineering Stochastic Processes I (crosslisted)
(also ORIE 3510 )
Spring, Summer. 4 credits.
Prerequisite: grade of C- or better in ORIE 3500 or equivalent.
Staff.
For description, see ORIE 3510 .
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STSCI 3520 - Statistical Computing (crosslisted)
(also BTRY 3520 )
Spring. 4 credits.
Prerequisites: BTRY 3080 , enrollment in MATH 2220 and MATH 2240 or equivalents.
G. Hooker.
For description and learning outcomes, see BTRY 3520 .
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STSCI 4030 - Linear Models with Matrices (crosslisted)
(also BTRY 4030 )
Fall. 4 credits.
A two-semester sequence on statistical methods (e.g. BTRY 3010 -BTRY 3020 ), a course on probability and distribution theory (e.g. BTRY 3080 ), multivariable calculus, and linear/matrix algebra.
J. Booth.
The focus of this course is the theory and application of the general linear model expressed in its matrix form. Topics will include: least squares estimation, multiple linear regression, coding for categorical predictors, residual diagnostics, anova decomposition, polynomial regression, model selection techniques, random effects and mixed models, maximum likelihood estimation and distributional theory assuming normal errors. Homework assignments will involve computation using the R statistical package.
Outcome 1: Students will be able to discuss the mathematical foundations of linear statistical models using matrix algebra.
Outcome 2: Students will be able to use diagnostic measures to assess the validity of a given statistical model.
Outcome 3: Students will be able to analyze data involving both fixed and random factors.
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STSCI 4060 - Introduction to High Performance Computing Tools and Concepts
Spring. 2 credits.
Prerequisite: Basic programming skills (any language). Permission of department required.
X. Yang.
The first part of the course teaches basic Python programming knowledge and skills. The second part deals with Python application in statistics (e.g., data visualization and statistical analysis), Python-database integration (e.g., access, update and control an Oracle database), and Python web services (e.g., database-driven dynamic webpages using Python CGI scripts). These techniques are utilized in a comprehensive course project.
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STSCI 4090 - Theory of Statistics (crosslisted)
(also BTRY 4090 )
Spring. 4 credits. Letter grades only.
Forbidden Overlap: Students may receive credit for only one course in the following group: STSCI 4090, ECON 3125 (formerly 3210) , MATH 4720 .
Prerequisites: BTRY 3080 or equivalent and at least one introductory statistics course.
Staff.
For description, see BTRY 4090 .
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STSCI 4100 - Multivariate Analysis (crosslisted)
(also BTRY 4100 , ILRST 4100 )
Spring. 4 credits.
Prerequisite: ILRST 3120 , STSCI 2200 , or equivalent; some knowledge of matrix-based regression analysis.
Staff.
Theory and application of classical and modern multivariate methods to data arising in biology, sociology, economics, engineering and other fields. Topics include MANOVA, principal components, factor analysis, structural equations, discriminant analysis and clustering.
Outcome 1: Students will be able to explain the utility of multivariate methods for MANOVA, PCA, factor analysis, discriminant analysis and clustering.
Outcome 2: Students will be able to analyze multivariate data using modern statistical software.
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STSCI 4110 - Categorical Data (crosslisted)
(also BTRY 6030 , ILRST 4110 )
Spring. 4 credits. Letter grades only.
Prerequisite: BTRY 3020 , BTRY 6020 , or equivalent with BTRY 3080 also highly recommended. Offered alternate years.
Staff.
Categorical data analysis, including logistic regression, log-linear models, stratified tables, matched pairs analysis, polytomous response, and ordinal data. Applications in biological, biomedical and social sciences.
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STSCI 4140 - Applied Design (crosslisted)
(also BTRY 4140 , ILRST 4140 )
Spring. 4 credits.
Prerequisites: STSCI 3200 or permission of instructor.
Staff.
Applications of experimental design including split plots, incomplete blocks, and fractional factorials. Stresses use of the computer for both design and analysis, with emphasis on solving real data problems.
Outcome 1: Students will be able to explain the basic design principles such as randomization, blocking and stratification.
Outcome 2: Students will be able to determine an appropriate design based on design principles.
Outcome 3: Students will be able to apply standard designs to date using modern statistical software and interpret the results.
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STSCI 4270 - Introduction to Survival Analysis (crosslisted)
(also BTRY 4270 )
Fall, spring. 3 credits.
Prerequisite: BTRY 3080 or equivalent preparation; 3 semesters of calculus.
Staff.
Develops and uses statistical methods appropriate for analyzing right-censored (i.e., incomplete) time-to-event data. Topics covered include nonparametric estimation (e.g., life table methods, Kaplan Meier estimator), nonparametric methods for comparing the survival experience of two or more populations, and semiparametric and parametric methods of regression for censored outcome data. Substantial use is made of the R statistical software package.
Outcome 1: Students will be able to conduct appropriate nonparametric and parametric analyses of right-censored survival data using the R software language, including tabular and graphical methods (i.e., life tables and Kaplan Meier plots), hypothesis testing (e.g., logrank tests and Wald tests) and likelihood-based methods of regression (i.e., proportional hazards and accelerated failure time regression models).
Outcome 2: Students will be able to interpret the results of a statistical analysis involving right censored survival data as well as articulate the associated limitations of such analyses.
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STSCI 4500 - Databases and Statistical Computing
Spring. 4 credits.
Exposure to multiple linear regression and logistic regression strongly recommended.
Staff.
The intent of the course is to provide the statistician with the computational tools for statistical research and applications. Topics including random number generation and Monte Carlo methods, regression computations and application to statistical methods of optimization, and sorting.
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STSCI 4550 - Applied Time Series Analysis (crosslisted)
(also ILRST 4550 , ORIE 5550 )
Spring. 4 credits.
Prerequisite: STSCI 3080 , STSCI 4030 (or equivalent) or permission of instructor.
D. Matteson.
Introduces statistical tools for the analysis of time-dependent data. Data analysis and application will be an integral part of this course. Topics include linear, nonlinear, seasonal, multivariate modeling, and financial time series.
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STSCI 4740 - Data Mining and Machine Learning
Fall. 4 credits.
Prerequisites: CS 1112 or equivalent, MATH 2220 , STSCI 3200 , STSCI 3080 .
Staff.
We start off with a detailed refresher for Linear Regression. We then turn to popular methods for classification including Logistic Regression and Discriminant Analysis. Finally, we consider more advanced topics which may include - depending on the audience - Resampling Methods, Tree-based Methods, or Support Vector Machines. The statistics software R is introduced and used for applications.
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STSCI 4940 - Undergraduate Special Topics in Statistics
Fall, spring. 1-3 credits, variable.
Permission of Department required.
Staff.
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STSCI 4980 - Tutorial in Actuarial Statistics
Fall, spring. 2 credits. S-U grades only.
M. Wells.
Problem solving sessions to prepare students for the first four actuarial examinations (probability, financial mathematics, statistical modeling, and risk management).
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STSCI 4990 - Undergraduate Individual Study in Statistics
Fall, spring. 1-3 credits, variable.
Permission of department required. Students must register using independent study form.
Staff.
Course consists of individual tutorial study selected by faculty. Because topics usually change year to year this course may be repeated for credit.
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STSCI 4999 - Undergraduate Dissertation Research
Fall, spring, summer. 1-3 credits, variable. S-U grades only.
Permission of instructor required.
Staff.
Research at the undergraduate level.
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STSCI 5010 - Applied Statistical Analysis 1
Fall. 4 credits. Letter grades only.
Enrollment is limited to: students in M.P.S. Program. Core course for students in master of professional studies (M.P.S.) degree program in applied statistics.
Staff.
This course teaches the basics of SAS (Statistical Analysis System) programming and the SAS Enterprise Miner software. This course is composed two modules. The first module, in the first 12 weeks, covers the objectives tested on the SAS Base Programming for SAS 9 Exam, including basic SAS programming concepts, producing reports, creating and modifying SAS data sets, reading various types of raw data and other data handling techniques. At the end of module 1, all the students will take the SAS Base Programming for SAS 9 Exam, which is administered by the MPS Program in Applied Statistics on the Cornell campus in conjunction with the SAS Institute, Inc. The second module, in the last three weeks, introduces the SAS Enterprise Miner software and cluster analysis. Students will learn how to use the SAS Enterprise Miner software and SAS procedures to do cluster analysis.
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STSCI 5060 - Database Management and SAS High Performance Computing with DBMS
Spring. 4 credits. Letter grades only.
Prerequisite: Base SAS programming knowledge and skills (STSCI 5010 ). Permission of instructor required. Enrollment limited to: students in the MPS Program in Applied Statistics.
X. Yang.
Using relational databases in statistical computing has become more and more important. The knowledge and skill of database management and the ability to combine this knowledge and skill with statistical analysis software tools, such as SAS, are a critical qualification of a statistical analyst. In this course we will study 1) the basics of modern relational database management systems, including database analysis, design and implementation, 2) database application in advanced SAS programming and, 3) SAS high performance computing using database-related techniques.
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STSCI 5080 - Probability Models and Inference (crosslisted)
(also BTRY 5080 , ILRST 5080 )
Fall, spring. 4 credits.
F. Bunea.
This course provides an introduction to probability and parametric inference. Topics include: random variables, standard distributions, the law of large numbers, the central limit theorem, likelihood-based estimation, sampling distributions and hypothesis testing, as well as an introduction to Bayesian methods. Some assignments may involve computation using the R programming language.
Outcome 1: Students will be able to manipulate random variables and their distributions using differential and integral calculus.
Outcome 2: Students will be able to derive properties of standard probability.
Outcome 3: Students will be able to derive maximum likelihood estimators for standard probability distributions and discuss their properties.
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STSCI 5110 - Statistical Methods for the Social Sciences II (crosslisted)
(also ILRST 5110 )
Fall, Spring. 3 Credits. Letter grades only.
Prerequisite: STSCI 2100 or equivalent introductory statistics course. Co-meets with ILRST 2110 /STSCI 2110 .
T. DiCiccio.
For description, see ILRST 5110 .
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STSCI 5951 - Statistical Consulting for MPS 1
Fall. 2 credits. Letter grades only.
Prerequisite: Multivariate calculus, linear algebra, and basic statistics. Permission of department required. Open only to students in MPS in Applied Statistics.
P. Velleman.
This course is for MPS students, who will be behaving as statistical consultants for clients and then presenting their analyses at the end of the semester. The course will discuss consulting in general. The topics will include: the statistical consulting process, how to interview a client, what to learn about the client’s problem and data, helping the client refine their questions and design a study, making initial displays of the data, dealing with data problems, etc. Writing skills will be emphasized, and students will write frequent short essays.
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STSCI 5952 - Statistical Consulting for MPS 2
Spring. 2 credits. Letter grades only.
Prerequisite: Multivariate calculus, linear algebra, basic statistics, and Statistical Consulting 1. Permission of department required. Open only to students in MPS in Applied Statistics.
P. Velleman.
This course will help students perform their data analyses on the data that they obtained in Statistical Consulting for MPS 1 (STSCI 5951), write them up, and develop and practice their presentations. Presentation skills will be emphasized.
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STSCI 5990 - Directed Studies in Applied Statistics
Fall, spring, summer. 1-4 credits, variable.
Prerequisite: Multicalculus, linear algebra, and basic statistics. Permission of department required.
Staff.
For individual or group research projects conducted under the direction of a member of STSCI faculty or instructors in a special area of statistical science that is not covered by regular course offerings.
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STSCI 6000 - Statistics Seminar
Fall, spring. 1 credit. S-U grades only.
Prerequisite or Corequisite: BTRY 4090 or permission of instructor.
Staff.
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STSCI 6520 - Computationally Intensive Statistical Methods (crosslisted)
(also BTRY 6520 )
Spring. 4 credits.
Prerequisites: ORIE 6700 (or equivalent) and at least one course in probability, or permission of instructor.
Staff.
Modem applications in statistics often require intensive computation and the use of modem statistical learning techniques. This course covers topics in statistical computing, induding numerical optimization and finding zeros (likelihood and related techniques), regressions, logistic regressions, neural neworks, decision trees, boosting, bagging, dimension reductions (including classical methods and new techniques) for handling modem massive data sets (MMDS). Intensive programming is done in MATLAB.
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STSCI 6940 - Graduate Special Topics in Statistics
Fall, spring. 1-3 credits, variable.
Permission of department required.
Staff.
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STSCI 7170 - Theory of Linear Models
Fall. 3 credits.
Prerequisites: BTRY 4090 , BTRY 6020 , or equivalents.
D. Matteson.
Properties of the multivariate normal distribution. Distribution theory for quadratic forms. Properties of least squares and maximum likelihood estimates. Methods for fixed-effect models of less than full rank. Analysis of balanced and unbalanced mixed-effects models. Restricted maximum likelihood estimation. Some use of software packages and illustrative examples.
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STSCI 7999 - Graduate Level Dissertation Research
Fall, spring, summer. 1-9 credits, variable. S-U grades only.
Ph.D. Candidate permission of Graduate Field Member concerned.
Staff.
Research at the Ph.D. Level.
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STSCI 9999 - Doctoral Level Dissertation Research
Fall, spring, summer. 1-9 credits, variable.
Permission of instructor required.
Staff.
Doctoral Level Dissertation Research.
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SYSEN—Systems Engineering |
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SYSEN 1100 - Getting Design Right: A Systems Approach
Summer. (Six-week session) 3 credits.
Prerequisite: high school mathematics and science and familiarity with spreadsheet modeling (e.g., Microsoft Excel). Next offered 2014-2015. Web-delivered.
P. L. Jackson.
Freshman-level exposure to the product design process. The process of getting design right is sometimes called systems engineering. We explain the process using the acronym DMEODVEI (Define, Measure, Explore, Optimize, Develop, Validate, Execute, and Iterate). The process begins with understanding customer requirements and ends with executing the design to satisfy those requirements. It can then be iterated to greater levels of design detail. The focus is not on detailed engineering design but rather on the process of ensuring that the detailed design will meet the needs of the customer. Students work through the steps of the process with reference to a particular product design challenge. The course is web-delivered using the Blackboard learning instruction system.
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SYSEN 5100 - Model Based Systems Engineering (crosslisted)
(also CEE 5240 , ECE 5120 , MAE 5910 , ORIE 5140 )
Fall. 4 credits.
Prerequisite or corequisite: enrollment in group-based project with strong system design component approved by course instructor. Enrollment is limited to: seniors or graduate students in an engineering field. Core course for Systems Engineering majors. Co-meets with SYSEN 5110 .
D. Schneider.
Fundamental ideas of systems engineering, and their application to design and development of various types of engineered systems. Defining system requirements, creating effective project teams, mathematical tools for system analysis and control, testing and evaluation, economic considerations, and the system life cycle. Content utilizes model-based systems engineering, which is the integration of systems modeling tools, such as SysML, with tools for systems analysis, such as Matlab and Modelica. The vision for this integration is the ability to create and analyze complete parametric representations of complex products and systems. These systems make it possible to investigate the impact of changing one aspect of a design on all other aspects of design and performance. This course will familiarize students with these modeling languages. Off-campus students must provide their own Windows 7, internet-connected, computer with administrator access in order to install the commercial software used in this course.
Students majoring in Systems Engineering should enroll in SYSEN 5100. Students taking the minor in Systems Engineering should enroll in CEE 5240 , ECE 5120 , MAE 5910 , or ORIE 5140 . Students in distance learning programs should enroll in SYSEN 5110 . Course is identical for all versions.
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SYSEN 5110 - Model Based Systems Engineering
Fall. 4 credits.
Prerequisite or corequisite: (past two years) enrollment in group-based project with strong system design component approved by course instructor. Enrollment limited to: senior or graduate standing in engineering field. Intended for off-campus students. Co-meets with SYSEN 5100 , ECE 5120 , ORIE 5140 , MAE 5910 , CEE 5240 .
D. Schneider.
For description, see SYSEN 5100 .
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SYSEN 5200 - Systems Architecture, Behavior, and Optimization (crosslisted)
(also CEE 5252 , ECE 5130 , MAE 5920 , ORIE 5142 )
Spring. 3 credits.
Prerequisite: Applied System Engineering MAE 5910 , CEE 5240 , ECE 5120 , ORIE 5140 , SYSEN 5100 or SYSEN 5110 , or permission of instructor. Core course for Systems Engineering majors. Students majoring in Systems Engineering enroll in SYSEN 5200. Students taking the minor in Systems Engineering enroll in MAE 5920 , CEE 5252 , ECE 5130 , or ORIE 5142 . Students in distance learning programs enroll in SYSEN 5210 . Course is identical for all versions.
H. Topaloglu.
This is an advanced course in the application of the systems engineering process to the architecture design and operation of complex systems. Topics include techniques for design, simulation, optimization, and control of complex systems. Case studies and system simulations in diverse areas provide context for the application of these techniques.
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SYSEN 5210 - Systems Architecture, Behavior, and Optimization
Spring. 3 credits.
Prerequisite: Applied Systems Engineering or permission of instructor. Intended for off-campus students.
H. Topaloglu.
For description, see SYSEN 5200 .
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SYSEN 5220 - Systems Dynamics
Fall. 3 credits.
H. Deng.
This course focuses on the design and development of computational models to understand and predict the dynamic behavior of systems. In particular considerable emphasis will be placed on the development of systems thinking skills in the analysis of systems, the translation of those skills into the creation of computational tools to support modeling of these systems and the testing of those models. Students will build realistic models in commercial software packages including Vensin.
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SYSEN 5240 - Search and Optimization with Metaheuristics
Summer. 3 credits.
Prerequisites: Senior/graduate standing. Student should have working knowledge of a high-level programming language (e.g., Matlab, Python, C, Java). Student should have completed a course in optimization or systems analysis that emphasizes formulation and modeling (e.g., ORIE 3310 , SYSEN 5100 , SYSEN 5110 ) and a course in introductory probability and statistics (e.g. ENGRD 2700 ).
K. Y. Daisy Fan.
This course analyzes modern heuristic methods for search and optimization problems. Single-solution based, population based, as well as hybrid techniques are examined. A survey of classical exact optimization methods is given to put metaheuristics in context, and the problem characteristics that favor a heuristic approach are discussed. This course uses implementation case studies to emphasize the solution process representation, computation, parameter estimation, and performance analysis. Students will complete a demonstration project by implementing a metaheuristic solution on a problem of their choice.
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SYSEN 5300 - Systems Engineering and Six Sigma for the Design and Operation of Reliable Systems (crosslisted)
(also MAE 5930 )
Fall. 3-4 credits, variable.
Prerequisite: SYSEN 5100 and either ENGRD 2700 or CEE 3040 or permission of instructor. Satisfies Modeling and Analysis elective requirement. To take course, enroll for three credits. Extra project work is required to earn the Six Sigma Black Belt certification. Enroll for four credits to undertake the extra project work and earn Black Belt certification.
H. O. Gao.
Develops skills in the design, operation and control of systems for reliable performance. Focuses on four key themes; risk analysis (with a particular emphasis on risk assessment and risk characterization), modeling system reliability (including the development of statistical models based on accelerated life testing), quality control techniques and the optimization of system design for reliability. Six Sigma Green or Blackbelt can be earned through activities associated with course. Students in distance-learning programs enroll in SYSEN 5100 . Lectures are identical for all versions.
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SYSEN 5310 - Systems Engineering and Six Sigma for the Design and Operation of Reliable Systems
Fall. 3-4 credits, variable.
Prerequisite: SYSEN 5100 and either ENGRD 2700 or CEE 3040 or permission of instructor. Intended for off-campus students.
H. O. Gao.
For description, see SYSEN 5300 .
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SYSEN 5700 - Special Topics in Systems Engineering
1-6 credits, variable.
Offered on demand.
Staff.
Supervised study by individuals or small groups of one or more specialized topics not covered in regular courses.
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SYSEN 5710 - Practicum in Systems Engineering
1-4 credits, variable.
Offered on demand.
P. Jackson.
Supervised study by individuals or small groups of one or more specialized topics not covered in regular courses.
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SYSEN 5720 - The Art of Innovation: A Hands On Approach
Fall, spring. 2 credits. Letter grades only.
Permission of instructor required.
S. Simoncini, T. Brandenburg.
This hands-on course will prepare you to be future innovators by teaching you the human-centered design methodology known as “design thinking.” You will work in a cross-disciplinary team where you will be immersed in the entire design thinking cycle: empathize, define, ideate, prototype and test. Class challenges will have real sponsors who will give us a complex problem they face in their daily work. You can expect to learn the following: 1) ethnographic techniques for building empathy in order to understand human needs and desires; 2) how to build a design vision based on deep insights gleaned from fieldwork; 3) effective brainstorming techniques; 4) rapid prototyping; 5) how to test prototypes with real users and make multiple iterations based on user feedback.
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SYSEN 5750 - Independent Study in Systems Engineering
1-6 credits, variable.
Offered on demand.
P. Jackson.
Supervised study by individuals or small groups of one or more specialized topics not covered in regular courses.
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SYSEN 5760 - Systems Engineering Project - Track I
Offered on demand. 1-6 credits, variable.
Prerequisite: permission of instructor is required.
P. Jackson.
A design project that incorporates the principles of systems engineering for a complex system. Projects are performed by teams of students working together to meet the requirements of the project.
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SYSEN 5770 - Systems Engineering Project - Track II
Offered on demand. 1-6 credits, variable.
Permission of instructor required.
P. Jackson.
A design project that incorporates the principles of systems engineering for a complex system. Projects are performed by teams of students working together to meet the requirements of the project.
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SYSEN 5900 - Systems Engineering Design Project
Fall, spring. 1-6 credits, variable.
Permission of instructor required. Satisfies project requirement. Must have a total minimum of six credits before graduation.
Staff.
A design project that incorporates the principles of systems engineering for a complex system. Projects are performed by teams of students working together to meet the requirements of the project.
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SYSEN 5920 - Systems Engineering Management for Virtual Teams
Fall. 1 credit.
Enrollment limited to: students matriculated into Systems Engineering Master of Engineering distance learning degree program.
P. L. Jackson, F. J. Wayno.
First of two one-week intensive experiential courses (35 hours) in systems engineering management, with emphasis on laying the social groundwork for students to conduct projects in geographically dispersed teams. Course involves a significant design challenge that must be completed within the week. A leadership laboratory is run simultaneously with the design experience to encourage students to self-assess their leadership style and practices in systems engineering projects.
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SYSEN 5940 - Creativity and Innovation within Systems Engineering
Spring. 1 credit.
Prerequisites: CEE 6910 ; SYSEN 5920 . Enrollment limited to: matriculation in M.Eng. (Systems Engineering) distance learning degree program.
P. L. Jackson, F. J. Wayno.
Second of two one-week intensive courses (35 hours) in systems engineering management with emphasis on understanding individual creativity and organizational innovation and on developing the required systems engineering leadership skills to foster both.
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SYSEN 5960 - Systems Engineering Design Project for Virtual Teams
Fall, spring, summer. 1-6 credits, variable.
Prerequisite: SYSEN 5100 , SYSEN 5920 , SYSEN 5940 , and SYSEN 6910, or permission of instructor. Enrollment is limited to: matriculation in Systems Engineering M.Eng. distance learning degree program. Satisfies project requirement. Must have a total minimum of six credits before graduation. Fulfills M.Eng. Degree requirement for project, subject to credit hour minimum.
Staff.
Systems engineering project for geographically dispersed teams.
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SYSEN 6100 - Systems Seminar Series
Fall, spring. 1 credit.
Enrollment limited to: students in a PhD or Masters of Science Program.
H. Gao, D. Schneider.
This is a weekly seminar course designed to give graduate students experience in improving their skills in presenting their research, judging peers’ and field experts’ research, as well as learning to accurately dissect and summarize the main points of a research talk. Being part of the Systems Engineering program, this course will strongly focus on developing the ability to present research in a broader context or as it relates to a larger system or process, and communicate the importance of the students’ work to a more varied audience; a particularly important skill for both future “job talks” and grants’ “importance paragraphs.” Students’ presentations will also include some research depth as part of developing stronger conference talks and to aid classmates in expanding their knowledge.
Graduate students will be expected to give one presentation on their research as well as attend the majority of their peers’ presentations and the field experts’ presentations that are part of the Erza’s Roundtable seminar series. Students will also be given the opportunity to receive critiques and feedback from not only their colleagues but from faculty, including potential individual or small groups meetings with the instructing faculty.
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TAG—Tagalog |
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TAG 1121 - Elementary Tagalog I
Fall. 4 credits. Letter grades only.
T. Savella.
Gives a thorough grounding in basic speaking and listening skills with an introduction to reading and writing.
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TAG 1122 - Elementary Tagalog II
Spring. 4 credits. Letter grades only.
Prerequisite: TAG 1121 or equivalent.
T. Savella.
Gives a thorough grounding in basic speaking and listening skills with an introduction to reading and writing.
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TAG 2201 - Intermediate Tagalog I
(GB) Satisfies Option 1.
Fall. 3 credits. Letter grades only.
Prerequisite: TAG 1122 or equivalent. Permission of instructor required.
T. Savella.
Develops all four skills: reading, writing, speaking, and comprehension.
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TAG 2202 - Intermediate Tagalog II
(GB) Satisfies Option 1.
Spring. 3 credits. Letter grades only.
Prerequisite: TAG 2201 or equivalent. Permission of instructor required.
T. Savella.
Develops all four skills: reading, writing, speaking, and comprehension.
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TAG 3301 - Advanced Tagalog I
(GB) Satisfies Option 1.
Fall. 3 credits. Letter grades only.
Prerequisite: TAG 2202 or equivalent. Permission of instructor required.
T. Savella.
Continuing instruction on conversational skills but with emphasis on reading and writing. Selected core readings in contemporary Tagalog literature are used, but students, in consultation with the instructor, may select some of the reading materials.
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TAG 3302 - Advanced Tagalog II
(GB) Satisfies Option 1.
Spring. 3 credits. Letter grades only.
Prerequisite: TAG 3301 or equivalent. Permission of instructor required.
T. Savella.
Continuing instruction on conversational skills but with emphasis on reading and writing. Selected core readings in contemporary Tagalog literature are used, but students, in consultation with the instructor, may select some of the reading materials.
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TAG 4431 - Directed Study
Fall. 1-4 credits, variable. Letter grades only.
Permission of instructor required.
T. Savella.
Intended for advanced language study.
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TAG 4432 - Directed Study
Spring. 1-4 credits, variable. Letter grades only.
Permission of instructor required.
T. Savella.
Intended for advanced language study.
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TAMIL—Tamil |
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TAMIL 1121 - Elementary Tamil I
Fall. 4 credits. Letter grades only.
Video-conference with Columbia University.
D. S. Sudanandha.
To develop Tamil language proficiency (i.e. to develop the basic skills of listening, speaking, reading and writing in Tamil language). An interactive video-conference course.
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TAMIL 1122 - Elementary Tamil II
Spring 4 credits. Letter grades only.
Prerequisite: TAMIL 1121 . Taught via video-conference with Columbia University.
D. S. Sudanandha.
To develop Tamil language proficiency (i.e. to develop the basic skills of listening, speaking, reading and writing in Tamil language). An interactive video-conference course.
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TAMIL 2201 - Intermediate Tamil I
(GB) Satisfies Option 1.
Fall. 4 credits. Letter grades only.
Taught via video-conference with Columbia University.
D. S. Sudanandha.
To further enhance the language proficiency (the basic skills of listening, speaking, reading and writing) adding linguistic and cultural nuances to the communication ability. Also to get acquainted with the literary and cultural milieu of Tamil country through the ages.
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TAMIL 2202 - Intermediate Tamil II
(GB) Satisfies Option 1.
Spring. 4 credits. Letter grades only.
Prerequisite: TAMIL 2201 . Offered via video-conference with Columbia University.
D. S. Sudanandha.
To further enhance the language proficiency (the basic skills of listening, speaking, reading and writing) adding linguistic and cultural nuances to the communication ability. Also to get acquainted with the literary and cultural milieu of Tamil country through the ages.
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THAI—Thai |
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THAI 1101 - Elementary Thai I
Fall. 6 credits. Letter grades only.
Intended for beginners or students placed by examination.
N. Jagacinski.
Gives a thorough grounding in all the language skills: listening, speaking, reading, and writing.
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THAI 1102 - Elementary Thai II
Spring. 6 credits. Letter grades only.
Prerequisite: THAI 1101 or equivalent. Intended for beginners or students placed by examination. Permission of instructor required.
N. Jagacinski.
Gives a thorough grounding in all the language skills: listening, speaking, reading, and writing.
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THAI 2201 - Intermediate Thai Reading I
(GB) Satisfies Option 1.
Fall. 3 credits. Letter grades only.
Prerequisite: THAI 1102 . Permission of instructor required.
N. Jagacinski.
Continuing instruction in spoken and written Thai.
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THAI 2202 - Intermediate Thai Reading II
(GB) Satisfies Option 1.
Spring. 3 credits. Letter grades only.
Prerequisite: THAI 2201 or equivalent. Permission of instructor required.
N. Jagacinski.
Continuing instruction in spoken and written Thai.
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THAI 2203 - Intermediate Thai Composition and Conversation I
(GB) Satisfies Option 1.
Fall. 3 credits. Letter grades only.
Prerequisite: THAI 1102 . Permission of instructor required.
N. Jagacinski.
Intermediate instruction in spoken and written grammar and reading comprehension.
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THAI 2204 - Intermediate Thai Composition and Conversation II
(GB) Satisfies Option 1.
Spring. 3 credits. Letter grades only.
Prerequisite: THAI 2203 . Permission of instructor required.
N. Jagacinski.
Intermediate instruction in spoken and written grammar and reading comprehension.
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THAI 3301 - Advanced Thai I
(GB) Satisfies Option 1.
Fall. 4 credits. Letter grades only.
Prerequisite: THAI 2202 , THAI 2204 or equivalent. Permission of instructor required.
N. Jagacinski.
Selected readings in Thai writings in various fields.
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THAI 3302 - Advanced Thai II
(GB) Satisfies Option 1.
Spring. 4 credits. Letter grades only.
Prerequisite: THAI 3301 or equivalent. Permission of instructor required.
N. Jagacinski.
Selected readings in Thai writings in various fields.
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THAI 3303 - Thai Literature I
(GB) (CA-AS) Satisfies Option 1.
Fall. 4 credits. Letter grades only.
Prerequisite: THAI 3302 or equivalent. Permission of instructor required.
N. Jagacinski.
Reading of significant novels, short stories, and poetry written since 1850.
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THAI 3304 - Thai Literature II
(GB) (CA-AS) Satisfies Option 1.
Spring. 4 credits. Letter grades only.
Prerequisite: THAI 3302 or equivalent. Permission of instructor required.
N. Jagacinski.
Reading of significant novels, short stories, and poetry written since 1850.
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THAI 4431 - Directed Study
Fall. 1-4 credits, variable. Letter grades only.
Permission of instructor required.
N. Jagacinski.
Intended for advanced language study.
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THAI 4432 - Directed Study
Spring. 1-4 credits, variable. Letter grades only.
Permission of instructor required.
N. Jagacinski.
Intended for advanced language study.
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TIBET-Tibetan |
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TIBET 1121 - Elementary Classical Tibetan I
Fall. 4 credits. Letter grades only.
Taught through distance learning using video-conferencing technology from Columbia University. Offered on a limited basis with no guarantee of future courses available to fulfill the language requirement.
P. Hackett.
Introduces students to the grammar of Classical Literary Tibetan as found in Indian treatises translated from Sanskrit into Tibetan, as well as indigenous Tibetan philosophical works. The course progresses through a sequence of the basic rudiments of the language, including an introduction to the script and its romanization, pronunciation (central Lhasan dialect), normative dictionary order, and the basic categories of grammar. Following these preliminaries, students proceed to guided readings in Tibetan literature designed to introduce them to the formal approach of Tibetan lexical semantics with an emphasis on the role of verbs in determining argument realization options. Over the duration of the course, students encounter new vocabulary (and associated Buddhist concept hierarchies) and increasingly complex sentence structures. This course thus provides a solid foundation for the later exploration of other genres of literature and styles of composition.
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TIBET 1122 - Elementary Classical Tibetan II
Spring. 4 credits. Letter grades only.
Prerequisite: TIBET 1121 . Offered via video-conference with Columbia University.
P. Hackett.
Introduces students to the grammar of Classical Literary Tibetan as found in Indian treatises translated from Sanskrit into Tibetan, as well as indigenous Tibetan philosophical works. The course progresses through a sequence of the basic rudiments of the language, including an introduction to the script and its romanization, pronunciation (central Lhasan dialect), normative dictionary order, and the basic categories of grammar. Following these preliminaries, students proceed to guided readings in Tibetan literature designed to introduce them to the formal approach of Tibetan lexical semantics with an emphasis on the role of verbs in determining argument realization options. Over the duration of the course, students encounter new vocabulary (and associated Buddhist concept hierarchies) and increasingly complex sentence structures. This course thus provides a solid foundation for the later exploration of other genres of literature and styles of composition.
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TOX—Toxicology |
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TOX 3070 - [Pesticides, the Environment, and Human Health] (crosslisted)
(also ENTOM 3070 )
Fall. 2 credits.
Next offered 2014-2015. (Offered alternate years) Permission of instructor or sophomore standing required. Lec.
J. G. Scott.
For description, see ENTOM 3070 .
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TOX 4370 - Regulation of Cell Proliferation, Senescence, and Death (crosslisted)
(also BIOMG 4370 )
Fall. 2-3 credits, variable.
Prerequisite: two majors-level biology courses and BIOMG 3300 , or BIOMG 3330 , or BIOMG 3350 , or BIOMG 3310 /BIOMG 3320 . Recommended prerequisite: BIOMG 2800 and BIOMG 4320 . Enrollment limited to: approximately 20 students per discussion. Students may take lecture for 2 credits or lecture and discussion for 3 credits.
S. Lee.
For description and learning outcomes, see BIOMG 4370 .
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TOX 4900 - Toxicology of Insecticides (crosslisted)
(also ENTOM 4900 )
Spring. 3 credits.
Prerequisite: general chemistry course. Offered alternate years.
J. G. Scott.
For description and learning outcomes, see ENTOM 4900 .
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TOX 5970 - Risk Analysis and Management (crosslisted)
(also CEE 5970 )
Spring. 3 credits.
Prerequisite: introduction to probability and statistics course (e.g., CEE 3040 , ENGRD 2700 , ILRST 2100 , or AEM 2100 ); two semesters of calculus. Enrollment limited to: senior or graduate students; or permission of instructor.
J. R. Stedinger.
For description, see CEE 5970 .
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TOX 6100 - Introduction to Chemical and Environmental Toxicology (crosslisted)
(also BIOMI 6100 )
Fall. 3 credits. Letter grades only.
Offered even years. Enrollment limited to: permission of instructor or graduate standing in field required.
A. Hay.
For description, see BIOMI 6100 .
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TOX 6110 - Molecular Toxicology (crosslisted)
(also NS 6110 )
Spring. 3 credits.
S. Bloom, D. Muscarella, B. Strupp.
For description and learning outcomes, see NS 6110 .
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TOX 6990 - Toxicology Journal Club (crosslisted)
(also BIOMI 6990 )
Spring. 1 credit.
Required for toxicology students until post-A exam.
A. G. Hay.
For description, see BIOMI 6990 .
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TOX 7010 - Mouse Pathology and Transgenesis (crosslisted)
(also VTBMS 7010 )
Fall. 1 credit. Letter grades only.
Prerequisite: basic histology course BIOAP 4130 and NS 4900 are highly recommended. Permission of instructor required. Enrollment limited to: 12 students. Meets during second half of semester and relies on background information from NS 4900 , which meets during first half. Students interested in both courses must register for them separately.
A. Nikitin, staff.
For description, see VTBMS 7010 .
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TOX 7020 - Seminar in Toxicology (crosslisted)
(also NS 7020 )
Fall. 1 credit. S-U grades only.
A. Hay.
For description, see NS 7020 .
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TOX 7130 - Cell Cycle Analysis (crosslisted)
(also VTBMS 7130 )
Spring. 1 credit. S-U grades only.
Offered alternate years. Minimum enrollment 5.
A. Yen.
For description, see VTBMS 7130 .
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TOX 8900 - Master’s Thesis and Research
Fall, spring. 1-12 credits, variable.
Permission of instructor and committee chair required.
Staff.
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TOX 9900 - Doctoral Thesis and Research
Fall, spring. 1-12 credits, variable.
Permission of instructor and committee chair required.
Staff.
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UKRAN—Ukrainian |
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UKRAN 3300 - Directed Studies
Fall or spring. 1-4 credits, variable.
Permission of instructor required.
W. Browne.
Taught on a specialized basis to address particular student needs.
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URDU—Urdu |
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URDU 1125 - Introduction to Urdu Script (crosslisted)
(also NES 1312 )
Spring. 2 credits. Letter grades only.
Prerequisite: HINDI 1101 or permission of instructor. Permission of instructor required.
N. Rizvi, S. Singh.
Introduction to Urdu reading and writing. Assumes some knowledge of spoken Hindi-Urdu. May be taken concurrently with HINDI 1102 .
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URDU 2225 - Intermediate Urdu Reading and Writing I (crosslisted)
(also NES 2201 )
Fall. 2 credits. Letter grades only.
Prerequisites: URDU 1125 or permission of instructor.
N. Rizvi, S. Singh.
This course is designed to develop competence in Urdu reading and writing for students with a first-year knowledge of Hindi and knowledge of Urdu script. May be taken concurrently with Intermediate Hindi.
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