Relevant Courses from Other Schools
Please consult the websites of the schools and departments for the most updated information on relevant courses.
26:120:560 Effective College Teaching
- Recent syllabus by Lion Gardiner
Psychology Department, Rutgers-Newark
The department is recruiting in social psychology and may expand its teaching in this area. Visit the website ›
26:830:512 Decision Making
- Recent syllabus by Mauricio Delgado
26:830:545 Research Design
26:830:595 Research Method Psychology
- Recent syllabus by Steve Hanson
26:830:613 Introduction to Social Conflict.
May be useful for Organization Management students.
26:830:667 Cognitive Processes
May serve as a minor course for behavioral students in Accounting.
Department of Public Administration, Rutgers-Newark
26:834:607 Doctoral Research Methods I
26:834:608 Doctoral Research Methods II
26:834:609 Qualitative Methods in Public Administration
- Recent Syllabus
- Course description by Dr. Van Ryzin
Department of Economics, Rutgers-Newark
26:220:515 Economics of the Public Sector
26:220:518 International Economics I
26:220:536 Health Economics
26:220:685 Development Economics
- Recent syllabus by Professor Julia Schwenkenberg
Department of Computer and Information Science, NJIT
In addition to the doctoral courses listed above, the CIS Department offers a wide range of master’s courses, many of them every semester. Courses of particular interest to our program include the following:
CS 631 Databases
CIS 634 Information Retrieval.
CS 670 Artificial Intelligence.
CS 675Evaluation of Information Systems
An exploration of the techniques, methodologies, and approaches to the evaluation of information systems within the context of the user and organizational environment. The subjects covered will include automatic activity monitoring, controlled experimentation, survey and interview design, models of human performance and flow and network models of information transfer in the organizational environment. This will include analysis of data gathered by the above approaches by methods such as analysis of variance and covariance, regression, and factor analysis. The emphasis will be on the application of these techniques in assessing information systems and their performance for users and organizations.
- Recent syllabus by Professor Roxanne Hiltz.
CS 677 Information System Principles
Reviews the role of information systems in organizations and how they relate to organizational objectives and organizational structure. Identifies basic concepts such as: the systems point of view, the organization of a system, the nature of information and information flows, the impact of systems upon management and organizations, human information processing and related cognitive concepts. Introduces various types of applications that are part of information systems.
Professor Michael Bieber.
CS 679 Management of Computer and Information Systems
The course covers management policies and practices associated with the acquisition, development, implementation, system testing and acceptance of computer and information systems. It places particular emphasis on the factors and considerations for the successful operation of computer and information systems within an organizational context. Topics included are motivating and organizing technical personnel, planning and managing the software development process, acquisition of hardware and software, planning of a facility, evaluation of the operation, charging policies, organizational objectives and strategic applications of information systems.
Professor Murray Turoff.
CS 732 Design of Interactive Systems
Covers the current professional literature on the design of interactive systems and human computer interfaces, including the "knowns, unknowns, and unk unks" of design. Three design projects will be completed during the course. The course emphasizes application areas that have a great deal of cognitive variability and diverse user populations. The student will be responsible for a final project dealing with the current professional literature in a specific area of interface design. Prerequisite: CIS 675.
Professor Murray Turoff
CS 767 Decision Support Systems
Information, preferences and their interaction in decision making activities of individuals and groups. The course begins with basic models for dealing with information and making judgments. In particular, we have a look at the many fallacies of human judgment. Second, we introduce the basics of fuzzy information engineering to deal with qualitative information, and apply this to information retrieval problems for collaborative filtering and preference modeling. Third, we investigate group decision and consensus models.
Professor Bartel van de Walle
CS 776 Independent Study (for the CIS State of the Art Paper)
CS 786 Simulation & Modeling for Engineering & Business (Crosslisted with CS 661 System Simulation)
This course covers the use of simulation as a tool for analyzing engineering and business problems. The two primary goals of the course are to learn how to plan, build and use simulation models and to develop an understanding of when simulation is an appropriate tool for analysis. Much of the work in the course will involve learning the mathematical and software tools for building simulation models, performing experiments with them, and interpreting the results.
- Recent syllabus by James Calvin
- Previous syllabus by Professor Marvin Nakayama
- Spring, 2006 course description by Professor Marvin Nakayama
CS 790 Doctoral Dissertation and Research
Department of Mathematical Sciences, NJIT
Math 698 Sampling Theory (Really a course in sample surveys.)
Prerequisite: Math 662 or equivalent. (Professor Bhattacharjee stated that our course 26:960:577 Introduction to Linear Statistical Models, which all our students take in their first semester, is enough to qualify students to take this course.)
Role of sample surveys. Sampling from finite populations. Sampling designs, the Horowitz-Thompson estimator of the population mean. Different sampling methods, simple random sampling, stratified sampling, ratio and regression estimates, cluster sampling, systematic sampling.
Math 644 Regression Analysis Methods
Prerequisite: Math 661 or equivalent.
Regression models and the least squares criterion. Simple and multiple linear regression. Regression diagnostics. Confidence intervals and tests of parameters, regression and analysis of variance. Variable selection and model building. Dummy variables and transformations, growth models. Other regression models such as logistic regression. Using statistical software for regression analysis. This course usually uses JMP, which is a menu-driven version of SAS.
Although this course overlaps with our 26:960:577 Introduction to Linear Statistical Models, it could be very useful for students who need to see this material for a second time.
Math 646 Time Series Analysis
Prerequisite: Math 661 or, permission of instructor. Time series models, smoothing, trend and removal of seasonality. Naive forecasting models, stationarity and ARMA models. Estimation and forecasting for ARMA models. Estimation, model selection and forecasting of nonseasonal and seasonal ARIMA models.
Math 661 Applied Statistics
Prerequisite: undergraduate calculus. Role and purpose of applied statistics. Data visualization and use of statistical software used in course. Descriptive statistics, summary measures for quantitative and qualitative data, data displays. Modeling random behavior: elementary probability and some simple probability distribution models. Normal distribution. Computational statistical inference: confidence intervals and tests for means, variances, and proportions. Linear regression analysis and inference. Control charts for statistical quality control. Introduction to design of experiments and ANOVA, simple factorial design and their analysis.
This course would be excellent for students who come into our program without the background expected for students in 26:960:577 Introduction to Linear Statistical Models.
Math 662 Probability Distributions
Prerequisite: a background in undergraduate statistics or permission of instructor. Probability, conditional probability, random variables and distributions, independence, expectation, moment generating functions, useful parametric families of distributions, transformation of random variables, order statistics, sampling distributions under normality, the central limit theorem, convergence concepts and illustrative applications.
Math 668 Probability Theory
Prerequisite: Math 662 or equivalent. Introduction to measure theory and integration, axiomatic probability, random variables, distribution function, expectation, independence, modes of convergence, characteristic functions, Laplace-Stieltjes transforms, sums of identically distributed random variables, conditional expectation, martingales.
This course is at a slightly higher level than our 26:960:575 Introduction to Probability and might be useful for students in finance or management science.
Math 691 Stochastic Processes with Applications
Prerequisite: Math 662 or equivalent. Renewal theory, renewal reward processes and applications. Homogeneous, non-homogeneous and compound Poisson processes with illustrative applications. Introduction to Markov chains in discrete and continuous time with selected applications.
Math 699 Design and Analysis of Experiments
Prerequisite: Math 662 or equivalent. Statistically designed experiments and their importance in data analysis, industrial experiments. Role of randomization. Fixed and random effect models and ANOVA, block design, latin square design, factorial and fractional factorial designs and their analysis.
Math 761 Statistical Reliability Theory and Applications
Prerequisite: Math 662 or permission of instructor. Survival distributions, failure rate and hazard functions, residual life. Common parametric families used in modeling life data. Introduction to nonparametric aging classes. Coherent structures, fault tree analysis, redundancy and standby systems, system availability, repairable systems, selected applications such as software reliability.
Statistics Department, Rutgers-New Brunswick
26:960:580 Stochastic Processes
16:960:567 Applied Multivariate Analysis
May substitute for 26:630:670.
26:960:577 Linear Statistical Models
- Recent syllabus by Farid Alizadeh
16:960:590 Design of Experiments
16:960:592 Theory of Probability
May substitute for 26:960:575 for Management Science majors.
16:960:593 Theory of Statistics
May serve as a minor course for Management Science majors.
School of Communication, Information, and Library Studies (SCILS), Rutgers-New Brunswick
16:194:546 Management and Information Technology
School of Management and Labor Relations (SMLR), Rutgers-New Brunswick
16:545:610 Economics for Industrial Relations and Human Resources
16:545:611 Seminar in Industrial Relations
16:545:612 Seminar in Human Resources
Rutgers Center for Operations Research
Department of Economics, Rutgers-New Brunswick
Please note: Links to recent syllabi are provided where possible. In some cases, the link goes to the web site for the individual faculty member, where the syllabus is maintained. In other cases, the link allows you to download the syllabus. Other syllabi are available in the Program Office.
These syllabi are provided as information to potential applicants. They should also help current students make their individual study plans. But they are subject to change. Students should not buy books or make other plans related to a course until they have confirmed with the instructor that they have an up-to-date syllabus for the semester in which they are taking the course.