Management Science and Information Systems 623

33:623:370 Management Information Systems (3) Fundamentals of computer technology, including hardware, software, telecommunications, and basics of the internet. The role of computer-based information and executive decision support systems in the modern firm. Design, normalization, creation, and querying of relational databases. Management of information and data. Elementary system development principles. Prerequisite: Admission to Rutgers Business School–New Brunswick.

33:623:385 Statistical Methods in Business (3) Review of principles of hypothesis testing, chi-square tests, one-way and two-way ANOVA, simple and multiple regression analysis, correlation analysis, nonparametric methods, indices, time series, forecasting, and applications to business. Prerequisite: Admission to Rutgers Business School–New Brunswick.

33:623:386 Operations Management (3) Key quantitative techniques essential for analyzing and improving business operations. Spreadsheet modeling of business decision problems, both with and without data uncertainty. Linear and integer programming optimization models. Elementary applied probability modeling and Monte Carlo simulation. Prerequisite: Admission to Rutgers Business School–New Brunswick.

33:623:388 Foundations of Business Programming (3) Principles of programming and software development are covered in depth, with an emphasis on an object-oriented (OO) programming style, using an OOP language such as C++ or Java. Studies the principles of object-oriented design using the UML modeling language; fundamental data structures and algorithm development for solving business problems. Open to 623 majors. Junior or senior status.

33:623:400 Business Decision Analytics Under Uncertainty (3) Introduction to methods for planning problems that include both time evolution and uncertainty. Covers the ideas of dynamic programming, classical decision trees, application of Bayesian methods to derive tree probabilities. Includes fundamental computer programming techniques for numerical calculations, loops and arrays. Introduction to sampling-based simulation techniques. Writing and implementing Monte Carlo simulations, and the study of specialized packages for discrete-event simulation. Prerequisite: 33:623:386. Open to 623 majors. Junior or senior status.

33:623:405 Risk Modeling (3) Introduction of the main concepts and models of decision making under uncertainty when risk aversion plays a major role. Topics serve as a foundation for models involving measures of risk and include expected utility modes with their economic background and business applications; mean-risk models with applications to finance; the concepts of value at risk and average value at risk with applications. Examines fundamental dynamic models, with applications to insurance, finance, and inventory management. Prerequisite: 33:623:386. Open to 623 majors. Junior or senior status.

33:623:470 Business Data Management (3) Introduces principles and techniques for managing corporate data resources. Techniques for managing the design and development of large database systems, including data models, concurrent processing, data distribution, database administration, and data warehousing; demonstrates their use in business applications. Discusses principles of database systems, database design, database schemas, and database manipulation using SQL. Surveys advanced database management topics such as transaction control, distributed databases, data warehouses, database ecommerce applications, and object-oriented databases. In addition to conceptual material, provides significant hands-on experience using current database technologies. Prerequisites: 33:623:370 and 388. Open to 623 majors. Junior or senior status.

33:623:471 Information System Security (3) Provides an overview of information security and assurance in ebusiness and other cyber environments. Studies key issues associated with protecting information assets, determining levels of protection and response to security incidents, and designing a consistent, reasonable information security system with appropriate intrusion detection and reporting features. Covers the fundamentals of threats, vulnerabilities, firewalls, secure access, intrusion detection, cryptography, disaster recovery techniques, and secure programming. Prerequisite: 33:623:370. Open to 623 majors. Junior or senior status.

33:623:485 Time Series Modeling for Business (3) Introduction to time-series models with emphasis on practical applications in business. Examines how dynamic financial and economic data can be modeled and analyzed using proper statistical techniques. Topics include methods for trend and seasonal analysis and adjustment, modeling and forecasting with autoregressive moving average (ARMA) processes, and model identification and diagnostics for time series, volatility, and state space models. This course provides hands-on experience by pairing lectures on methodology with lab sessions using high-performance statistical software to perform real-world data analyses. Prerequisite: 33:623:385. Open to 623 majors. Junior or senior status.

33:623:486 Optimization Modeling (3) Introduces optimization modeling beyond the confines of a two-dimensional spreadsheet. Study of appropriate mathematical notation for formulating realistic, complex optimization models, and how to translate this notation into a modern modeling language, and to represent given problem data symbolically and separate it from the fundamental model structure. Use of set-theoretic and network ideas in formulating models and representing data; use of nonlinear models or integer variables. Examines how to formulate models in a linear or convex manner; introduction of Lagrange multipliers and their economic interpretation. Prerequisite: 33:623:386. Open to 623 majors. Junior or senior status.

33:623:487 Large-Scale Business Data Analysis (3) Introduction to fundamental statistical techniques for analyzing large-scale business data; provides systematic training in statistical models for massive datasets and programming, data management, and exploratory data analysis in real-world settings. Develop context-sensitive models and perform model checking and diagnosis. Topics include parametric inference, logistic regression, nonlinear regression, causal inference, graphical models, dimension reduction, and model selection. Provides a comprehensive set of data analysis techniques through lessons, demonstrations, and programming labs. Prerequisite: 33:623:385. Open to 623 majors. Junior or senior status.

33:623:494 Data Mining for Business Intelligence (3) Explores the fundamental concepts of data mining and provides extensive hands-on experience in applying the concepts to real-world business applications. Topics include classification, clustering, association analysis, and anomaly/novelty detection. The final two weeks of the course involve student project presentations applying data mining techniques to applications such as fraud detection, web usage analysis, customer churn analysis, customer segmentation, blog mining, text mining, and other business data analysis. Open to 623 majors. Junior or senior status.