Rutgers, The State University of New Jersey
Undergraduate-New Brunswick
 
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Programs of Study and Courses for Liberal Arts and Sciences Students
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Mason Gross School of the Arts
Ernest Mario School of Pharmacy
Rutgers Business School: Undergraduate-New Brunswick
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Degree Requirements
Course Listing
Explanation of Three-Part Course Numbers
Accounting 010
Administrative Studies 011
Business Analytics and Information Technology 136
Business Law 140
Entrepreneurship 382
Finance 390
Business Ethics 522
Management and Global Business 620
Marketing 630
Supply Chain Management 799
Real Estate 851
Administration and Faculty
School of Communication and Information
School of Engineering
Edward J. Bloustein School of Planning and Public Policy
School of Management and Labor Relations
Honors College of Rutgers University-New Brunswick
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Camden Newark New Brunswick/Piscataway
Catalogs
New Brunswick Undergraduate Catalog 2017-2019 Rutgers Business School: Undergraduate-New Brunswick Course Listing Business Analytics and Information Technology 136  

Business Analytics and Information Technology 136

For required courses for the business analytics and information technology (136) major, see the Programs of Study chapter. Additional information about the undergraduate program can be found at http://www.business.rutgers.edu/undergrad-new-brunswick.

33:136:287 Introduction to Business Analytics (3) Introduction to Business Analytics for business administration minors. Prerequisites: 01:960:211 or 01:960:285 or 01:960:401 and 01:640:112 or 01:640:115 or 01:640:135. Business minors only; no Rutgers Business School (RBS) students. Course not eligible to transfer to RBS.
33:136:370 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. Open to Rutgers Business School majors.
33:136:385 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. Prerequisites: 01:640:135, 01:960:285. Open to Rutgers Business School majors and minors.
33:136:386 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.  Prerequisites: 01:640:135, 01:960:285. Open to Rutgers Business School majors and minors.
33:136:388 Foundations of Business Programming (3) Principles of programming and software development are covered in depth, with an emphasis on an object-oriented programming (OOP)  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 136 majors. Junior or senior status.
33:136:400 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. Prerequisites: 33:136:386. Open to 136 majors. Junior or senior status.
33:136:405 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. Prerequisites: 33:136:386. Open to 136 majors. Junior or senior status.
33:136:440 Special Topics: Relational Database and Web Applications (3) Provides the basics about the integration of relational databases and the World Wide Web.  Utilizes a server-side tool to build and query databases using html and SQL, including design of "shopping cart" applications and interactive web games.  Emphasis includes how to build, manipulate, and create output from a database to a webpage.
Prerequisite: 33:136:370. Open to Rutgers Business School juniors and seniors.
33:136:450 Investment Modeling with "R" (3) Extends upon the strategies from Investment Modeling with VBA and expands to implementing probability models in R with respect to investment models. Explores event-driven strategies in the hedge fund world and pricing inefficiences in complicated products such as options and ABS securities. Prerequisites: 33:136:370. Open to Rutgers Business School juniors and seniors.
33:136:455 Introduction to ERP (3) An introduction to Enterprise Resource Planning (ERP) systems. Addresses how enterprise information systems are integrated and facilitated to improve business operations. Emphasis on hands-on experience of SAP R/3. Includes the study of functional business domains and associated business processes, business problems inherent in unintegrated information systems, ERP systems as a solution to various business problems and challenges, and provides an overview of marketing, operations management, and accounting. Prerequisites: 33:136:370. Open to 136 majors. Junior or senior status.
33:136:465 Enterprise Architecture (3) An introduction to models, techniques, and tools for developing Enterprise Architecture (EA), the study of principles, methods, and models that are used in the design and realization of an enterprise's organization structure, business processes, information systems, and infrastructure. Covers methods and frameworks from The Open Group Architecture Framework (TOGAF), Zachman, and other frameworks. Provides an overview and history of EA, TOGAF core concepts, and follows with a tour of the IT landscape in business organizations. Presents EA through the TOGAF standard and its associated modeling language, ArchiMate. Prerequisites: 33:136:370. Open to Rutgers Business School juniors and seniors.
33:136:470 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: 33:136:370. Open to 136 majors. Junior or senior status.
33:136:471 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. Prerequisites: 33:136:370. Open to 136 majors. Junior or senior status.
33:136:485 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. Prerequisites: 33:136:385. Open to 136 majors. Junior or senior status.
33:136:486 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; how to translate this notation into a modern modeling language; and how 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. Prerequisites: 33:136:386. Open to 136 majors. Junior or senior status.
33:136:487 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. Students 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. Prerequisites: 33:136:370. Open to 136 majors. Junior or senior status.
33:136:494 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 136 majors. Junior or senior status.
 
For additional information, contact RU-info at 732-932-info (4636) or colonelhenry.rutgers.edu.
Comments and corrections to: Campus Information Services.

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