Graduate Courses

The faculty has approval to offer the following courses in the academic years 2019–2020 and 2020–2021; however, not all courses are taught each semester or summer session. Students should consult the Course Schedule to determine which courses and topics will be offered during a particular semester or summer session. The Course Schedule may also reflect changes made to the course inventory after the publication of this catalog.

Management Information Systems: MIS

MIS 380. Seminar in Organizational Communication.

Selected topics in organizational communication, written and oral. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

Topic 1: Research Methodology in Business and Organizational Communication.
Topic 2: Projects, Proposals, and Presentations. Communicating effectively in business using advanced writing and presentation concepts and techniques to increase individual and team effectiveness.
Topic 3: Advanced Report Writing, Professional Reports, and Other Scholarly Papers.

MIS 380N. Topics in Information Management.

Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Offered on the letter-grade basis only. Prerequisite: Graduate standing.

Topic 2: Managing Information. Understanding, designing, and controlling the information processing activities of an organization. Complements Business Administration 380C by focusing on information systems rather than information technology. Includes business intelligence, knowledge management, data modeling, group decision support systems, and electronic commerce. Offered on the letter-grade basis only. Additional prerequisite: Business Administration 380C.
Topic 3: Business Process Excellence. Emerging technology, data and process modeling (flow focus for integrated applications), reengineering, and change management. Offered on the letter-grade basis only. Additional prerequisite: Business Administration 380C.
Topic 4: Digital Economy and Commerce. Offered on the letter-grade basis only. Additional prerequisite: Management Information Systems 380N (Topic 2) and credit or registration for Management Information Systems 380N (Topic 3).

MIS 181N, 281N, 381N. Topics in Information Systems.

Selected topics in information technology and management of information systems development. For each semester hour of credit earned, one lecture hour a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

Topic 1: Introduction to Data Management.
Topic 2: Research in Information Systems: Organizational and Behavioral Perspectives.
Topic 3: Strategic Analysis for High-Tech Industries. Management 185, 285, 385 (Topic 9) and Management Information Systems 181N, 281N, 381N (Topic 3) may not both be counted. Additional prerequisite: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).
Topic 4: Decision Support Systems.
Topic 5: Information Systems Design and Implementation. Additional prerequisite: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).
Topic 6: Research Seminar.
Topic 7: Information and Knowledge Management. Additional prerequisite: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).
Topic 8: Managing Disruptive Innovations. Focuses on the management of disruptive technologies, including analyzing whether an emerging technology is sustaining or disruptive, identifying new markets for disruptive technologies, justifying investments in disruptive technologies, implementing disruptive technologies, and appropriating value from them.
Topic 9: Change Management Practicum I. Project-oriented course focusing on design of organizational change.
Topic 10: Change Management Practicum II. Project-oriented course focusing on implementation of organizational change. Additional prerequisite: Management Information Systems 381N (Topic 9).
Topic 11: Research in Information Technology.
Topic 12: Advanced Information Systems Readings.
Topic 13: Advanced Data Communications. Additional prerequisite: Management Information Systems 381N (Topic 8).
Topic 14: Global Information Technology Management.
Topic 15: Introduction to Electronic Commerce.
Topic 16: Information Systems Projects.
Topic 17: Client/Server Development.
Topic 18: Innovation, Technology, and Commercialization.
Topic 19: Technology Transfer: Theory and Practice.
Topic 20: Cross-Cultural Issues in Information Systems.
Topic 21: Seminar in Multimedia Systems.
Topic 22: Information Technology Strategy and Services. Additional prerequisite: Management Information Systems 380N (Topic 2: Managing Information), 380N (Topic 3: Managing Systems), and credit or registration for Management Information Systems 380N (Topic 4: Digital Economy and Commerce).
Topic 23: E-Business: Strategy and Policy. The responsibilities of the strategist for choosing, developing, and managing an overall e-business firm strategy in uncertain market, technology, and policy environments.
Topic 24: Global E-Business: Theory and Cases. Analysis of case studies, incorporating Oracle and other Web-based distributed computing solutions. Additional prerequisite: Consent of instructor.
Topic 25: E-Security and E-Forensic Frameworks. Discussion and hands-on use of current Web and distributed computing security software and e-forensic solutions. Additional prerequisite: Consent of instructor.
Topic 26: Research Methods in Information Systems. Restricted to doctoral students. Overview of research methods used to study information systems problems. Fundamental concepts and criteria for use with and evaluation of quantitative and qualitative, positivist and interpretive research methods. Current state-of-the-art applications.
Topic 27: Strategies for Networked Economy. Analyzes the competitive dynamics of platform-mediated networks; explores innovations like cloud computing in supporting network-based competition, the implications of information technology-enabled global sourcing, and business intelligence for business value and competitive advantage; and discusses the role of information technology in business transformation and making a case for information technology investments.
Topic 28: Data Management. Explore general database concepts such as E-R modeling, relational database design, and advanced SQL. Design and develop mission-critical web-based business applications using databases. Explore data warehouse design and advanced analytics functions within SQL. Management Information Systems 181N, 281N, 381N (Topic 28) and 284N (Topic: Data Management) may not both be counted.

MIS 382N. Topics in Information Management.

Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

Topic 1: Managing Financial Information. Data modeling and information management for investment analysis and financial systems.
Topic 2: E-Business Change.
Topic 3: E-Business Application Development.
Topic 4: Cross-Functional Systems Integration. Prerequisite: Management Information Systems 380N (Topic 2), 380N (Topic 3), and credit or registration for Management Information Systems 380N (Topic 4).
Topic 5: Managing Complexity.
Topic 6: Computer Auditing and Systems Security.
Topic 7: Project Management in Fast-Cycle Environments.
Topic 8: Balanced Scoreboard: An Information Systems Perspective. Theory and tools that support the design and implementation of balanced scoreboard evaluation systems.
Topic 10: Data Mining for Marketing.
Topic 11: Business Intelligence Capstone. Explores foundations of business analytics related to database management, data analysis techniques, and business decision making to solve a business problem of a client. Additional prerequisite: Consent of instructor.
Topic 12: Social Media Analytics. An introduction to social network analysis for business value using statistical optimization and decision theory, including the foundation for analyzing online search and conversation data for market sensing, sentiments, product quality, reputation, recommendations, and brand awareness. Additional prerequisite: Consent of instructor.
Topic 13: Predictive Analytics and Data Mining. Management Information Systems 382N (Topic 9: Business Data Analytics with Data Mining) and and 382N (Topic 13) may not both be counted.
Topic 14: Business Data Science. An introduction to basic concepts, methodology, algorithms, and technology used in business analytics and decision making. Explore concepts from probabilistic modeling, analysis and experimental design. Examine the basics of modern regression and classification, clustering, visualization, dimensionality reduction, A/B Testing and an introduction to deep learning. Management Information Systems 382N (Topic: Business Data Science) and 382N (Topic 14) may not both be counted.

MIS 383N. Topics in Information-Intensive Business Processes.

Topics in management of information in specific industries or application areas. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

Topic 1: Health Care Management.
Topic 2: Health Services Seminar.
Topic 3: Customer Insights.
Topic 4: Supply Chain Management.
Topic 5: Computer Tools for Investment Science.
Topic 6: Trading-Floor Technology.
Topic 10: Practicum in Multimedia Systems Development. Restricted to MBA and MPA students who have chosen the information management concentration. Additional prerequisite: Business Administration 380C and consent of instructor.
Topic 12: E-Business Innovation.
Topic 13: Managing Innovation in a Global Company. Examines innovation-based business strategies that rely on internal and external sources, processes in different organization forms, and market structures.

MIS 184N, 284N, 384N. Topics in Business Analytics.

Restricted to students admitted to the Master of Science in Information, Risk, and Operations Management. Selected topics in business analytics. One, two, or three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate Standing; additional prerequisites vary with the topic.

MIS 385. Management Information Systems.

Restricted to students in the MS in Information Technology and Management Program. Overview of hardware and software life cycles; in-depth considerations of program design, including experience programming for large-scale computer systems in COBOL, FORTRAN, and/or BASIC. Three lecture hours a week for one semester. Prerequisite: Graduate standing.

MIS 185N, 285N, 385N. Topics in Information Technology and Management.

Restricted to students admitted to the Master of Science in Information Technology and Management program. Selected topics in information technology and management. For each semester hour of credit earned, the equivalent of one lecture hour a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing; additional prerequisites vary with the topic.

Topic 1: Big Data and Distributed Programming. Explore a range of subjects required for developing modern applications that operate over vast data sets that are potentially distributed in nature. Consider alternative technologies and architectures for working with big data, examining the pros and cons of the different approaches. Management Information Systems 284N (Topic: Big Data/Distr Programming) and 185N, 285N, 385N (Topic 1) may not both be counted.
Topic 2: Emerging Technologies I. Explore all aspects of the Internet of Things (IoT) product life-cycle. Interface with the devices (sensors/actuators) that collect data and affect the environment. Explore network protocols for communication with these constrained devices. Examine programming of the back-end services that host, manipulate and disseminate the collected data. Study the development of apps that facilitate human interaction with these devices and the analysis of the data they produce. Examine security, privacy and performance considerations specific to IoT. Management Information Systems 284N (Topic: Emerging Technologies I) and 185N, 285N, 385N (Topic 2) may not both be counted.
Topic 3: IT Security, Policy, and Compliance. Explore the prevention and mitigation of data security and privacy risks in newly designed digital artifacts through IT governance, risk, and control frameworks as well as relevant laws, regulations, and industry standards. Management Information Systems 382N (Topic: IT/Secur/Policy/Compliance) and 185N, 285N, 385N (Topic 3) may not both be counted.
Topic 4: IT Capstone. Develop real-life business and social solutions using emerging information technologies. Engage with industry partners to explore business context for IT Capstone projects. Management Information Systems 382N (Topic: IT Capstone) and 185N, 285N, 385N (Topic 4) may not both be counted.
Topic 5: Cognitive Computing. An overview of convolutional neural networks, recurrent neural networks and generative adversarial networks. Explore deep learning and artificial neural networks. Applications include computer vision, image and time series modeling as well as computational aspects of deep learning over big datasets. Utilize Python and Tensorflow, among other tools. Management Information Systems 284N (Topic: Cognitive Computing) and 185N, 285N, 385N (Topic 5) may not both be counted.
Topic 6: Strategic IT and Change Management. Explore the strategic management of new IT-embedded product and service innovations and their incorporation into the digital business ecosystems of organizations. Management Information Systems 284N (Topic: Strat IT and Change Mgmt) and 185N, 285N, 385N (Topic 6) may not both be counted.
Topic 7: IT and Supply Chain Management. Examine the role of Information Technology in managing Supply Chains. Explore the IT capabilities needed by firms to coordinate their operations, collaborate with business partners and manage uncertainty. Illustrate the role of technologies and tools like ERP platform, ABAP programming, XML, web services, distributed computing and machine learning to improve the performance of supply chains. Management Information Systems 284N (Topic: IT and Supply Chain Mgmt) and 185N, 285N, 385N (Topic 7) may not both be counted.
Topic 8: Design Methods. Utilize design tools and methods to understand user needs, frame business opportunities, and design solutions. Examine design from both organizational and technical perspectives. Conduct research with end users, synthesize data, prototype solution ideas, and communicate compelling stories. Undertake design challenges that focus on emerging information technologies, including the internet of things (IoT), cognitive computing, AI, cloud, mobile, and 3D/4D printing. Management Information Systems 382N (Topic: Design Methods) and 185N, 285N, 385N (Topic 8) may not both be counted.
Topic 9: Advanced Programming and App Development. Explore various approaches to modern app development, including required advanced programming and software engineering concepts. Explore approaches to app development ranging from native platform programming through programming frameworks that allow cross-platform development, to high-level approaches based on web frameworks. Management Information Systems 382N (Topic: Adv Programming/App Devel) and 185N, 285N, 385N (Topic 9) may not both be counted.
Topic 10: User Generated Content Analytics. Generate business and social insights from user-generated content (e.g., text, images, video, etc.) through the use of text analytics, sentiment analysis, visualization techniques, etc. Management Information Systems 381N (Topic: User Genrtd Content Anlytcs) and 185N, 285N, 385N (Topic 10) may not both be counted.
Topic 11: Advanced Data Mining and Web Analytics. Examine a variety of data mining and machine learning techniques for descriptive, predictive and prescriptive analytics. Explore approaches to analyzing different types of information from the Web (web structure, content, usage). Management Information Systems 382N (Topic: Advanced Mining/Web Analytics) and 185N, 285N, 385N (Topic 11) may not both be counted.
Topic 12: Healthcare IT and Analytics. Design new healthcare solutions using emerging information technologies such as Internet of Things, cognitive computing, artificial intelligence, the cloud, mobile, and 3D and 4D printing.
Topic 13: Emerging Technologies II. Explore the design and development of blockchains and their applications in healthcare, information security, supply-chain logistics, and enterprise systems. Management Information Systems 382N (Topic: Emerging Technologies II) and 185N, 285N, 385N (Topic 13) may not both be counted.
Topic 14: Programming Blockchain. Examine the ins and outs of blockchain development. Explore the details of how Bitcoin works including live coding challenges. Management Information Systems 284N (Topic: Programming Blockchain) and 185N, 285N, 385N (Topic 14) may not both be counted.

MIS 698. Thesis.

The equivalent of three lecture hours a week for two semesters. Offered on the credit/no credit basis only. Prerequisite: For 698A, graduate standing in information, risk, and operations management and consent of the graduate adviser; for 698B, Management Information Systems 698A.

MIS 398R. Master's Report.

Preparation of a report to fulfill the requirement for the master's degree under the report option. The equivalent of three lecture hours a week for one semester. Offered on the credit/no credit basis only. Prerequisite: Graduate standing in information, risk, and operations management and consent of the supervising faculty member and the graduate adviser.

MIS 399W, 699W, 999W. Dissertation.

May be repeated for credit. Offered on the credit/no credit basis only. Prerequisite: Admission to candidacy for the doctoral degree.

Operations Management: O M

O M 380. Seminar in Operations Management.

Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing; additional prerequisites vary with the topic.

Topic 1: Combinatorial Optimization. Concepts of computational complexity; the foundation of discrete mathematics and combinatorial theory.
Topic 2: Linear Programming. Model formulation: solution algorithms; duality theory; decomposition; sparse matrix issues; sensitivity and parametric analysis; optimization and matrix generation computer software.
Topic 3: Network Optimization. Applications, theory, and algorithms of the shortest path, maximum flow, and minimum cost flow problems. Discussion of classic and contemporary aspects of network optimization, including auction algorithms and cost-scaling techniques, to provide an integrated view of theory, algorithms, and applications. Additional prerequisite: Coursework in linear algebra and introductory coursework in operations management.
Topic 4: Algorithms and Implementations. Design, analysis, implementation, and use of computer algorithms. Introduction to fundamental data structures, sorting, recursive programs, dynamic data structures, memory management, algorithm design techniques and complexity analysis, and applications in optimization problems. Examples from linear and integer programming, covering, knapsack, graph-theoretic problems, network analysis, and scheduling.
Topic 5: Business Process Simulation. Modeling with simulation languages; random number generation; statistical analysis of input and output; variance reduction techniques; computer software applications. Additional prerequisite: Introductory coursework in operations management and statistics.
Topic 6: Integer Programming. Mathematical programming models with discrete (integer) decision alternatives. Model formulation and solution algorithms. Additional prerequisite: Coursework in linear programming.
Topic 7: Nonlinear Programming. Optimization of nonlinear functions of many variables subject to linear or nonlinear constraints. Basic theory, solution algorithms, applications, computer software. Additional prerequisite: Coursework in advanced calculus and linear algebra.
Topic 8: Large-Scale System Optimization. Formulation and solution of large mathematical optimization models. Focus on algorithms that exploit special structure of linear and nonlinear programming models. Applications. Additional prerequisite: Coursework in advanced calculus and linear programming.
Topic 9: Stochastic Processes. Discrete stochastic systems, queueing processes, inventory models, replacement, renewal theory, Markovian processes. Additional prerequisite: Mathematics 362K or the equivalent; completion of calculus and mathematical statistics and probability is recommended.
Topic 10: Queueing Systems. Deterministic queues, priorities, random walks, networks, approximations, and applications. Additional prerequisite: Operations Management 380 (Topic 9) or the equivalent.
Topic 11: Graduate Seminars. Required for doctoral students in operations management.
Topic 12: Logistics. Tools and concepts for the management of the flow of information, material, product, and cash between the initial suppliers of raw material and the ultimate consumers of finished goods.
Topic 13: Management Planning and Control of Complex Systems. Designed to provide guidance to doctoral students interested in research on new approaches to management planning and control of complex systems, and to MBA students interested in evaluating new practices currently being used in management planning and control activities.
Topic 15: Optimization I. Introduction to operations research and optimization, including linear programming, network models, deterministic dynamic programming, decisions under uncertainty, game theory, inventory models, and simulation. Emphasis on mathematical programming models and algorithmic approach of operations research problems.
Topic 16: Optimization II. Designed to provide students, especially those involved in research, with more advanced optimization tools in several broad areas. Includes nonlinear programming, graph theory, integer programming, Markov chains, probabilistic dynamic programming, queueing theory, and metaheuristics. Emphasis on mathematical programming modeling and algorithmic approach of operations research problems. Additional prerequisite: Operations Management 380 (Topic 15).
Topic 17: Supply Chain Analytics. Supply chain analytics combines analytical tools with technology to identify trends, compare performance and highlight improvement opportunities in supply chain areas including sourcing, inventory management, manufacturing, quality, sales and logistics. Additional prerequisite: Consent of instructor.

O M 184, 284, 384. Topics in Business Analytics.

Restricted to students admitted to the Master of Science in Information, Risk, and Operations Management (MSIROM) program. Selected topics in business analytics. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

O M 186, 286, 386. Current Issues in Operations Management.

Strategic problems, policies, models, and concepts for the design and control of new or existing operations systems. For each semester hour of credit earned, one lecture hour a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

Topic 1: Service Management.
Topic 2: Supply Chain and Operations Strategy.
Topic 3: Strategic Quality Management.
Topic 4: Operations Practicum.
Topic 5: Managing Projects.
Topic 6: Decision-Support Modeling. Operations research and modeling to assist in decision making through building data models and operations research software systems. Management Information Systems 383N (Topic: Decision-Support Modeling) and Operations Management 386 (Topic 6) may not both be counted.

O M 392. Seminar: Operations Management.

Intensive analysis of operations management issues. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Admission to the doctoral degree program and consent of instructor.

Topic 1: Operations Management Colloquium.

Risk Management: R M

R M 391. Topics in Decision Analysis.

Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing; additional prerequisites vary with the topic.

Topic 1: Decision Analysis. Descriptive and normative principles of decision making. Additional prerequisite: Admission to the PPA or MPA program or consent of instructor.
Topic 2: Managing Decisions under Risk. State-of-the-art methods and tools to analyze risky decisions and design optimal strategies. Practical knowledge and practice are emphasized.
Topic 3: Research Issues in Decision Making. Talks by students and faculty members with research interests in decision making, and group discussion of the talks and of students' decision-related research. Additional prerequisite: Admission to the doctoral program in the Department of Information, Risk, and Operations Management.
Topic 4: Behavioral Decision Theory. The psychology of decision making: how and why our judgments are more fallible than we ordinarily suppose, and the extent to which predictive judgment can be improved through use of normative strategies that tell us how we should make judgments and decisions.

R M 192, 292, 392. Topics in Quantitative Finance.

For each semester hour of credit earned, one lecture hour a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

Topic 1: Modeling and Optimization in Finance.
Topic 2: Statistics for Finance.
Topic 3: Financial Engineering.
Topic 4: Mathematical Finance.
Topic 5: Computational Finance.

R M 194, 294, 394. Topics in Business Analytics.

Restricted to students admitted to the Master of Science in Information, Risk, and Operations Management program. Selected topics in business analytics. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

R M 195, 295, 395. Seminar: Risk Management.

For each semester hour of credit earned, one lecture hour a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

Topic 1: Corporate Risk Management. Analysis of risk management and security needs of businesses and individuals; related insurance coverages and other tools available to deal with risk.
Topic 2: Financial and Economic Aspects of Risk Management. Analysis of risk management techniques and insurance company operations. Similarities between insurance pricing techniques and risk management methodology.
Topic 3: Risk Management and Finance. Examination of theories underlying risk management techniques for business and insurance mechanisms; theoretical analysis of problems and practices in risk management.
Topic 5: Managing Environmental Risk.
Topic 6: Risk Analysis and Management.
Topic 7: Managing International Risk. The global aspects of risk management; basic risk and crisis management principles pertinent to multinational firms; financially, legally, and culturally multinational marketplaces such as reinsurance markets, captive offshore insurance.
Topic 8: Managing Employee Risks and Benefits. Corporate planning and public policy issues associated with employee benefits.

Statistics: STA

STA 180, 280, 380. Seminar in Business Statistics.

Selected topics in the applications of statistical methods to business problems. For each semester hour of credit earned, one lecture hour a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing; additional prerequisites vary with the topic.

Topic 1: Correlation and Regression Analysis.
Topic 2: Design of Experiments.
Topic 3: Statistical Computing with SAS.
Topic 4: Nonparametric Methods.
Topic 5: Statistical Consulting. Additional prerequisite: Coursework in mathematical statistics and regression.
Topic 6: Survey Research Methods.
Topic 7: Forecasting. Development of forecasting techniques for use in business applications. Additional prerequisite: Business Administration 386T or the equivalent.
Topic 8: Cybernetics and the Law: Societal, Economic, and Other Problems.
Topic 9: Applied Linear Models. Theory and application of linear models in empirically oriented research in business. Additional prerequisite: Business Administration 386T or the equivalent.
Topic 10: Mathematical Statistics for Applications. Introduction to the basic concepts of probability and mathematical statistics for doctoral degree students who plan to use statistical methods in their research but do not need a highly mathematical development of the subject. Includes probability distributions and estimation theory and hypothesis testing techniques. Additional prerequisite: A calculus course covering integration and differentiation.
Topic 11: Analysis of Variance. Additional prerequisite: Business Administration 386T or the equivalent.
Topic 12: Applied Multivariate Methods. Additional prerequisite: Business Administration 386T or the equivalent, and familiarity with statistical software.
Topic 13: Statistical Decision Theory. Development of the mathematical basis of statistical decision theory from both the Bayesian and the frequentist point of view. Additional prerequisite: A calculus-level course in statistics.
Topic 14: Risk Analysis and Management. The quantification and analysis of risk, considered from several perspectives: financial risk measures, strategic risk measures, stochastic dominance rules, chance constrained programming, and safety-first approaches.
Topic 15: Research on Probabilistic Judgment. Research training and experience for graduate students and advanced Business Honors Program undergraduate students who are interested in probabilistic judgment. Additional prerequisite: Statistics 309H or the equivalent and consent of instructor.
Topic 16: Probability and Science in the Courtroom. The role of probability and scientific reasoning in legal judgments: differences between probability evidence and other types of evidence; legal and psychological implications of these differences; the role of statistics, formal analyses, and expert opinions in legal decisions; their impact on judges and jurors. Management Science 380 (Topic 20) and Statistics 380 (Topic 16) may not both be counted.
Topic 17: Predictive Modeling. Introduction to statistical methods for prediction including regression analysis, logistic and multinominal regression, classification and regression trees, bias-variance trade-off, cross validation, variable selection, principal component regression and partial least squares regression. Additional prerequisite: Consent of instructor.
Topic 18: Learning Structures and Time Series. Introduction to exploring data analysis, clustering, dimension reduction, networks, text timing, and time series. Additional prerequisite: Consent of instructor.

STA 280N. Topics in Statistics.

Two lecture hours a week for one semester. May be repeated for credit when the topics vary. Offered on the letter-grade basis only. Prerequisite: Graduate standing.

Topic 1: Advanced Statistics and Econometrics with R. Statistics 280N (Topic 1) and 284N (Topic: Advanced Statistics And Econom) may not both be counted. Offered on the letter-grade basis only.

STA 381. Sampling.

Theory of sampling; sample design, including stratified, systematic, and multistage sampling; nonsampling errors. Three lecture hours a week for one semester. Prerequisite: Graduate standing and Business Administration 386T.

STA 184N, 284N, 384N. Topics in Business Analytics.

Restricted to students admitted to Master's of Science in Information, Risk, and Operations Management program. Selected topics in business analytics. May be repeated for credit when the topics vary. Prerequisite: Graduate standing.

STA 287, 387. Business Analytics and Decision Modeling.

Introduction to some of the basic concepts in quantitative business analysis that are used to support organizational decision making over various time frames. Explores methods that apply to all areas of an organization, with emphasis on financial decision making. For 287, four lecture hours a week for a half a semester; for 387, three lecture hours a week for one semester. Prerequisite: Graduate standing and admission to the McCombs School of Business.