BUAN - Business Analytics

This course deals with model building, multiple regression analysis, and related methods useful in a business environment. Microcomputer software will be utilized throughout the course, with necessary computing skills being taught as the course proceeds. However, students are expected to already possess some basic familiarity with microcomputer applications. Prerequisite: STAT 161 or equivalent. Credit will be granted for only one of MGTSC 212 (formerly MGTSC 312) and STAT 252. Students may not receive credit for both MGTSC 212 and MGTSC 312.

This course is concerned with methods used to predict the uncertain nature of business trends in an effort to help managers make better decisions and plans. Such efforts often involve the study of historical data and manipulation of these data to search for patterns that can be effectively extrapolated to produce forecasts. This is a business statistics course that covers all aspects of business forecasting where the emphasis is on intuitive concepts and applications. Topics covered include the family of exponential smoothing methods, decomposition methods, dynamic regression methods, Box-Jenkins methods and judgmental forecasting methods (e.g. the Delphi method). Because forecasting is best taught through practice, the course contains numerous real, relevant, business oriented case studies and examples that students can use to practice the application of concepts. Prerequisites: MGTSC 312, MGTSC 352 or OM 352.

Application of predictive statistical models in areas such as insurance risk management, credit risk evaluation, targeted advertising, appointment scheduling, hotel and airline overbooking, and fraud detection. Students will learn how to extract data from relational databases, prepare the data for analysis, and build basic predictive models using data mining software. Emphasizes the practical use of analytical tools to improve decisions rather than algorithm details. Prerequisite: OM 252. Not open to students with previous credit in OM 420.

Visual displays of quantitative information include charts, tables, maps, dashboards, animations, and more. Such displays can be used to understand, to inform, and to convince. This course will focus on strategies for carefully and clearly communicating analytical findings to the people who need to take action based on them. We will learn to use both basic tools (MS Excel) and advanced tools (Tableau and R) to create visual displays. Evaluation components will include assignments, presentations, and exams (midterm and final exam). Prerequisites: BUAN 212. Not open to students with previous credit in OM 421.

Prescriptive analytics involves the use of data, mathematical models, and algorithms to identify optimal solutions for achieving organizational goals. This process builds on descriptive and predictive analytics, going beyond the interpretation of past events and the forecasting of future scenarios to also provide advice on the most effective actions to meet business objectives. Students acquire the skills to convert complex business problems into mathematical models, and employ Python programming and commercial solvers to derive optimal decisions. Evaluation components will consist of assignments, case studies, group projects, and two midterm exams. Prerequisites: OM 252. Not open to students with previous credit in OM 423.

The objective of the course is to study and understand process and product variation, interactions among product and process variables, and ultimately to take action to reduce variation. The topics covered include statistical process control, design of experiment, factorial design, Taguchi's methods and cases, and applications of quality control in management. Prerequisites: MGTSC 312, ECON 101 or equivalents.

Special study for advanced undergraduates. Prerequisites: consent of Instructor and Assistant Dean, Undergraduate Program.

Special Study for advanced undergraduates. Prerequisites: MGTSC 495, consent of the Instructor and Assistant Dean, Undergraduate Program.

Special Study for advanced undergraduates. Prerequisites: MGTSC 496, consent of the Instructor and Assistant Dean, Undergraduate Program.