Course Descriptions

This course deals with the fundamentals of regression analysis and modeling. It covers simple linear regression, multiple regression and logistic regression. The focus is on hands-on experience. Use statistical software packages. Not to be taken if credit for STAT 5020 has been received.  (3 credits)

Logical database design and effective implementation, including hierarchical, network and relational models. Not to be taken if credit for MIS 5400 has been received. (3 credits)

This course is an introduction to modern techniques in data analysis, including stem-and-leafs, box plots, resistant lines, smoothing and median polish. Not to be taken if credit for MATH 5470 has been received. (3 credits)

This course provides students with the opportunity to develop analytical skills through the application of quantitative models to managerial problems. Students will learn to design analyses in the context of practical business situations and use the results obtained to support managerial decision-making. Topics include linear and integer programming models, including multiple criteria decision making and goal programming, decision analysis under uncertainty, and computer simulation for risk analysis.  Uses Excel spreadsheet solver software package. Not to be taken if credit for MBA 6010 has been received.  (3 credits)

The areas of focus are descriptive analytics, including data management and exploratory data analysis, and predictive analytics based on regression modeling and analysis.  (1 credit)

This course deals with the methods and applications of statistical time series analysis and forecasting. Emphasis will be on practical modeling and forecasting techniques for data collected sequentially in time. Time domain methods will be the focus of the course. Use statistical software packages. Pre-requisite: MSA 5020. Not to be taken if credit for STAT 5160 has been received. (3 credits)

This course deals with the process of organizing, processing, presenting and using big internal and external data/information to facilitate strategic decision-making.  It covers data warehouse, on-line analytical processing, data visualization and presentation, and analysis, design, and development of Business Intelligence systems. Use ERP/BI software. Pre-requisite: MIS 5400/MSA 5400. Not to be taken if credit for MIS 5600 has been received.  (3 credits)

Data mining is the analysis of large data sets for the purpose of discovering useful information, uncovering important trends, detecting frauds and prediction.  This course will cover a variety of data mining applications and algorithms.  Topics include classification and regression trees, neural networks, clustering, discriminant analysis, affinity analysis, and Bayesian methods.  Students will be exposed to a variety of applications in areas such as finance, insurance, manufacturing, marketing, fraud detection, and scientific data.  Uses spreadsheet and statistical software packages. Pre-requisite: MSA 5020. Not to be taken if credit for STAT 6440 has been received.  (3 credits)

This course focuses on analytic tools for unstructured data. The students will learn basic and advanced analytic methods. Big data analytics technology and tools, including Hadoop and MapReduce, will be introduced. It also discusses SQL extensions and other advanced SQL techniques for in-database analytics. Pre-requisite: MIS 5400/MSA 5400.  (3 credits)

This course is designed to allow students to work on analytics projects. The areas of focus are descriptive analytics, including data mining and business intelligence, predictive analytics based on data mining and time series analysis and forecasting, and prescriptive analytics based optimization techniques. Pre-requisite: MSA 6701. (1 credit)

In this course, students will learn how to deal with non-responses or in general missing-data problems that happen often to marketing research. Students will also learn contemporary techniques how to combine new evidence with prior beliefs in business decision making. Pre-requisite: MSA 5470.  (3 credits)

Learn the principles and best practices in managing business analytic projects.  Learn how to initiate, plan, execute, monitor, and control a project.  Develop the necessary skills to manage the scope, time, cost, and quality of a project.  Understand the role of effective human resource management, communication, and procurement management.  Learn to manage the project risks and integrate various tools and software products in project management.  Pre-requisite: MIS 5400/MSA 5400.  (3 credits)

This course is designed to allow students to work on analytics projects. The areas of focus are advanced modeling techniques and process optimization. In this course, student teams will submit a final paper and give an oral presentation. Pre-requisite: MSA 6702.  (1 credit)