500

This course is a survey of quantitative methods as they apply to the problems of business management, marketing, finance and economics. This course involves study and analysis of numerous methods such as linear programming, forecasting, queuing models, inventory analysis and project planning and control methods. Students are also introduced to probability and statistical concepts, measurements of central tendency and regression, and correlation analysis. Equivalent: Three semester hours of Business Statistics and three semester hours of Quantitative Methods for Business or Management Decision Science.

This course introduces data-oriented forecasting models and their practical applications. Current data models popular in use for business predictive forecasting are studied. A comprehensive understanding of predictive analytics approaches is provided with real-world data. Students learn various business forecasting models and required methods to analyze business data with hands-on mainstream software applications. Techniques for presenting forecasting model findings with current data visualization tools are discussed.

This course introduces the foundations of data warehousing concepts. Business data warehouse development methodology and data warehouse planning stages are discussed. Identification of business requirements, feasibility analysis, and development of logical data warehouse models will be discussed. The students will learn the development of the data architecture as well as the implementation and administration of the data warehouse.

The students will have an in-depth understanding of the visualization techniques for business data. Several data visualization techniques for improved decision-making and problem-solving will be discussed. The students will learn how to transform complex categorical data to equivalent easy-to-use visual representation. Current tools and development environments will be discussed.

This course is a hands-on study of the current data mining techniques for business decisions. The discussed methodology includes decision trees, rule-based reasoning, neural networks, and cluster analysis. The techniques are demonstrated with data from finance, marketing, operations, economics, and other disciplines.

This course focuses on competitiveness, with emphasis placed on the close coordination of business unit operational decision making and strategic planning. Topics covered include product process design, inventory management, quality management, forecasting and statistical quality control. Operations management is an interesting mix of managing people and applying sophisticated technology. The goal is to efficiently create wealth by supplying quality goods and services.

This course focuses on operational coordination within a firm and gradually transitions to include inter-function, inter-firm, and international coordination. Specific modules focus on retail operations and the role of supply chain intermediaries, for example, distributors and sourcing agents. The impact of incentives and market imperfections, and the changing impact of the Internet and other information technology on supply chain operations are emphasized.

The students will learn the business analytic tools to help with supply chain optimization. The course introduces the techniques and the industry applications for strategic and operational issues of supply chain management. The course covers the data management practices for a globally connected business.

This course is an introduction to database management systems. Fundamentals of database models are discussed. Designs and issues concerning storage, access, and management of data and information are explored.