BSAN - Business Analytics

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.

Current information technology topics are surveyed and analyzed. Hardware, software, and communication systems used to process, store, and transmit information is studied and analyzed. Students learn how to manage information technology in today’s network enterprises and global networks. Students read current print and electronic media articles in addition to text. Due to the dynamic nature of the computer field, there is no equivalent. Exceptions will be reviewed on a case by case basis.

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.

This course introduces the use of current information technology for healthcare and health data systems. It is designed to give the student an understanding of the different types of data captured, analyzed, maintained and processed for medical studies.

This course examines the current legal environment for confidentiality of healthcare data. It introduces the laws, regulations, policy, and procedures for protecting sensitive patient data. The students learn risk assessment and how to address potential threats in a healthcare setting. Security policy and procedure development methods to secure the healthcare data as required by current laws are discussed in detail. 

Designed for the in-depth study of the healthcare systems, this course teaches systems analysis and design specifically for the healthcare data. The students learn how to identify business problem statements for healthcare organizations, how to identify data requirements, and how to gather data for detailed systems analysis. Systems development techniques to address business problems by improving existing information systems or developing new information systems are explained. Data manipulation concepts for health information systems are introduced. 

This course introduces students to the current data mining and business intelligence tools for informed decision making. The tools to process and analyze increasingly complicated data sets are explained. Real-life scenarios from finance, CRM, operations, social media marketing, information systems, and other disciplines are studied in detail. Specifically decision trees, classification, clustering, segmentation, decision support systems, search algorithms, data mining, factor and discriminant analysis and optimization concepts for both structured and unstructured data are discussed.

This course will allow students to demonstrate proficiency in business analytics with a semester project. The students are expected to employ the skills presented throughout the curriculum in an organized manner to solve realistic business data management problems. Mastery of skills for the student’s identified concentration is expected.