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The Business Analytics minor is a unique minor that integrates a functional area of business with computer expertise. It is a minor that develops skills in the use of the computer as a decision-making tool and provides expertise in a business field in which these skills can be applied. The modern business employer is looking for graduates who have more than just a degree in a particular functional area. They are looking for students who have multiple skills which allow them to serve their company in many ways. Business Analytics provides the training needed to develop those skills.
Fully integrated business degree combined with advanced data and quantitative analysis
Courses use multiple data platforms such as Excel, Arena, SAS and SPSS, Tableau, and R programming
Double majors are accommodated with other business disciplines that allow students to complete program with minimal additional coursework
Major areas of statistics and management science techniques, such as simulation, optimization, forecasting, and visualization individually explored in senior level coursework
This course introduces students to the use of advanced data modeling in spreadsheets and self-service business intelligence tools to analyze data and make business decisions in Excel. Power Pivot and the DAX language are used to extract meaningful information from large data sets. Power Query is introduced as an ETL tool, and the Power BI Desktop is used for visualization purposes. These topics are then applied to analyze problems in predictive analytics. Examples include advanced multiple regression and classification techniques in data mining. Prerequisites: A grade of C or better in BNAL 301, BNAL 306, and a declared major in the University or permission of the Dean's Office.
This course addresses advanced business analytics techniques and the application of such techniques to large data sets. Some alternative business analytics strategies are introduced. Descriptive, predictive, and prescriptive models are included. Topics covered in this course include data visualization and exploration, cluster analysis, and developing and calibrating predictive models for big data. Applications of multivariate, logistic, and probit regression to business analytics are discussed. Software packages such as SAS/JMP/SPSS may be used. Prerequisites: A grade of C or better in BNAL 306 and a declared major in the University or permission from the Dean's Office.
Simulation modeling is an integral part of the analytics revolution, enabling the creation of models that can represent the variability that exists in many real business systems. This course covers the theory and application of simulation modeling, with an emphasis on how simulation provides predictive and prescriptive analytics to support business decision-making. Topics include simulation fundamentals, the project life-cycle, model development, input and output analysis, verification and validation, and the presentation of a simulation study. We utilize a major commercial simulation software package for assignments and class projects. Prerequisites: OPMT 303 with a grade of C or better and BNAL 306 with a grade of C or better, senior standing and a declared major in the University or permission of the Dean's Office.
Students entering the Minor program in Business Analytics should meet the minimum university admission requirements (Undergraduate Admission)
Estimated rates for the 2022-23 academic year. Rates are subject to change. Anyone that is not a current Virginia resident will be charged non-resident rates. That includes international students.
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