Business Analytics - UG (BUAN)


BUAN 301  Evidence-Based Management: The Need for Data  3 Credits (3,0)

Students are introduced to the theories of Evidence-Based Management (EBM) and the importance of making data-driven decisions. Through several course examples, students will be asked to look for publicly available data sets to address their working hypothesis as well as bridge existing data sets to form new data relationships. Students will be exposed to consumer, economic, demographic, sales, and other types of data often used in making business decisions with prescriptive analytics. Students will also be exposed to the scientific method and the role of quantitative and qualitative analysis in generating business intelligence.
Prerequisites: MMIS 221 and CSCI 123

BUAN 302  Communication and Ethics in Data Analysis  3 Credits (3,0)

This course focuses on how data is communicated as well as best practices in data ethics. Understanding ethics, the ethical use of data, and the role of ethics in communicating data will be explored as well as common pitfalls in the overall presentation of data. Students will use recent casesthat involve communications and ethical challenges from which to explore and understand how properly communicating data can be as important as the analysis itself.
Prerequisites: BUAN 301

BUAN 304  Advanced Statistics and Analytics Concepts  3 Credits (3,0)

This course builds on the math core and business statistics to focus on mathematical models, simulation models, and forecasting tools that enable the student to work with big data in an applied business format. The student will use data cleansing concepts and prepare data for analysis and chose the propermethod of analysis based on the structure of the data. Students will use the SAS suite of products and learn the hands-on skills for data cleansing, model building and forecasting using real life scenarios.
Prerequisites: BUAN 301 and BUSW 352

BUAN 405  Applied Analytics I -- Descriptive Analytics  3 Credits (3,0)

This course focuses on descriptive analysis of a large data set to test a hypothesis. Sentiment analysis will also be introduced to manage qualitative data and obtain additional insights from qualitative sources. Live data dashboards will be introduced as an integrative bridge between descriptive analytics and data presentation. Complete analysis models will be built and tested using a variety of different data sets using the SAS suite of products as well as less structured programming languages.
Prerequisites: BUAN 304

BUAN 406  Applied Analytics II -- Predictive Analytics  3 Credits (3,0)

This course uses the SAS suite of products to make predictions and forecast results from large data sets. Students will understand the different predictive nodes of SAS suit products to include neural networks, regression models, and other predictive concepts and how to apply those models to a data set and interpret model comparison output. Students will also utilize compiled and/or interpreted coding languages to build applications for predictions.
Prerequisites: BUAN 405

BUAN 407  Business Intelligence in Industry Capstone  3 Credits (3,0)

This Capstone experience will include a student-selected project that encompasses the entire problemsolving process from data sourcing through the presentation of results using data visualizations. The project will require a report-out to an executive audience including decision recommendations. Students will also be introduced to the application of business intelligence concepts within the aviation industry as well as be exposed to marketing metrics, psychometrics, etc. and how business can use big data in everyday operations.
Prerequisites: BUAN 406

BUAN 428  Business Analytics and Data Intelligence  3 Credits (3,0)

The massive growth of the Internet and the rapid expansion of communication and information technology have resulted in a great flow of data -- both structured and unstructured, and while accessing and gathering data is important, analyzing and making sense of that data is even more important. This course introduces students to how businesses can use applications and technologies to effectively manage, analyze, and distribute enterprise data to arrive to more accurate analysis that can lead to more confident decision making and greater operational efficiencies, cost reduction, greater revenue, and reduced risks.
Prerequisites: MMIS 221