This course provides foundational, advanced knowledge in the realm of business analytics. Advanced topics such as machine learning, analysis of structured data, text mining, and network analysis are covered. Industry standard tools such as R and Python are extensively used in completing student projects.

Students apply their mathematical, data analytic, and integrative business analytics skills in a complex project involving real or simulated data. Under the supervision of an advisor, students work in teams to perform a stipulated task/project and write a comprehensive report at the end of the experience. Subject to approval by the program director, an individual student internship/coop followed by an in-depth report may obtain equivalent credit.

This course introduces concepts for interpreting and analyzing data as a tool for assisting managers in making complex business decisions. Topics to be covered include: review of descriptive statistics, normal distribution, sampling distributions, estimation, test of hypothesis for single and two populations, linear regression, multiple regression and model building. The application of appropriate statistical tools will be required.

This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments.

Current topics seminars offer an in-depth examination of current events, issues and problems. Specific topics will vary depending upon student and faculty interest and on recent events in the business world. Seminar topics for a specific semester will be announced prior to the course offering. (topic-dependent)