Enterprise AnalyticsBack to Course Guide
Enterprise Analytics is the capstone course for the Master of Science in Enterprise Analytics. Students will use integrative, problem-based learning materials including a reference text, case studies, and business analytics simulations to review, apply, and integrate content learned throughout the Master of Science in Enterprise Analytics program. Strong emphasis will be placed on enterprise analytics project completion. Business analytical topics that will be reviewed, integrated, and applied to real-world problems in business include data management and wrangling, data visualization and summary measures, forecasting, probability distributions, statistical inference, regression analysis, decision trees, cluster analysis, and working with time-series data. Students will also build upon their current knowledge to learn the fundamentals of data mining, both supervised and unsupervised, and its role in enterprise analytics. Finally, students will learn how to utilize a statistical processing software package in a lab-like setting to master the science of enterprise analytics.
The student must have completed all other courses required for this degree with the exception of the two business administration/management elective courses.
UPON COMPLETION OF THE COURSE, THE STUDENT WILL BE COMPETENT IN:
- Critically examining real-world problems using teamwork, logic, and data analytics to provide support for organizational decisions.
- Integrating business analytics capabilities with strategy, leadership, and management skills to affect positive change.
- Achieving mastery of graphical, descriptive, and inferential statistical analyses covered in the MSEA program through the use of problem-based learning techniques.
- Applying business analytical techniques through discussion, business analytics simulations, written integrative case analyses, and a final presentation.
- Understanding the role that data analytics plays in helping an organization achieve its strategic initiatives and attain a long-term competitive advantage.
- Demonstrating that data analytics and organizational decision-making are integrative processes, and that as decisions take place, new data must be analyzed and understood.
- Understanding that successful financial performance is the result of several possible variables; that is, rarely does a single variable fully explain a business outcome of interest.