This course will expose students to business statistics, the interpretation of data and results, and the ability to plan and execute statistical tests. Emphasis will be placed on applying the appropriate parameter and non-parametric analysis methods to investigate, test, and summarize data into business intelligence. The course will introduce business case materials, data discovery projects, analysis methods, and best practices in presenting technical results.

This course is designed to equip organizational leaders to make use of qualitative and quantitative data analysis for effective decision making within various types of organizations. Students explore and practice the use of quantitative techniques, such as simple and higher level statistics, probability, investment analysis, and financial statement trend analysis. These techniques and the presentation of results are applied in real-world business situations through business cases.

The aim of this course is to develop modeling techniques to go beyond basic descriptive statistics and introduce students to a variety of methods that are informative and technically advanced and provide learners with the knowledge, skills, and abilities necessary to examine data in a variety of applications and settings.

This course will present a comprehensive overview to time series forecasting models, interpretations, and methods to assess model accuracy, comparisons, and understand when models decay. Students will be introduced to a vast array of industry and organizational data to highlight applications and real business case situations. Course materials will focus on documenting and the development of effective technical/applied presentations. Students will use leading commercial analytical software tools that include JMP, SAS Studio, Tableau, and BayesiaLab.