Multivariate statistics training to analyze Big Data sets. The course covers discrete choice modeling (logistic and probit models), classification techniques (discriminant and cluster analyses), data reduction techniques (factor analysis), and advanced predictive techniques (regression models with interactions and curvilinear effects, structural equation modeling, and factorial ANOVA). Trains students on IBM-SPSS, SAS, and R.

This interdisciplinary course explores four critical areas at the intersection of business and sustainability: 1) Climate Finance, 2) Conservation Finance, 3) Circular Economy and 4) ESG Investing. In addition to acquiring an understanding of these key sustainability challenges, participants will gain skill in applying analytical tools and techniques to the evaluation of sustainable investment opportunities.

Students taking this course will explore areas and issues of special interest that are not otherwise covered in the graduate curriculum. This course is offered at the discretion of the supervising professor.