This semester students will conduct an evaluation of their project and manage closure processes. At the end students will submit a final written report and present it to the faculty and peers. This final project and the final report detailing the project will serve as the capstone course of the program.

This course introduces students to statistical tools for modeling and identifying patterns in complex data sets. The goal of statistical learning is to develop predictions informed by data. Topics to be covered include Gaussian linear models, cross-validation techniques, penalized regression methods such as ridge and LASSO, nonlinear models, logistic regression, tree-based models including random forests, bagging, and boosting, and support vector machines. Application areas include Marketing (e.g., effectiveness of advertising and customer satisfaction), Financial Economics (valuation), and Operations Management (resource allocation). The course delivery will be a mix of lectures, readings/podcasts with discussion, and hands-on data analyses.

This course introduces students to statistical and machine learning tools for modeling unstructured data; including emails, documents, text messages, and social media data. Topics to be covered include text mining, clustering, mixture models, deep learning, and topic models. The course integrates numerous applications to demonstrate practical approaches to analyzing large unstructured collections of data. Application areas include Marketing (Yelp and Trip Advisor reviews), Human Resources (health care plan analysis), Social Media (Twitter, YouTube, and Instagram). The course delivery will be a mix of lectures, readings/podcasts with discussion, and hands-on data analysis.

The course is designed to introduce analytical techniques needed in the estimation and analysis of temporal (time series) data in various business disciplines. The course focuses on theoretical and application aspects of stationary/non-stationary univariate as well as multivariate time series models. Emphasis will be given to topics such as time series regression, random walks, ARIMA/SARIMA processes, ARCH/GARCH for modeling conditional volatility, Vector ARMA models, and transfer functions. Modern software implementation is fully integrated into the course. Some examples of the business application areas include demand forecasting, financial asset return modeling, stochastic volatility modeling of financial indexes and securities, mortgage default risk assessment, online webpage click-rate modeling, market share modeling.