EDBA 741A Quantitative Research Methods II-A (2)

This course will introduce students to critical skills for succeeding in today’s data-intensive world. Utilizing both computing and mathematical perspectives will be helpful in preparing students for continued study of research methodology and applied statistics. This course will enable students to conduct data analysis and make recommendations to management. They will learn how to utilize database systems (such as SQL and NoSQL), analytics software (such as R, Python, and SAS), and how to make trustworthy predictions using traditional statistics and machine learning methods. Topics include supervised (prediction and classification) and unsupervised (exploratory data analysis, principal components, cluster analysis) learning; prediction models including multiple linear regression, nonlinear regression, time series forecasting, artificial neural networks, regression trees, k-nearest neighbors; classification models including logit/probit models and classification trees. It also covers econometric methods in parallel with other statistical methods.

Grading Basis: Graded