DSBA 6115: Statistical Learning with Big Data

University of North Carolina, Charlotte

A survey of major statistical learning concepts and methods for big data analysis, including both supervised and unsupervised learning such as resampling methods, support vector machines, model selection and regularization, tree-based methods and ensembles, and statistical graphics. �Students learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms in case studies in financial investment, gene identification, and feature selection in high-dimensional spaces.

Average GPA: 3.71

Grade distribution records: 91 students across 9 terms.

Grade distribution

Based on 91 student grade records across 9 terms and 2 professors.

Instructors

• Jiancheng Jiang 52 students, Average GPA 3.54
• Jun Song 39 students, Average GPA 3.92