Statistics 630 - Statistical Methods/DataSci

This course provides an introduction to the statistical techniques that are most applicable to data science. Topics include regression, classification, resampling, linear model selection and regularization, tree-based methods, support vector machines and unsupervised learning. The course includes a computing component using statistical software.

Open to Graduate students only. Undergraduates may enroll with the permission of the instructor.

Students must have understanding of linear algebra at the level of Math 235. Students must have prior experience with a statistical programming language such as R, Python or MATLAB. Co-requisites Stat 516 or Stat 608.