Statistical Learning for Big Data

Graduate

This course introduces modern statistical learning methods for large, high-dimensional datasets. Topics include dimensionality reduction, regularized regression, cluster analysis, and linear discriminant analysis. The goal of this course is for students to become comfortable handling and manipulating large datasets and identifying appropriate statistical analyses to find the signal in the noise. All work will be completed in Python, and class time will be devoted to exploring Python functionality and relevant libraries such as Pandas, NumPy, and Scikit-learn. 0.75 Dartmouth units; (HP, P, LP, NC) *Core Requirement for MS in Health Data Science Online Prerequisites: HSE 712 , HSE 731 and HSE 732 Offered: Fall