Data Exploration and Visualization

Catalog description

This course explores methods for understanding and visualizing data, with a focus on approaches for dealing with large and multidimensional datasets. Students will build a toolkit for exploring real-world data, with an eye towards identifying patterns, trends, and outliers. Methods of dimensionality reduction such as PCA and t-SNE will be covered, with attention to both the mathematical underpinnings of these methods as well as their practical use. Students will also be introduced to common machine learning classification tools and assessment frameworks, as well as methods of non-parametric inference commonly used in real-world data applications.