Data Clustering and Exploratory Data Analysis

Catalog description

This course introduces data clustering and exploratory data analysis (EDA). The course covers essential methods for visualizing and understanding data, including unsupervised learning techniques like k-means clustering, hierarchical clustering, and DBSCAN. Students will also learn how to explore datasets, detect patterns, identify outliers, and perform dimensionality reduction. Emphasis will be placed on using real-world datasets in the health sciences. Methods will be implemented in the R programming language. By the end of the course, students will be able to use EDA and clustering techniques to draw insights and inform data-driven decision-making.