University at Buffalo 3 credits Biostatistics
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. Prerequisites: STA 119 STA 119.
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