Data Analysis for Life Sciences

Statistics for Life Science Research

Life science data is messy: high-dimensional, often limited sample sizes, complex dependency structures. This course teaches R and statistical foundations alongside the underlying math of linear models that make sense of biology.

You'll move from basics to techniques designed for high-throughput genomics and proteomics, learning when correlation misleads and how to extract real signal from noise. For researchers, graduate students, and analysts in biology, genetics, and biomedical fields.