Computational Genomics

4 credits

Introduces computational and statistical approaches and practices for deriving robust and rigorous insights from modern genomics datasets. Includes genomics-inspired problems in areas such as human genetics, regulatory genomics, and single cell genomics as well as relevant foundational statistical and computational concepts such as causal inference, hidden confounding factors, and model interpretation. Recommended: familiarity with general concepts in machine learning (e.g. CSE 446 or equivalent); and some familiarity with general principles of biology (e.g., college-level introductory course or equivalent).