• Provides a practical introduction to probability and statistics used in modern biology. Topics covered include discrete and continuous probability distributions, statistical modeling, hypothesis testing, independence, conditional probability, multiple test corrections, nonparametric methods, clustering, correlation, linear regression, principal components analysis with applications to high-throughput DNA sequencing and image data analysis. Homework is in the R programming language, but prior programming experience is not required. Students taking the graduate version are expected to explore the subject in greater depth.

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