Probability and Statistics for Data Science and Bioinformatics

Course description

Probability; Kolmogorov’s axioms; independence; random variables; discrete and continuous distributions; expected values; joint, marginal and conditional distributions; Monte Carlo simulation; sampling distributions; law of large numbers; central limit theorem; maximum likelihood estimation; confidence intervals and hypothesis testing involving one- and two-sample problems; linear regression; proofs of key results; practical examples illustrating the theory; and introduction to a statistical software.


Calculus through multivariate calculus and department consent required.

Spring 2018