This course establishes a foundation in applied statistics and data science for those interested in pursuing data-driven research. The course may involve examples from any area of science, but it places a special emphasis on modern biological problems and data sets. Topics may include data wrangling, data exploration and visualization, statistical programming, reproducible data analysis, likelihood based inference, Bayesian inference, bootstrap, EM algorithm, regularization, statistical modeling, principal components analysis, latent variable modeling, multiple hypothesis testing, and causal inference. The statistical programming language R is used extensively to explore methods and analyze data.
A detailed syllabus, which includes grading and administrative detials, can be found on the course Blackboard site.