(0.25 credits)

1. An introduction to RMarkdown, functional note taking and reproducible data analysis, plain text.
2. More R! data types, installing packages, simple plots, exploring data
3. Making a function() and running it.
4. An introduction to Bayesian thinking. Monte Hall and Bertrand’s box, Regression to the mean, Gambler’s fallacy 5. Common statistical errors when designing and interpreting biomedical experiments; or why the p-value is what you get, but the effect size is what you want. 6. An introduction to high throughput sequencing and data types generated in high throughput sequencing. The error structure and correlations in high throughput sequencing. Counts, or the idea of a lattice