Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale...

A focus on the techniques commonly used to perform statistical inference on high throughput data.

Learn to use R programming to apply linear models to analyze data in life sciences.

A focus on several techniques that are widely used in the analysis of high-dimensional data.

Increase your quantitative reasoning skills through a deeper understanding of probability and statistics.

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

Learn inference and modeling: two of the most widely used statistical tools in data analysis.

Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty.