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...

The structure, annotation, normalization, and interpretation of genome scale assays.

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results,...

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.

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

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

Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal...

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.