An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
Learn to use R programming to apply linear models to analyze data in life sciences.
A focus on the techniques commonly used to perform statistical inference on high throughput data.
A focus on several techniques that are widely used in the analysis of high-dimensional data.
A course on the structure, annotation, normalization, and interpretation of genome scale assays.
Perform analyses with RNA-Seq, ChIP-Seq, and DNA methylation data, using open source software, including R and Bioconductor.
Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.