Life Sciences Data Analysis Series Individual Course
Statistics and R
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
Matrix Algebra and Linear Models
Learn to use R programming to apply linear models to analyze data in life sciences.
Statistical Inference and Modeling for High-throughput Experiments
A focus on the techniques commonly used to perform statistical inference on high throughput data.
High-Dimensional Data Analysis
A focus on several techniques that are widely used in the analysis of high-dimensional data.
Introduction to Bioconductor
A course on the structure, annotation, normalization, and interpretation of genome scale assays.
Case Studies in Functional Genomics
Perform analyses with RNA-Seq, ChIP-Seq, and DNA methylation data, using open source software, including R and Bioconductor.
Advanced Bioconductor
Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.