Data Analysis Courses

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

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.

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

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

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

Learn introductory programming and data analysis in MATLAB, with applications to biology and medicine.

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

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

Learn basic data visualization principles and how to apply them using ggplot2.

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.

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

Learn to process and convert raw data into formats needed for analysis.