R Courses

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

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

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

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

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

Sentar las bases de conocimiento en R y aprender a discutir, analizar y visualizar datos.

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

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

Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

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

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

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

Learn how to effectively use data to tackle your business decisions. Designed for managers, this course provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information.

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

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

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

Show what you’ve learned from the Professional Certificate Program in Data Science.