Courses

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

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

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.

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

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

Contracts are a part of our everyday life, arising in collaboration, trust, promise and credit. How are contracts formed? What makes a contract enforceable? What happens when one party breaks a promise?

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

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

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

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

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

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