Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal...

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

Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty.

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

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

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

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

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

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

Apply tools of single-variable calculus to create and analyze mathematical models used by real practitioners in social, life, and...

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

Increase your quantitative reasoning skills through a deeper understanding of probability and statistics.

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.

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