Mathematics Courses

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

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

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

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.

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

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 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.

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.