Statistics Courses

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.

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.

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.

Develop a data mindset and the analytical skills to interpret and communicate data while applying concepts to real business problems in this course from Harvard Business School (HBS) Online.

This three-course program from Harvard Business School (HBS) Online will teach you the fundamental skills to confidently contribute to business decisions and decision-making.  

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