Data Analysis Courses

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.

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

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

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

Learn introductory programming and data analysis in MATLAB, with applications to biology and medicine.

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.

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

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

Develop skills in digital research and visualization techniques across subjects and fields within the humanities.

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.