Climate and Sustainability Courses

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

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

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

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.

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

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

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.

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.

Join Harvard University instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.