This course is divided into separate modules, each designed to give you a hands-on experience working in teams to reproduce or examine a fundamental result in global change ecology. Each module will also introduce a different type of data and different tooling to handle it, as described in the summaries below. You will spend a few weeks working through each module in your team, largely at your own pace. Weekly readings and introductory live-code sessions will provide some necessary background, but real learning will happen only by doing. Most of the questions in your assignment for each module do not have 'right' answers, but involve open ended research to fully explore. Students without prior statistical or computational experience should be able to complete initial exercises, while teams with greater prior experience are expected to push those boundaries by going deeper into the analysis and presentation. Workflow and communication are central elements to each module. All work should appear in professional and well documented format using RMarkdown notebooks in the project GitHub repository and pass all automated checks on Travis-CI.