Global Change Science Analytics with R

Global Change Science Analytics with R taught by Dr. Matthew R. Helmus is a course that provides an overview of how researchers use data to solve global problems such as climate change, mass extinction, pandemics, and poverty. Students explore interdisciplinary data, from economics to public health and learn a marketable skill: communicating data with R, an intuitive statistical computer language. The course is project-based, no prior coding experience is necessary, and no tests are given. Instead, assessment is on project progress and communication of a global change problem of their choice. The most successful students leave class with the quantitative skills to go out and solve our most pressing problems. All majors are welcome! Instructor permission is required. See the application here.

Credit Hours: 3.000 Levels: Graduate, Undergraduate
Schedule Types: Base Lecture. Spring 2019 Syllabus


Below are final projects produced by students who took the course. These examples span the range of R techniques learned and ideas explored. Before this class, most of these students never had training in R or computer programing.

Example Final Undergraduate Projects

Distribution of street trees in Philadelphia, PA

Adverse Childhood Experiences: A Mid-Atlantic Investigation

A Comprehensive Breakdown of the Education Budget across US States and the Factors that Influence It

The Spread and Prevalence of Bd in American Amphibians

Investigating Factors Impacting Childhood Mortality Rates in the ASEAN Region

Determining the Correlation between Pesticide Usage, Children’s Health, and Income in California


Example Final Graduate Projects (all are related to their thesis work)

Detecting species interactions with machine learning APIs: expanding the hotdog/nothotdog paradigm?

Limb Autotomy Effects on Spider Gait and Running Performance

More propagules, more invaders? Biofouling and ship-borne introductions in the Pacific coast

Predation increases functional diversity in the tropics revealing latitudinal variation in community assembly

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