Our classes and workshops have an interdisciplinary mix of undergraduate, graduate, and returning professional students who participate in active, collaborative synchronous and asynchronous learning through options for in-person or online courses. This variety in available participation modes supports students by addressing geographic and time constraints that might otherwise limit their access to formal education in earth data science.

We contribute to the larger field of earth and environmental data science education by studying best practices for online and in person data-intensive teaching and learning. We focus on reaching diverse audiences, including working professionals with families, students at schools serving communities historically underrepresented in STEM and first generation college students.

Earth data science education should be open and available to anyone. The earthdatascience.org learning portal has free and open lessons, textbooks and courses used by over 200,000 people each month across the world that allow anyone to learn and teach earth data science skills.

We maintain free and open tools that support teaching and learning earth data science. Our tools include EarthPy which makes it easier to download data and work with spatial data, as well as Abc-Classroom and Matplotcheck which support teaching and autograding. We also maintain a custom, tested Python conda environment which is used to support all of our teaching and online lessons.

As the availability and volume of earth data continues to grow, and science and industry become increasingly collaborative and interdisciplinary, there is a growing need for professionals with a combination of hard and soft skills. Backed by market research, our programs focus on five learning areas: data science workflows, domain science knowledge, knowledge of varying data types, scientific communication and collaboration. These skills prepare our students for success.

The Earth Lab Education Team is committed to leading, promoting and facilitating earth data science education for groups historically underrepresented in STEM. Promoting diversity, equity, inclusion, and accessibility (DEIA) is not only fair, it improves the quality of science. We do this through a combination of workshops, training, capacity building with faculty and developing open curriculum and tools that anyone can use in their courses. We also collect data and conduct research to assess the effectiveness of our approaches. We currently lead two projects that explicitly focus on equitable access to earth data science skills: the Earth Data Science Corps and pyOpenSci.