Data Analysis and Statistics in EaES (Fall)
We in the geosciences are feeling the effects of our society’s digital revolution. In the last couple of decades, our ability to acquire long-term, continuous, and high-frequency environmental measurements – even in remote locations – has improved radically. These awesome technological advances resolve old problems of data scarcity, but other old problems of knowing how to analyze and interpret data remains, and new problems of how to store, visualize, and manage big data have emerged.
This course will introduce you to fundamentals of coding in the free and widely used programming language ‘R’. Through hands-on classroom programming and interactive data analysis, you will become familiar with the data science workflow including how to import, tidy, visualize, analyze, and communicate big EaES datasets, including geospatial data, and how to use version control to ensure your work is reproducible, a valued skill in both private industry and academic research. The course will also cover fundamental statistics including data distributions, descriptive statistics, and linear regression. You will need access to a personal computer but there are no math or computer science (coding) prerequisites for this class.
This class is supported by DataCamp, an intuitive learning platform for data science and analytics. DataCamp’s learn-by-doing methodology combines short expert videos and hands-on-the-keyboard exercises to help learners retain knowledge. DataCamp offers 350+ courses by expert instructors on topics such as importing data, data visualization, and machine learning. They’re constantly expanding their curriculum to keep up with the latest technology trends and to provide the best learning experience for all skill levels.
Climate and Ecosystems (Spring)
This course will explore contemporary global climate change using the knowledge and methods of ecosystem science. Students will study the biogeochemical regulation of the global climate system and will focus on important mechanisms organized across a hierarchy of scales (i.e., soil microbial, to vegetation canopy, to landscape patchwork, etc.). Course content includes lecture, laboratory experiments and/or field work, geospatial data analysis, reading of primary literature and select textbook chapters, group discussions, and oral presentations.