Data Science in Earth and Environmental Sciences

Data Science in Earth and Environmental Sciences SyracuseUniversity View Course Problem Statement This module is designed to introduce learners to the basics of R and Python programming as well as the application of emerging data analytics and machine learning methods in the Earth and Environmental Sciences. Module Overview Applying emerging data mining tools in resolving problems in Earth and Environmental Sciences Topics Covered (1) Basics of R coding in RStudio (2) Basics of Python coding in Jupyter Notebook (3) Analysis of driving forces of wildfire (4) Analysis of impact of hydrocarbon production on groundwater quality Prerequisites N/A Learning Objectives At the end of this module, you should be able to describe and implement the steps involved in: (1) reviewing and modifying others' codes in R and/or Python (2) writing codes in R and/or Python (3) developing and implementing a data science workflow for a data-driven project (4) designing a data science project by conceptualizing a domain science problem This will be accomplished through activities within each section. Results from each activity will be recorded in specified results templates. The results templates for each activity can be found at the beginning of each activity. The results templates are organized such that results from one activity can easily be used in successive activities. Course Authors Tao Wen Assistant Professor, Syracuse University Contact: twen08@syr.edu Christina Bandaragoda Senior Research Scientist, University of Washington Contact: cband@uw.edu Lucas Harris Post-doctoral scholar, Penn State University Contact: lbh146@psu.edu Target Audience This module is designed to serve a broad mix of learners whose coding expertise ranges from beginner to expert level and whose geoscience-related research interests Tools Needed Computer with access to the Internet CUAHSI HydroShare account (https://www.hydroshare.org/) Course Sharing and Adaptation This course is available for export by clicking the "Export Link" at the top right of this page. This course can cited as follows: Bandaragoda, C., Wen, T., (2020). Data Science in Earth and Environmental Sciences. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:SyracuseUniversity+EAR601+2020_Fall/about If you are an Instructor seeking the answer keys, please contact the course creators using your official University email account. Expected Total Hours A student can expect to complete this module with approximately 30 work hours