Data Visualization and Statistics in Geosciences

Undergraduate / Graduate course, University of Michigan, CLaSP, 2019

Co-developer and lecturer of a required 4 credit course for winter 2018 and 2019 for data visualization and statistics in climate and space scientists. All laboratory sessions are posted online.

Course Description: This is an upper level undergraduate course focusing on fundamental data science, data and error analysis, data-model comparison tests and metrics, and visualization techniques. The instructor will teach through a combination of lecture and interactive laboratory sessions from space and climate sciences using Python for analysis. No experience in Python is required, basic familiarity with programming is recommended. The course will culminate in a final project with a dataset chosen by the students and guided by the instructor. By the completion of this course, students will be able to: produce publication ready scientific data visualization, read and write data sets using Python, perform large data set analysis, and basic hypothesis testing.

Web Resource: https://github.com/astro-abby/data_vis_statistics_geosciences