This course will introduce students to the theory and practice of quantitative data analysis using data gathered from various environmental sensors deployed around Bennington’s campus. We will use spreadsheets and basic python coding to compile descriptive statistics, combine data from multiple sources, produce visual graphics, and perform regression analysis to quantify seasonal patterns in time-series data. We will also learn methods to gather and clean data from web archives, and properly merge it with locally collected data for wider ranging analyses.
Learning Outcomes:
Delivery Method: Hybrid in-person and remote, with faculty in-person
Prerequisites:None.
Course Level: 2000-level
Credits: 2
M 3:40PM -5:30PM (Full-term)
Maximum Enrollment: 16
Course Frequency: Every 2-3 years
Categories: All courses , Computer Science , Earth Science , Environment , Hybrid In-Person and Remote
Tags: Climate , Data , Environment , Quantitative reasoning , Water