We Can Get More from Spatial, GIS, and Public Domain Datasets

This course aims to teach learners how to utilize spatial, GIS, and public domain datasets effectively. By the end of the course, students will be able to analyze similarities in prices of short-term rental apartments, create smooth maps using limited air pollution measurements, integrate remote sensing data with Earth-sampled data, and gain insights at a finer scale from county-level socio-economic factors. The course teaches the skills of spatial interpolation using the `pyinterpolate` package. The teaching method involves practical demonstrations and examples. This course is intended for individuals working with spatial data, GIS professionals, researchers, and data analysts interested in enhancing their data analysis skills.