Spatial data (also called geospatial data or geographic information) is any type of data that directly or indirectly references a specific geographical area or location. Spatial data science is a subset of data science that focuses on spatial data. It investigates/models the spatial pattern of physical, social, economic phenomena, using large volume of georeferenced data coming from multiple sources such as remote sensing (various platforms), historic archives, contemporary monitoring programs (traffic control, industrial monitoring, weather stations), location-based services (mobile phone data, social media data, GPS trajectories). This course introduces the fundamentals of programming in spatial data science, using R and Python, two of the most used programming languages for spatial data science. Recommended prerequisites: Students should have some basic understanding of geography and spatial data (equivalent to some introductory geography courses). Please contact the instructor if you have not met these requirements, you may gain this background through self-learning. Students who receive credit for Gog 408 cannot receive credit for this course.