Course focused on statistical methods for spatial data analysis. It teaches students how to account for spatial features' attributes and locations while exploring potential relationships among them. The course covers five key statistical methods: spatial point pattern analysis, spatial association (LISA), regionalization, spatial inference, and interpolation. Through a combination of lectures, labs, and a mini-research project conducted during the last six weeks of the semester, culminating in a poster presentation. This hands-on project phase enhan ces students' understanding by allowing them to apply the concepts and skills learned throughout the course. Students will utilize freeware software like R, RStudio, and GeoDa to manage, analyze, summarize, and display spatial data, gaining proficiency in data preparation, analysis, and interpretation.