Spatiotemporal Methods in Data Science.

Catalog description

This course introduces the principles and methods used to analyze data that vary across both space and time. Students first learn the foundations of spatial data analysis, including spatial dependence, nonstationarity, scale effects, and spatial heterogeneity. Techniques for visualizing spatial patterns, quantifying spatial relationships, and building spatially aware models are introduced using modern data science tools. The course also covers time series analysis, focusing on identifying temporal patterns such as trends and seasonality, selecting appropriate forecasting models, and generating predictions at multiple horizons. Through hands-on analysis and professional projects using real-world datasets, students develop practical skills for modeling and interpreting.