Spatial data science is concerned with analyzing the spatial distributions, patterns, and relationships of data over a predefined geographical region. It relies on the dependence of observations where the primary assumption is that nearby spatial values are associated in a certain way. For decades, the size of most spatial datasets was modest enough to be handled by exact inference. Nowadays, with the explosive increase of data volumes, High-Performance Computing (HPC) has become a popular tool for many spatial applications to handle massive datasets. Big data processing becomes feasible with the availability of parallel processing hardware systems such as shared and distributed memory, multiprocessors and GPU accelerators. In spatial statistics, parallel and distributed computing can alleviate the computational and memory restrictions in large-scale Gaussian random process inference. In this course, we will first briefly cover the motivation, history, and recent developments of statistical methods so that the students can have a general overview of spatial statistics. Then, the cutting-edge HPC techniques and their application in solving large-scale spatial problems with the new software ExaGeoStat will be presented.