Spatial data is a significant component of the vast amount of data available in the real world. Whether it is the population recorded in a national census or the various shops in your neighborhood, a majority of datasets possess a location aspect that can be leveraged to maximize their potential. This course aims to demonstrate how to seamlessly integrate spatial data into your Python Data Science workflow. Through this course, you will acquire the skills to effectively interact with, manipulate, and enhance real-world data by incorporating their geographic dimension. You will gain proficiency in reading tabular spatial data in commonly used formats such as GeoJSON, shapefile, and geopackage, and effectively visualize them through maps. Additionally, you will learn how to combine diverse data sources by utilizing their location as a connecting factor, enabling you to establish relationships between them. By the conclusion of this course, you will possess a comprehensive understanding of the distinctive qualities of geographic data, empowering you to transform and adapt them to various contexts.