You will explore the QGIS interface and learn how to load vector and raster data to begin working with maps.

You will apply graduated color styling to map layers to improve clarity and make data easier to interpret.

You will create a clean map layout and export it as a PDF for sharing in reports and presentations

In this module, you will explore how PyQGIS is organized and how Python interacts with QGIS objects. The focus is on understanding layers, features, geometries, and the processing framework so scripts are readable and maintainable.

In this module, you will learn to write a Python script that buffers road features by 500 meters using PyQGIS processing tools. Emphasis is on correctness, parameters, and reproducibility.

In this module, you will execute your script in the QGIS Python console and save the resulting buffer layer for reporting and reuse.

In this module, you will create spatial indexes on geometry columns using PostGIS to enable efficient spatial queries. The module focuses on why spatial indexing is essential for scalable proximity analysis in compliance workflows.

In this module, you will apply spatial SQL concepts to answer a common real-world question: identifying parcels located within a specific distance of schools. Learners will move from understanding spatial relationships to executing practical PostGIS queries that use distance-based logic. By the end of the lesson, learners will be able to run spatial SQL queries that identify parcels within 100 meters of schools, building confidence in applying PostGIS functions to real analysis tasks.

In this module, you will shift focus from writing spatial queries to understanding how those queries perform. Learners are introduced to EXPLAIN ANALYZE and learn how to interpret query execution output to diagnose performance issues. By the end of the module, learners will be able to read and explain EXPLAIN ANALYZE results to understand how a spatial query is executed and where optimization opportunities may exist.

In this project, you will use a desktop GIS tool to integrate spatial and tabular datasets and create a choropleth map showing population distribution across regions. You will clean and prepare the data, perform an attribute join, apply a filter or query, and create a clear map visualization. This project develops core skills in desktop GIS workflow, including data loading, validation, joining, querying, symbology, and spatial interpretation. It also introduces foundational spatial database thinking through field matching, attribute structure, and data integrity checks. This project represents a focused applied workflow and covering Desktop GIS & Spatial Databases skill set. You will also write a short explanation of your workflow and findings. This project shows how GIS tools are used in real-world urban planning and infrastructure analysis.