In this module, you will learn how to evaluate whether cloud-based raster processing delivers meaningful performance benefits compared to local execution. You will run the same raster processing task in both environments, measure execution time and resource usage, and interpret the results.

In this module, you will upload raster datasets to Amazon S3, organize them for analytical access, and apply lifecycle policies to control storage costs as data ages. You will configure transition rules that automatically move older rasters to cheaper storage tiers and validate that the policies work as expected.

In this module, you will discover how raw CSV address data is transformed into spatially enabled database records. Starting from first principles, you will build confidence with ETL thinking and apply it by loading geospatial data into PostGIS. This module focuses on doing, not memorizing.

In this module, you will move from manual scripts to automated pipelines. You will learn how Airflow schedules, runs, and retries ETL jobs so your address updates arrive every night without manual intervention.

In this module, you will learn how to detect and respond to pipeline failures. You will add logging and monitoring, so issues are visible before downstream users are impacted.

In this module, you will set up your first real-time GPS data stream using MQTT—a lightweight messaging protocol designed for high-frequency IoT scenarios. Through guided videos, live pipeline configuration, and simulated GPS feeds, you’ll learn how to publish and subscribe to live location data using a broker. You will work in a safe sandbox environment and focus on core streaming concepts: topics, payloads, latency, and reliability. By the end of this lesson, you’ll have a functioning GPS stream feeding into your local or cloud-based listener—forming the backbone of your fleet-tracking system.

In this module, you will bring your streaming GPS data to life through dynamic, interactive visualizations using Leaflet.js. Starting from basic map rendering, you’ll layer in real-time GPS updates, explore how to represent markers and polylines, and enable interactivity like panning and zooming. Whether tracking delivery fleets, wildlife, or emergency vehicles, you will walk away with a reusable dashboard foundation that updates live as new GPS points stream in. You’ll learn by doing—not just building maps, but turning raw location data into actionable, visual insights.

In this module, you will gain hands-on experience measuring end-to-end latency in a streaming GPS data pipeline and learn how to interpret latency metrics to identify bottlenecks. You’ll experiment with optimization techniques to improve performance—balancing throughput, accuracy, and responsiveness for real-time tracking applications.

In this Module, you will be introduced to temperature anomalies and explained why they are commonly used in climate analysis and sustainability reporting. You will work with NetCDF climate data and apply basic calculations to derive anomalies relative to a baseline period. By the end of this module, you will have a repeatable workflow for calculating temperature anomalies from NetCDF climate data and interpreting them for sustainability analysis.

This module focuses on analyzing climate data by visualizing long-term trends. You will create decade-scale charts and learn how to interpret patterns and variability in a way that supports sustainability analysis. By the end of this module, you will be able to produce clear, decade-long climate trend visualizations using Matplotlib and extract meaningful patterns relevant to sustainability reporting.

In this module, you will translate climate data analysis into clear, responsible ESG reporting language. The focus is on evidence-based interpretation rather than prediction. By the end of this module, you will be able to translate climate trend analyses into a concise, evidence-based ESG narrative suitable for inclusion in a sustainability report.

In this module, you will develop the skills needed to position yourself for entry-level geospatial data science roles. You will learn how to translate your technical project work into clear professional value, build a strong portfolio, and create effective career materials such as a LinkedIn profile and project summaries. You will practice crafting your professional narrative, aligning your skills with job requirements, and presenting your work in a way that resonates with employers. This module demonstrates how to bridge the gap between technical capability and career readiness in the geospatial industry.

You will design and implement an automated geospatial data pipeline that ingests nightly address updates from cloud storage, validates and transforms the records, and loads them into a PostGIS database. You will also add orchestration, retries, logging, and alerting so the workflow can run reliably with minimal manual effort, while extending the pipeline with a real-time style monitoring layer and a daily climate/ESG summary tied to the processed address data. This project serves as the capstone assessment and is designed to demonstrate mastery across all four geospatial data engineering skill expressions: cloud infrastructure, ETL and geospatial modeling, streaming-style monitoring, and climate/ESG analytics.