Unit 7: Data Science for Air Quality
Apply computational and statistical methods to analyze large air quality datasets, identify patterns, build predictive models, and communicate findings through data visualization.
Unit Overview
This unit introduces data science concepts through the lens of air quality analysis. Students work with real datasets from regulatory monitoring networks and low-cost sensor arrays, applying statistical techniques and machine learning approaches to extract insights. The unit emphasizes reproducible analysis, data quality considerations, and effective visualization.
Lessons
Air Quality Data Sources
Explore regulatory monitoring networks, satellite data, and low-cost sensor deployments
EngageStatistical Analysis Methods
Apply regression, time series analysis, and hypothesis testing to air quality data
ExploreMachine Learning for Prediction
Build and evaluate models to forecast air quality and identify pollution sources
ExplainData Visualization
Create effective visualizations to communicate air quality patterns and trends
ElaborateData Analysis Project
Conduct an original analysis of air quality data and present findings
Evaluate