Data Science for Air Quality: A Python Tutorial on Analyzing Environmental Trends
What you will learn
Program with Python
Learn to use matplotlib
Visualize climate data
Use linear regression
Find real-life air pollution data
Learn data analysis techniques
Why take this course?
Interested in air quality, programming, or data analysis? Then this course is for you!
In this course, you will learn how to analyze and visualize air quality data using Python in the Google Colab IDE. We’ll explore how air quality has changed over time by comparing key indicators like the Air Quality Index (AQI), PM2.5, and NO2 levels across different years and cities. Using real-life data collected by the Environmental Protection Agency (EPA), we’ll cover how to handle missing values, prepare data for analysis, and create informative visualizations. We’ll start by importing and cleaning environmental data, ensuring it is ready for analysis. Then, you’ll learn how to perform exploratory data analysis (EDA) to identify trends and seasonal patterns. We will graph data and look into any observations we may notice. We’ll delve into advanced techniques like linear regression to examine relationships between pollutants and predict AQI values. Our visualization journey will include plotting data from multiple regions and comparing air quality across different years. You’ll learn to create clear, compelling graphs using libraries such as `matplotlib` and `seaborn`. By the end of this course, you’ll have the skills to analyze environmental data, uncover insights, and communicate findings effectively. No prior programming experience is needed. Join us and make a difference with data!