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Introduction to Python for Environmental Data Analysis

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Introduction to Python for Environmental Data Analysis, available at Free, has an average rating of 4.25, with 9 lectures, based on 2 reviews, and has 419 subscribers.

You will learn about Program with Python Learn to use matplotlib Visualize climate data Use linear regression Find real-life air pollution data Learn data analysis techniques This course is ideal for individuals who are Programmers curious about the intersection of environmental science, coding, and data science/visualization. It is particularly useful for Programmers curious about the intersection of environmental science, coding, and data science/visualization.

Enroll now: Introduction to Python for Environmental Data Analysis

Summary

Title: Introduction to Python for Environmental Data Analysis

Price: Free

Average Rating: 4.25

Number of Lectures: 9

Number of Published Lectures: 9

Number of Curriculum Items: 9

Number of Published Curriculum Objects: 9

Original Price: Free

Quality Status: approved

Status: Live

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

Who Should Attend

  • Programmers curious about the intersection of environmental science, coding, and data science/visualization.

Target Audiences

  • Programmers curious about the intersection of environmental science, coding, and data science/visualization.

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!

Course Curriculum

Chapter 1: Introduction + Air Quality Basics

Lecture 1: Introduction + Air Quality Basics

Chapter 2: Set Up Google Colab Python Notebook

Lecture 1: Set Up Google Colab Python Notebook + Install Libraries

Chapter 3: Air Quality Dataset Info + Preprocessing

Lecture 1: Download Air Quality Data, Upload to Google Colab, and Preprocess Data

Chapter 4: Graphing AQI Data Using MatPlotLib

Lecture 1: Create a Basic Graph of AQI Over Time

Chapter 5: Graphing AQI vs PM2.5

Lecture 1: Create a Graph of AQI vs PM2.5

Chapter 6: Linear Regression to Evaluate AQI vs PM2.5 Correlation

Lecture 1: Use Linear Regression to Evaluate Correlation Between AQI and PM2.5

Chapter 7: Comparing AQI Data from Two Different Years

Lecture 1: Compare AQI Data from Two Different Years (2024 vs 2000)

Chapter 8: Graphing NO2 Data Over Time + Course Conclusion

Lecture 1: Create a Graph of NO2 Over Time + Course Conclusion

Chapter 9: Complete Notebook Guide With Code

Lecture 1: Notebook Guide With Code

Instructors

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  • 3 stars: 1 votes
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  • 5 stars: 1 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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