Module 4: Make Explanatory Visualizations

Module Overview

In this module, you'll master the fundamentals of data visualization in Python. You'll learn the anatomy of a figure, work with Matplotlib and Seaborn packages for creating visualizations, develop skills to identify misleading visualizations, and learn how to interpret different types of distributions. These skills are essential for creating accurate and effective data visualizations.

Learning Objectives

• Understand and work with the different components of a figure
• Create visualizations using Matplotlib and Seaborn packages
• Recognize and avoid misleading visualization practices
• Analyze and interpret various types of data distributions

Guided Project

Open DS_114_Make_Explanatory_Visualizations.ipynb in the GitHub repository below to follow along with the guided project:

Resources

• Kaggle Dataset: Climate Change: Earth Surface Temperature Data
• What makes a visualization misleading?
• Matplotlib Tutorials
• Seaborn Tutorials
• Python Data Science Handbook: Matplotlib
• Python Graph Gallery

Practice Activity

After completing the guided project, you'll work on a practice activity to strengthen your skills in creating explanatory visualizations. This assignment gives you hands-on experience with matplotlib and helps you build confidence in your data visualization abilities.

Module Project

Complete all tasks in the Jupyter notebook named DS_114_Make_Explanatory_Visualizations_Assignment_AG.ipynb provided in the GitHub repository. The project will assess your understanding of effective visualization techniques, matplotlib functions, and the application of style sheets.