Data Visualization Basics

In this project, you will use the Tips dataset (built into Seaborn) to practice creating different types of charts. The goal is not to do deep analysis — it is to get comfortable with the syntax and options of both Matplotlib and Seaborn.

You will build the same charts in both libraries so you can compare how they work.

Project Requirements

• Load the Tips dataset with sns.load_dataset("tips")

• Create the following charts using both Matplotlib and Seaborn:

  • Histogram

  • Bar chart

  • Box plot

  • Scatter plot

  • Heatmap of correlations

• Customize labels, titles, font sizes, and color palettes

• Save at least one chart as a PNG file

Technologies to Use

• Python

• Matplotlib

• Seaborn

• Pandas

• Jupyter Notebook

What You Will Learn

You will understand the differences between the two libraries, when to use each one, and how to control the look of your charts. This is essential before moving to more complex visualizations.

Want to See a Solution?

A similar project using different datasets is available on Towards Data Science: 🔗 Introduction to Data Visualization in Python