2 Data Visualization with Seaborn

2.1 Summary

Within this workshop, we focus on the importance and practicality of creating different visualizations.

2.2 Learning Objectives

After completing this workshop, you are expected to be able to:

• Apply quantitative and visual exploratory techniques on data
• Deduce and interpret patterns revealed during exploratory data analysis (EDA)
• Create common statistical graphs using Seaborn.

2.3 Content

Title	Link
0.1: Google Colab Slides	https://inmas-training.github.io/lecture-slides/00a-google-colab.pdf
2.1: Data Visualization Slides	https://inmas-training.github.io/lecture-slides/02a-data-visualization-inmas.pdf
2.2: Data Visualization Notebook

2.4 Resources

Title	Link
Seaborn Cheatsheet	https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf
Seaborn API Reference	https://seaborn.pydata.org/api.html
Python Graph Gallery	https://www.python-graph-gallery.com/
Data-to-Viz Gallery	https://www.data-to-viz.com/