Exploratory Data Analysis with Seaborn

Producing visualizations is an essential first step in data analysis, and this project-based course focuses on using Seaborn to explore and interpret relationships in the Breast Cancer Wisconsin (Diagnostic) Data Set. Through hands-on projects on Rhyme, learners will delve into key concepts of exploratory data analysis (EDA) using visualizations.

Key learning points include producing and customizing various chart types such as histograms, violin plots, box plots, joint plots, pair grids, and heatmaps, as well as applying graphical techniques in EDA. Learners will have access to a pre-configured cloud desktop with Python, Jupyter, and scikit-learn pre-installed, enabling them to focus on practical learning.

• Identify and interpret inherent quantitative relationships in datasets
• Produce and customize various chart types with Seaborn in Python
• Apply graphical techniques in exploratory data analysis (EDA)