Overview
Exploratory Data Analysis (EDA) is a critical step in any data science project. It helps you understand the data, identify patterns, and detect anomalies.
Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics.
Key Concepts
- Data profiling and summary statistics
- Handling missing values
- Distribution analysis
- Correlation analysis
- Advanced visualization with Seaborn
Practice Exercise
Exercise: Comprehensive EDA
Using a dataset of your choice from Kaggle:
- Perform data profiling to understand the structure
- Visualize distributions of key variables
- Identify and visualize relationships between variables
- Create at least 3 different types of plots (histogram, scatter plot, box plot, etc.)
- Summarize your findings in a few bullet points
Resources
Kaggle Data Visualization
Main resource for today
Seaborn Tutorial
Official Seaborn tutorial
EDA with Python
Towards Data Science article
Pandas Profiling
Automated EDA with pandas-profiling
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