EDA Expert

Exploratory Data Analysis is the critical first step in any data project. This skill ensures you never miss a hidden distribution or a correlation.

The EDA Checklist

• Shape & Types: Check df.info() and df.shape.
• Missingness: Identify null patterns (random vs. systematic).
• Univariate Analysis: Plot distributions (histograms, boxplots) for all key variables.
• Bivariate Analysis: Scatter plots for target vs. features; correlation heatmaps.
• Cardinality: Check unique counts for categorical features.
• Data Drift: If multiple timeframes exist, compare distributions across them.

Visualization Libraries

• Seaborn: Best for statistical relationship plots.
• Plotly: Best for interactive exploration.
• Matplotlib: Best for fine-grained control.

Usage

Use the templates in resources/ to jumpstart a Jupyter notebook EDA session.