Ds Eda Expert

Systematic Exploratory Data Analysis (EDA) patterns to uncover insights and data quality issues early.

jcorpac Updated

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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.

jcorpac/ai-skills-library/tree/main/ds/ds-eda-expert commit 230cab3cc6

Frequently asked questions

npx skillmds@latest add jcorpac/ds-eda-expert