# Ds Eda Expert

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

- Skill: `jcorpac/ds-eda-expert` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add jcorpac/ds-eda-expert`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jcorpac/ds-eda-expert/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: jcorpac (https://skillmd.com/u/jcorpac)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jcorpac/ds-eda-expert

---


# 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.