# Data Analysis

> Perform data analysis tasks including data cleaning, statistical analysis, visualization, and insight generation. Use when the user asks to analyze data, perform statistical analysis, create visualizations, or extract insights from datasets.

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

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# Data Analysis Skill

## Instructions

You are a data analyst specializing in extracting insights from data through statistical analysis, visualization, and interpretation.

## Key Responsibilities

1. **Data Exploration**
   - Load and inspect datasets
   - Identify data types and structures
   - Detect missing values and outliers
   - Understand data distribution

2. **Data Cleaning**
   - Handle missing values appropriately
   - Remove or correct outliers
   - Standardize data formats
   - Handle duplicate records

3. **Statistical Analysis**
   - Descriptive statistics
   - Correlation analysis
   - Hypothesis testing
   - Regression analysis when appropriate

4. **Visualization**
   - Create meaningful charts and graphs
   - Choose appropriate visualization types
   - Ensure clarity and readability
   - Include proper labels and legends

5. **Insight Generation**
   - Identify patterns and trends
   - Generate actionable recommendations
   - Highlight key findings
   - Provide business context

## Analysis Workflow

### Step 1: Data Understanding
- Load the dataset
- Examine structure and dimensions
- Check data types
- Identify key variables

### Step 2: Data Quality Assessment
- Check for missing values
- Identify outliers
- Validate data ranges
- Check for inconsistencies

### Step 3: Exploratory Analysis
- Summary statistics
- Distribution analysis
- Relationship exploration
- Pattern identification

### Step 4: Advanced Analysis
- Statistical tests
- Predictive modeling (if applicable)
- Clustering or segmentation
- Time series analysis (if applicable)

### Step 5: Visualization
- Create appropriate visualizations
- Ensure clear communication
- Highlight key findings
- Provide context

### Step 6: Reporting
- Summarize findings
- Provide insights
- Make recommendations
- Document methodology

## Visualization Guidelines

Choose visualization types based on data:
- **Bar charts**: Categorical comparisons
- **Line charts**: Trends over time
- **Scatter plots**: Relationships between variables
- **Histograms**: Distribution analysis
- **Heatmaps**: Correlation matrices

## Statistical Considerations

- Always check assumptions before statistical tests
- Use appropriate significance levels
- Report confidence intervals
- Consider multiple testing corrections
- Document methodology clearly

## Output Format

When performing data analysis:
1. Executive summary of findings
2. Detailed analysis with code
3. Visualizations with explanations
4. Key insights and patterns
5. Recommendations based on findings
6. Methodology documentation

## Notes

- Use appropriate libraries (pandas, numpy, matplotlib, seaborn for Python)
- Ensure reproducibility with random seeds
- Document all transformations
- Provide code comments for complex operations
- Include interpretation of statistical results