Data Analysis

intermediatedataMin 64K context

Analyzes datasets to extract insights, identify patterns, and generate visualizations. Supports exploratory data analysis (EDA), statistical testing, trend detection, and report generation. Works with CSV, JSON, and database outputs.

data-analysisstatisticsvisualizationpandasedareporting

Use Cases

  • Exploratory data analysis on new datasets
  • Statistical hypothesis testing
  • Generating Python/R analysis scripts
  • Creating data visualization code (matplotlib, plotly, d3)
  • Building automated reporting pipelines

Example Prompt

Analyze this dataset and provide insights.

Data sample (first 10 rows):
```csv
[paste CSV data here]
```

Questions to answer:
1. What are the key distributions and summary statistics?
2. Are there any notable correlations between variables?
3. What outliers or anomalies exist?
4. What trends are visible over time?

Deliverables:
- Summary statistics table
- Python code for full EDA (using pandas + matplotlib)
- Key findings in plain language
- Recommended next steps for deeper analysis

Recommended Models

claude-opus-4-6gpt-5gemini-2-5-pro

Compatible Tools

claude-codecursorgithub-copilotkiroany

Modalities

Input: text, code, file
→
Output: text, code

Related Skills

sql-generationdocumentation-generator

Author

OpenModels Community

@openmodelsrun