Claude Data Analysis Assistant

A modern, intelligent data analysis platform built with Claude Code's sub-agents, slash-commands, and hooks. Transform your data analysis workflow with AI-powered assistance.

🚀 Quick Start

1. Set Up Your Data

Place your dataset in the data_storage/ directory:

cp your_data.csv ./data_storage/

2. Start Analysis

Use intuitive slash commands to analyze your data:

# Basic exploratory analysis
/analyze user_behavior_sample.csv exploratory

# Create visualizations
/visualize user_behavior_sample.csv all

# Generate analysis code
/generate python data-cleaning

# Create comprehensive report
/report user_behavior_sample.csv complete markdown

🎯 Key Features

Intelligent Sub-Agents

• data-explorer: Expert statistical analysis and pattern discovery
• visualization-specialist: Beautiful, insightful charts and graphs
• code-generator: Production-ready analysis code
• report-writer: Comprehensive analysis reports
• quality-assurance: Data validation and quality control
• hypothesis-generator: Research hypothesis and insights

Intuitive Slash Commands

• /analyze [dataset] [type] - Perform data analysis
• /visualize [dataset] [type] - Create visualizations
• /generate [language] [type] - Generate analysis code
• /report [dataset] [format] - Generate reports
• /quality [dataset] [action] - Quality assurance
• /hypothesis [dataset] [domain] - Generate hypotheses

Automated Workflows

• Data Validation: Automatic quality checks on data upload
• Smart Context: Project-aware analysis suggestions
• Reproducible Analysis: Complete documentation and code generation

📊 Usage Examples

User Behavior Analysis

# Complete analysis workflow
/analyze user_behavior.csv exploratory
/visualize user_behavior.csv trends
/quality user_behavior.csv clean
/report user_behavior.csv complete html
/generate python user-segmentation

Sales Data Analysis

# Sales performance analysis
/analyze sales_data.csv statistical
/visualize sales_data.csv trends
/generate sql revenue-analysis
/report sales_data.csv executive pdf

Customer Analytics

# Customer segmentation
/analyze customer_data.csv predictive
/visualize customer_data.csv distribution
/generate r clustering-analysis
/hypothesis customer_data churn-prediction

🛠️ Project Structure

claude-data-analysis/
├── .claude/
│   ├── agents/          # Sub-agent configurations
│   ├── commands/        # Slash command definitions
│   ├── hooks/          # Automation scripts
│   └── settings.json   # Claude Code settings
├── data_storage/       # Your data files
├── visualizations/     # Generated charts
├── generated_code/     # Analysis code
├── analysis_reports/   # Analysis reports
├── examples/          # Example datasets and workflows
└── docs/             # Documentation

🎨 Sample Data

The project includes sample data to get you started:

• user_behavior_sample.csv: Sample user behavior data with user actions, devices, locations, and revenue
• Field descriptions: user_id, session_id, timestamp, action, page_url, device_type, location, revenue

🔧 Configuration

Environment Setup

The project uses Claude Code's configuration system. Key settings:

1. Hooks: Automated validation and context loading
2. Sub-agents: Specialized AI assistants for different tasks
3. Commands: Custom slash commands for common operations

Requirements

• Python 3.8+ for data analysis
• Claude Code with sub-agents enabled
• Data files in CSV, JSON, or Excel format

📚 Getting Started Guide

For New Users

1. Place your data in data_storage/
2. Run exploratory analysis: /analyze your_data.csv exploratory
3. Create visualizations: /visualize your_data.csv all
4. Generate report: /report your_data.csv complete markdown

For Advanced Users

1. Customize agents: Modify .claude/agents/ configurations
2. Create custom commands: Add new commands in .claude/commands/
3. Set up automation: Configure hooks in .claude/settings.json
4. Extend functionality: Add custom analysis scripts

🎯 Analysis Types

Exploratory Analysis

• Data quality assessment
• Summary statistics
• Pattern discovery
• Initial insights

Statistical Analysis

• Hypothesis testing
• Correlation analysis
• Regression analysis
• Confidence intervals

Predictive Analysis

• Feature importance
• Predictive modeling
• Variable relationships
• Model recommendations

Complete Analysis

• All analysis types
• Comprehensive reports
• Visualizations
• Actionable insights

📈 Visualization Types

All Visualizations

• Comprehensive dashboard
• Multiple chart types
• Interactive exploration
• Executive summary

Specific Charts

• Trends: Time series, moving averages
• Distribution: Histograms, box plots, density plots
• Correlation: Heatmaps, scatter plots, correlation matrices
• Comparison: Bar charts, grouped charts, small multiples

🔍 Code Generation

Supported Languages

• Python: Pandas, NumPy, Scikit-learn, Matplotlib
• R: Tidyverse, ggplot2, caret
• SQL: All major dialects
• JavaScript: D3.js, Plotly.js, TensorFlow.js

Analysis Types

• Data cleaning and preprocessing
• Statistical analysis
• Machine learning
• Visualization code
• Custom analysis

📋 Project Status

Current Phase: Week 1.1 - Project Initialization ✅

Completed Features

• Project structure and configuration
• Data Explorer sub-agent
• Visualization Specialist sub-agent
• Core slash commands (/analyze, /visualize, /generate)
• Automation hooks
• Sample data and documentation

Upcoming Features

• Report Writer sub-agent
• Quality Assurance sub-agent
• Hypothesis Generator sub-agent
• Advanced slash commands
• Interactive dashboards
• Integration with external tools

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch
3. Add your improvements
4. Test your changes
5. Submit a pull request

Development Guidelines

• Follow the established code style
• Add comprehensive documentation
• Include unit tests for new features
• Update the README as needed

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

🙏 Acknowledgments

• Built with Claude Code
• Inspired by the DATAGEN project
• Powered by modern data science tools and frameworks

📞 Support

For support and questions:

• Check the documentation in the docs/ directory
• Review the examples in examples/
• Use the /help command for usage assistance

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Start analyzing your data smarter, not harder! 🚀