data-analysis

name	data-analysis
description	Analyze data patterns, create visualizations, and generate insights from datasets using statistical methods and data science techniques

Data Analysis Skill

Transform raw data into actionable insights. This skill helps you explore datasets, identify patterns, create visualizations, and generate statistical reports.

Purpose

This skill enables you to:

• Load and explore datasets of various formats (CSV, JSON, Parquet)
• Perform exploratory data analysis (EDA)
• Create statistical summaries and distributions
• Generate data visualizations and charts
• Identify correlations and trends
• Detect anomalies and outliers
• Build predictive models
• Export analysis reports

When to Use

Use this skill when you need to:

• Understand a new dataset
• Find trends and patterns in data
• Create reports with visualizations
• Identify data quality issues
• Compare groups or time periods
• Forecast future values
• Build summary dashboards
• Share insights with stakeholders

Key Features

1. EDA Tools - Automated exploratory analysis
2. Visualizations - Charts, graphs, and heatmaps
3. Statistical Analysis - Descriptive stats, hypothesis testing, correlation
4. Data Cleaning - Handle missing values, outliers, duplicates
5. Time Series - Seasonal decomposition and forecasting
6. Machine Learning - Clustering, classification, regression
7. Reports - Professional analysis documents with code
8. Export Options - Save to HTML, PDF, or interactive dashboards

Instructions

When using this skill:

1. Load Data - Provide dataset path or CSV/JSON content
2. Explore - Generate summary statistics and visualizations
3. Analyze - Identify patterns, trends, and relationships
4. Validate - Check data quality and handle issues
5. Visualize - Create meaningful charts and graphs
6. Model - Build predictive models if needed
7. Report - Document findings and recommendations

Guidelines

• Start Simple: Begin with univariate analysis before multivariate
• Visualize First: Always look at the data before statistics
• Question Assumptions: Don't assume patterns are significant
• Document Methods: Explain your analytical approach
• Consider Context: Interpret results within business context
• Validate Results: Confirm findings with domain experts
• Communicate Clearly: Use simple language and visual metaphors

Examples

Example 1: Customer Purchase Analysis

Dataset: Customer transactions with 10,000 records

Analysis Steps:

1. Load purchase data (date, customer_id, amount, category)
2. Calculate summary statistics (total spend, average order value)
3. Visualize purchase distribution by category
4. Analyze seasonal trends
5. Identify top customers
6. Detect purchase anomalies

Output:

# Customer Analysis Report

## Summary Statistics
- Total Revenue: $2.5M
- Average Order Value: $125
- Number of Customers: 3,450
- Date Range: 2023-01-01 to 2024-01-15

## Key Findings
1. Electronics category drives 42% of revenue
2. Top 20% of customers generate 80% of revenue (Pareto principle)
3. Strong seasonal pattern with peak in Q4
4. Average customer lifetime value: $1,200

## Recommendations
- Focus retention efforts on high-value customers
- Increase inventory for Q4 seasonal demand
- Cross-sell opportunities in Electronics + Home categories

Example 2: Website Traffic Analysis

Dataset: Daily pageviews, bounce rate, session duration

Key Metrics Analyzed:

• Traffic trends over time
• Device type distribution
• Top pages and conversion rates
• User behavior funnels
• Mobile vs. desktop comparison

Visualizations Generated:

• Line chart: Daily pageviews over 12 months
• Bar chart: Traffic by device type
• Funnel chart: User conversion flow
• Heatmap: Day/hour traffic patterns

Analysis Patterns

Scenario	Analysis Type	Key Metrics
Sales Data	Trend & Seasonal	Growth rate, Seasonality index
Customer Data	Segmentation	RFM score, Cohort analysis
Website Data	Behavior	Bounce rate, Conversion funnel
Time Series	Forecasting	Trend, Seasonality, Residuals
A/B Testing	Hypothesis Test	P-value, Effect size

Tools and Libraries

This skill uses:

• pandas - Data manipulation and analysis
• numpy - Numerical computations
• matplotlib/seaborn - Visualizations
• scipy - Statistical tests
• scikit-learn - Machine learning
• plotly - Interactive visualizations

Data Quality Checks

The skill automatically:

• Identifies missing values
• Detects duplicate records
• Flags outliers
• Validates data types
• Checks for referential integrity
• Reports data completeness

Common Analyses

Descriptive Analysis

• Data summaries
• Distribution analysis
• Correlation matrices
• Group comparisons

Predictive Analysis

• Trend forecasting
• Anomaly detection
• Classification models
• Regression models

Diagnostic Analysis

• Root cause analysis
• Cohort analysis
• Segmentation
• Attribution modeling

Related Resources

• Data Analysis Best Practices
• Python Data Science Cheatsheet
• Visualization Gallery
• Sample Datasets
• Analysis Scripts

Support

For data analysis help:

1. Review the examples above
2. Check sample datasets in assets/examples/datasets/
3. Use helper scripts in scripts/
4. Consult the detailed guide in references/