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/