analysis

Data Analysis Skill

Explore datasets, identify patterns, and generate actionable insights.

Quick Start

/wicked-garden:data:analysis explore data/sales.csv

This will:

1. Profile the data
2. Generate descriptive statistics
3. Identify patterns and trends
4. Suggest visualizations
5. Provide initial insights

After exploration, ask specific questions:

• "What's the trend in sales over time?"
• "Which customer segments are most valuable?"
• "Are there any anomalies in the data?"

Analysis Workflow

1. Profile & Understand

/wicked-garden:data:numbers sales.csv

Key questions:

• What's the grain? (one row per what?)
• What's the date range?
• What are the key metrics?
• Any obvious data quality issues?

2. Explore Patterns

Basic explorations:

• Distributions (histograms, percentiles)
• Categorical breakdowns (frequency tables)
• Time trends (daily/monthly aggregations)
• Correlations (relationship between metrics)

3. Segment Analysis

Common segmentations:

• RFM: Recency, Frequency, Monetary value
• Cohorts: Group by signup/first purchase month
• Geographic: By region, country, city
• Behavioral: By usage patterns

4. Anomaly Detection

Detection methods:

• Statistical outliers (>3 standard deviations)
• Unusual patterns (volume spikes/drops)
• Temporal anomalies (day-of-week deviations)

Insight Generation

Pattern: Observation → Insight → Action

Bad (just observations):

• Average order value is $45
• 60% of customers are in the US

Good (insights with actions):

### Insight: Weekend Shopping Behavior
**Observation**: Sales peak on Friday (+40% vs weekday avg)
**Insight**: Customers shop for weekend needs on Friday.
**Action**: Launch "Friday Flash Sale" campaign (+15% expected)
**Confidence**: HIGH (consistent over 6 months)

Visualization Guidance

Data Type	Comparison	Best Chart
Time series	Trend over time	Line chart
Categorical	Compare values	Bar chart
Distribution	Show spread	Histogram, Box plot
Relationship	Correlation	Scatter plot
Composition	Part of whole	Stacked bar, Pie
Geographic	Location data	Map (choropleth)

Integration

wicked-garden:data:numbers - Primary tool for data queries:

/wicked-garden:data:numbers data.csv

Native tasks - Document insights via TaskCreate with metadata.event_type="task" wicked-garden:mem - Store analysis patterns for reuse

Best Practices

• Start simple: Begin with basic aggregations before complex models
• Validate assumptions: Check data quality, verify calculations
• Tell a story: Lead with the "so what?" and make it actionable
• Be honest about uncertainty: Note limitations, quantify confidence

Common Pitfalls

• Correlation ≠ Causation: Ice cream and drownings both increase in summer
• Simpson's Paradox: Aggregate trend may reverse when segmented
• Survivorship Bias: Include failed cases for balanced view
• Cherry-picking: Report all patterns, not just favorable ones

Reference

For detailed content:

• Output Templates - Report and insight templates