Data Analysis

Data Analysis Skill

Analysis Report Template

Executive Summary

• Question: What business question are we answering?
• Key Finding: One-sentence summary of main insight
• Recommendation: Primary action item
• Confidence: High / Medium / Low

Methodology

• Data Sources: Where did data come from?
• Time Period: What timeframe?
• Sample Size: How many observations?
• Analytical Approach: What methods used?
• Assumptions: What assumptions were made?

Findings

• Finding 1: [Description]
  • Evidence: [Supporting data]
  • Confidence: [Statistical confidence]
• Finding 2: [Description]
  • Evidence: [Supporting data]
  • Confidence: [Statistical confidence]

Recommendations

• Priority 1: [Action] - [Expected impact]
• Priority 2: [Action] - [Expected impact]

Caveats

• Limitations: What are the limitations?
• Biases: Potential biases?
• Data Quality: Known data quality issues?
• Next Steps: What additional analysis needed?

SQL Query Patterns

Aggregation Patterns

Daily Metrics:

SELECT 
    DATE(created_at) as date,
    COUNT(*) as events,
    COUNT(DISTINCT user_id) as unique_users
FROM events
WHERE created_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY DATE(created_at)
ORDER BY date DESC;

Cohort Analysis:

WITH cohorts AS (
    SELECT 
        user_id,
        DATE_TRUNC('month', MIN(created_at)) as cohort_month
    FROM users
    GROUP BY user_id
)
SELECT 
    c.cohort_month,
    DATE_TRUNC('month', e.created_at) as event_month,
    COUNT(DISTINCT e.user_id) as active_users
FROM cohorts c
JOIN events e ON c.user_id = e.user_id
GROUP BY c.cohort_month, DATE_TRUNC('month', e.created_at)
ORDER BY c.cohort_month, event_month;

Window Functions

Running Totals:

SELECT 
    date,
    revenue,
    SUM(revenue) OVER (ORDER BY date) as running_total
FROM daily_revenue
ORDER BY date;

Ranking:

SELECT 
    product_id,
    revenue,
    RANK() OVER (ORDER BY revenue DESC) as revenue_rank
FROM product_revenue;