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;

Period-over-Period Comparison:

SELECT 
    date,
    revenue,
    LAG(revenue, 7) OVER (ORDER BY date) as revenue_7d_ago,
    revenue - LAG(revenue, 7) OVER (ORDER BY date) as change_7d
FROM daily_revenue
ORDER BY date;

Common Table Expressions (CTEs)

Multi-Step Analysis:

WITH filtered_data AS (
    SELECT *
    FROM events
    WHERE created_at >= CURRENT_DATE - INTERVAL '30 days'
        AND status = 'completed'
),
aggregated AS (
    SELECT 
        DATE(created_at) as date,
        COUNT(*) as count
    FROM filtered_data
    GROUP BY DATE(created_at)
)
SELECT 
    date,
    count,
    AVG(count) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as rolling_7d_avg
FROM aggregated
ORDER BY date;

Statistical Test Selection Guide

Comparing Means

Two Groups (Independent):

• Test: Two-sample t-test
• Assumptions: Normal distribution, equal variances
• When to use: Comparing means between two groups (e.g., control vs treatment)

Two Groups (Paired):

• Test: Paired t-test
• Assumptions: Normal distribution of differences
• When to use: Same subjects measured twice (e.g., before/after)

Multiple Groups:

• Test: ANOVA (Analysis of Variance)
• Assumptions: Normal distribution, equal variances, independence
• When to use: Comparing means across 3+ groups

Categorical Data

Two Categories:

• Test: Chi-square test
• Assumptions: Expected frequencies > 5
• When to use: Testing independence between two categorical variables

Multiple Categories:

• Test: Chi-square test of independence
• Assumptions: Expected frequencies > 5
• When to use: Testing relationships between categorical variables

Relationships

Linear Relationship:

• Test: Linear regression
• Assumptions: Linearity, independence, homoscedasticity, normality of residuals
• When to use: Predicting continuous outcome from continuous predictor

Multiple Predictors:

• Test: Multiple regression
• Assumptions: Same as linear regression
• When to use: Multiple predictors for one outcome

Non-Linear Relationship:

• Test: Non-linear regression or transformation
• When to use: Relationship is not linear

Non-Parametric Alternatives

When assumptions fail:

• Mann-Whitney U: Alternative to two-sample t-test
• Wilcoxon signed-rank: Alternative to paired t-test
• Kruskal-Wallis: Alternative to ANOVA

Data Quality Checklist

Completeness

• Check for NULL values
• Identify missing time periods
• Verify expected record counts
• Check for gaps in sequences

Accuracy

• Validate data ranges (min/max)
• Check for impossible values
• Verify calculations
• Cross-reference with source systems

Consistency

• Check for duplicate records
• Verify referential integrity
• Validate data types
• Check for schema drift

Freshness

• Verify last update timestamp
• Check pipeline execution logs
• Validate expected refresh cadence
• Identify stale data

Outliers

• Identify statistical outliers (IQR method)
• Check for data entry errors
• Validate extreme values
• Document outlier handling

A/B Test Analysis Template

Hypothesis

• Null Hypothesis: [H₀]
• Alternative Hypothesis: [H₁]
• Success Metric: [Primary metric]
• Minimum Detectable Effect: [MDE]

Test Design

• Sample Size: [Calculated sample size]
• Duration: [Test duration]
• Traffic Split: [A/B split ratio]
• Randomization: [Method used]

Results

Primary Metric:

• Control: [Mean, CI]
• Treatment: [Mean, CI]
• Difference: [Absolute, Relative %]
• P-value: [Statistical significance]
• Confidence Interval: [95% CI]

Secondary Metrics:

• [Metric Name]: [Results]

Statistical Analysis

• Test Used: [t-test / chi-square / etc.]
• Power Analysis: [Post-hoc power]
• Multiple Comparisons: [Correction applied?]

Interpretation

• Significant?: Yes / No
• Practical Significance: [Is effect size meaningful?]
• Confidence Level: [How confident?]

Recommendations

• Action: [Launch / Don't launch / Continue test]
• Reasoning: [Why?]
• Next Steps: [What to do next]

Caveats

• Limitations: [What limits interpretation?]
• Biases: [Potential biases?]
• External Factors: [Any confounding factors?]