Data Analyst Skill 📊

Turn your AI agent into a data analysis powerhouse.

Query databases, analyze spreadsheets, create visualizations, and generate insights that drive decisions.

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What This Skill Does

✅ SQL Queries — Write and execute queries against databases

✅ Spreadsheet Analysis — Process CSV, Excel, Google Sheets data

✅ Data Visualization — Create charts, graphs, and dashboards

✅ Report Generation — Automated reports with insights

✅ Data Cleaning — Handle missing data, outliers, formatting

✅ Statistical Analysis — Descriptive stats, trends, correlations

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Quick Start

1. Configure your data sources in TOOLS.md:

### Data Sources
- Primary DB: [Connection string or description]
- Spreadsheets: [Google Sheets URL / local path]
- Data warehouse: [BigQuery/Snowflake/etc.]

1. Set up your workspace:

./scripts/data-init.sh

1. Start analyzing!

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SQL Query Patterns

Common Query Templates

Basic Data Exploration

-- Row count
SELECT COUNT(*) FROM table_name;

-- Sample data
SELECT * FROM table_name LIMIT 10;

-- Column statistics
SELECT 
    column_name,
    COUNT(*) as count,
    COUNT(DISTINCT column_name) as unique_values,
    MIN(column_name) as min_val,
    MAX(column_name) as max_val
FROM table_name
GROUP BY column_name;

Time-Based Analysis

-- Daily aggregation
SELECT 
    DATE(created_at) as date,
    COUNT(*) as daily_count,
    SUM(amount) as daily_total
FROM transactions
GROUP BY DATE(created_at)
ORDER BY date DESC;

-- Month-over-month comparison
SELECT 
    DATE_TRUNC('month', created_at) as month,
    COUNT(*) as count,
    LAG(COUNT(*)) OVER (ORDER BY DATE_TRUNC('month', created_at)) as prev_month,
    (COUNT(*) - LAG(COUNT(*)) OVER (ORDER BY DATE_TRUNC('month', created_at))) / 
        NULLIF(LAG(COUNT(*)) OVER (ORDER BY DATE_TRUNC('month', created_at)), 0) * 100 as growth_pct
FROM transactions
GROUP BY DATE_TRUNC('month', created_at)
ORDER BY month;

Cohort Analysis

-- User cohort by signup month
SELECT 
    DATE_TRUNC('month', u.created_at) as cohort_month,
    DATE_TRUNC('month', o.created_at) as activity_month,
    COUNT(DISTINCT u.id) as users
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
GROUP BY cohort_month, activity_month
ORDER BY cohort_month, activity_month;

Funnel Analysis

-- Conversion funnel
WITH funnel AS (
    SELECT
        COUNT(DISTINCT CASE WHEN event = 'page_view' THEN user_id END) as views,
        COUNT(DISTINCT CASE WHEN event = 'signup' THEN user_id END) as signups,
        COUNT(DISTINCT CASE WHEN event = 'purchase' THEN user_id END) as purchases
    FROM events
    WHERE date >= CURRENT_DATE - INTERVAL '30 days'
)
SELECT 
    views,
    signups,
    ROUND(signups * 100.0 / NULLIF(views, 0), 2) as signup_rate,
    purchases,
    ROUND(purchases * 100.0 / NULLIF(signups, 0), 2) as purchase_rate
FROM funnel;

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Data Cleaning

Common Data Quality Issues

| Issue | Detection | Solution |

|-------|-----------|----------|

| Missing values | IS NULL or empty string | Impute, drop, or flag |

| Duplicates | GROUP BY with HAVING COUNT(*) > 1 | Deduplicate with rules |

| Outliers | Z-score > 3 or IQR method | Investigate, cap, or exclude |

| Inconsistent formats | Sample and pattern match | Standardize with transforms |

| Invalid values | Range checks, referential integrity | Validate and correct |

Data Cleaning SQL Patterns

-- Find duplicates
SELECT email, COUNT(*)
FROM users
GROUP BY email
HAVING COUNT(*) > 1;

-- Find nulls
SELECT 
    COUNT(*) as total,
    SUM(CASE WHEN email IS NULL THEN 1 ELSE 0 END) as null_emails,
    SUM(CASE WHEN name IS NULL THEN 1 ELSE 0 END) as null_names
FROM users;

-- Standardize text
UPDATE products
SET category = LOWER(TRIM(category));

-- Remove outliers (IQR method)
WITH stats AS (
    SELECT 
        PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY value) as q1,
        PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY value) as q3
    FROM data
)
SELECT * FROM data, stats
WHERE value BETWEEN q1 - 1.5*(q3-q1) AND q3 + 1.5*(q3-q1);

Data Cleaning Checklist

# Data Quality Audit: [Dataset]

## Row-Level Checks
- [ ] Total row count: [X]
- [ ] Duplicate rows: [X]
- [ ] Rows with any null: [X]

## Column-Level Checks
| Column | Type | Nulls | Unique | Min | Max | Issues |
|--------|------|-------|--------|-----|-----|--------|
| [col] | [type] | [n] | [n] | [v] | [v] | [notes] |

## Data Lineage
- Source: [Where data came from]
- Last updated: [Date]
- Known issues: [List]

## Cleaning Actions Taken
1. [Action and reason]
2. [Action and reason]

---

Spreadsheet Analysis

CSV/Excel Processing with Python

import pandas as pd

# Load data
df = pd.read_csv('data.csv')  # or pd.read_excel('data.xlsx')

# Basic exploration
print(df.shape)  # (rows, columns)
print(df.info())  # Column types and nulls
print(df.describe())  # Numeric statistics

# Data cleaning
df = df.drop_duplicates()
df['date'] = pd.to_datetime(df['date'])
df['amount'] = df['amount'].fillna(0)

# Analysis
summary = df.groupby('category').agg({
    'amount': ['sum', 'mean', 'count'],
    'quantity': 'sum'
}).round(2)

# Export
summary.to_csv('analysis_output.csv')

Common Pandas Operations

# Filtering
filtered = df[df['status'] == 'active']
filtered = df[df['amount'] > 1000]
filtered = df[df['date'].between('2024-01-01', '2024-12-31')]

# Aggregation
by_category = df.groupby('category')['amount'].sum()
pivot = df.pivot_table(values='amount', index='month', columns='category', aggfunc='sum')

# Window functions
df['running_total'] = df['amount'].cumsum()
df['pct_change'] = df['amount'].pct_change()
df['rolling_avg'] = df['amount'].rolling(window=7).mean()

# Merging
merged = pd.merge(df1, df2, on='id', how='left')

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Data Visualization

Chart Selection Guide

| Data Type | Best Chart | Use When |

|-----------|------------|----------|

| Trend over time | Line chart | Showing patterns/changes over time |

| Category comparison | Bar chart | Comparing discrete categories |

| Part of whole | Pie/Donut | Showing proportions (≤5 categories) |

| Distribution | Histogram | Understanding data spread |

| Correlation | Scatter plot | Relationship between two variables |

| Many categories | Horizontal bar | Ranking or comparing many items |

| Geographic | Map | Location-based data |

Python Visualization with Matplotlib/Seaborn

import matplotlib.pyplot as plt
import seaborn as sns

# Set style
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")

# Line chart (trends)
plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['value'], marker='o')
plt.title('Trend Over Time')
plt.xlabel('Date')
plt.ylabel('Value')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('trend.png', dpi=150)

# Bar chart (comparisons)
plt.figure(figsize=(10, 6))
sns.barplot(data=df, x='category', y='amount')
plt.title('Amount by Category')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('comparison.png', dpi=150)

# Heatmap (correlations)
plt.figure(figsize=(10, 8))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Matrix')
plt.tight_layout()
plt.savefig('correlation.png', dpi=150)

ASCII Charts (Quick Terminal Visualization)

When you can't generate images, use ASCII:

Revenue by Month (in $K)
========================
Jan: ████████████████ 160
Feb: ██████████████████ 180
Mar: ████████████████████████ 240
Apr: ██████████████████████ 220
May: ██████████████████████████ 260
Jun: ████████████████████████████ 280

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Report Generation

Standard Report Template

# [Report Name]
**Period:** [Date range]
**Generated:** [Date]
**Author:** [Agent/Human]

## Executive Summary
[2-3 sentences with key findings]

## Key Metrics

| Metric | Current | Previous | Change |
|--------|---------|----------|--------|
| [Metric] | [Value] | [Value] | [+/-X%] |

## Detailed Analysis

### [Section 1]
[Analysis with supporting data]

### [Section 2]
[Analysis with supporting data]

## Visualizations
[Insert charts]

## Insights
1. **[Insight]**: [Supporting evidence]
2. **[Insight]**: [Supporting evidence]

## Recommendations
1. [Actionable recommendation]
2. [Actionable recommendation]

## Methodology
- Data source: [Source]
- Date range: [Range]
- Filters applied: [Filters]
- Known limitations: [Limitations]

## Appendix
[Supporting data tables]

Automated Report Script

#!/bin/bash
# generate-report.sh

# Pull latest data
python scripts/extract_data.py --output data/latest.csv

# Run analysis
python scripts/analyze.py --input data/latest.csv --output reports/

# Generate report
python scripts/format_report.py --template weekly --output reports/weekly-$(date +%Y-%m-%d).md

echo "Report generated: reports/weekly-$(date +%Y-%m-%d).md"

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Statistical Analysis

Descriptive Statistics

| Statistic | What It Tells You | Use Case |

|-----------|-------------------|----------|

| Mean | Average value | Central tendency |

| Median | Middle value | Robust to outliers |

| Mode | Most common | Categorical data |

| Std Dev | Spread around mean | Variability |

| Min/Max | Range | Data boundaries |

| Percentiles | Distribution shape | Benchmarking |

Quick Stats with Python

# Full descriptive statistics
stats = df['amount'].describe()
print(stats)

# Additional stats
print(f"Median: {df['amount'].median()}")
print(f"Mode: {df['amount'].mode()[0]}")
print(f"Skewness: {df['amount'].skew()}")
print(f"Kurtosis: {df['amount'].kurtosis()}")

# Correlation
correlation = df['sales'].corr(df['marketing_spend'])
print(f"Correlation: {correlation:.3f}")

Statistical Tests Quick Reference

| Test | Use Case | Python |

|------|----------|--------|

| T-test | Compare two means | scipy.stats.ttest_ind(a, b) |

| Chi-square | Categorical independence | scipy.stats.chi2_contingency(table) |

| ANOVA | Compare 3+ means | scipy.stats.f_oneway(a, b, c) |

| Pearson | Linear correlation | scipy.stats.pearsonr(x, y) |

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Analysis Workflow

Standard Analysis Process

1. Define the Question

• What are we trying to answer?
• What decisions will this inform?

1. Understand the Data

• What data is available?
• What's the structure and quality?

1. Clean and Prepare

• Handle missing values
• Fix data types
• Remove duplicates

1. Explore

• Descriptive statistics
• Initial visualizations
• Identify patterns

1. Analyze

• Deep dive into findings
• Statistical tests if needed
• Validate hypotheses

1. Communicate

• Clear visualizations
• Actionable insights
• Recommendations

Analysis Request Template

# Analysis Request

## Question
[What are we trying to answer?]

## Context
[Why does this matter? What decision will it inform?]

## Data Available
- [Dataset 1]: [Description]
- [Dataset 2]: [Description]

## Expected Output
- [Deliverable 1]
- [Deliverable 2]

## Timeline
[When is this needed?]

## Notes
[Any constraints or considerations]

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Scripts

data-init.sh

Initialize your data analysis workspace.

query.sh

Quick SQL query execution.

# Run query from file
./scripts/query.sh --file queries/daily-report.sql

# Run inline query
./scripts/query.sh "SELECT COUNT(*) FROM users"

# Save output to file
./scripts/query.sh --file queries/export.sql --output data/export.csv

analyze.py

Python analysis toolkit.

# Basic analysis
python scripts/analyze.py --input data/sales.csv

# With specific analysis type
python scripts/analyze.py --input data/sales.csv --type cohort

# Generate report
python scripts/analyze.py --input data/sales.csv --report weekly

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Integration Tips

With Other Skills

| Skill | Integration |

|-------|-------------|

| Marketing | Analyze campaign performance, content metrics |

| Sales | Pipeline analytics, conversion analysis |

| Business Dev | Market research data, competitor analysis |

Common Data Sources

• Databases: PostgreSQL, MySQL, SQLite
• Warehouses: BigQuery, Snowflake, Redshift
• Spreadsheets: Google Sheets, Excel, CSV
• APIs: REST endpoints, GraphQL
• Files: JSON, Parquet, XML

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Best Practices

1. Start with the question — Know what you're trying to answer
2. Validate your data — Garbage in = garbage out
3. Document everything — Queries, assumptions, decisions
4. Visualize appropriately — Right chart for right data
5. Show your work — Methodology matters
6. Lead with insights — Not just data dumps
7. Make it actionable — "So what?" → "Now what?"
8. Version your queries — Track changes over time

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Common Mistakes

❌ Confirmation bias — Looking for data to support a conclusion

❌ Correlation ≠ causation — Be careful with claims

❌ Cherry-picking — Using only favorable data

❌ Ignoring outliers — Investigate before removing

❌ Over-complicating — Simple analysis often wins

❌ No context — Numbers without comparison are meaningless

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License

License: MIT — use freely, modify, distribute.

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"The goal is to turn data into information, and information into insight." — Carly Fiorina