Data Analysis Skill

Systematically analyze datasets to extract insights, identify patterns, and create visualizations.

When to Use

• User provides a dataset for analysis
• User asks "analyze this data"
• User wants insights from a CSV, DataFrame, or database table
• User asks about data quality or patterns

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Phase 1: Data Discovery

import pandas as pd

df = pd.read_csv("data.csv")
print(f"Shape: {df.shape}")
print(f"Columns:\n{df.dtypes}")
print(f"First rows:\n{df.head()}")

# Data quality
missing = df.isnull().sum()
duplicates = df.duplicated().sum()

Output: Data Discovery Report (shape, types, quality, initial observations)

Phase 2: Exploratory Analysis

import plotly.express as px

# Distributions, time series, correlations, outliers
for col in numeric_cols:
    fig = px.histogram(df, x=col, title=f"Distribution of {col}")
    fig.show()

corr = df[numeric_cols].corr()
fig = px.imshow(corr, text_auto=True, title="Correlation Matrix")
fig.show()

Phase 3: Deep Analysis

• Segmentation by category
• Comparative analysis (box plots)
• Statistical tests if needed
• Trend decomposition for time series

Phase 4: Insight Generation

For each insight:

1. Finding: What the data shows
2. Evidence: Supporting numbers/charts
3. Implication: What this means for the business
4. Recommendation: What action to take

Phase 5: Report

# Data Analysis Report

## Executive Summary
## Dataset Overview
## Key Insights
## Recommendations
## Appendix (methodology, data quality notes)

---

Branded Charts

CHART_COLORS = ["<BRAND_PRIMARY>", "<BRAND_DARK>", "#6B7280", "#9CA3AF"]

fig.update_layout(
    template="plotly_white",
    font_family="Inter, sans-serif",
    color_discrete_sequence=CHART_COLORS
)

---

Checklist

• Data loaded and validated
• Missing values addressed
• Distributions analyzed
• Trends identified
• Correlations checked
• Outliers investigated
• Insights are actionable
• Visualizations are clear
• Report is well-structured

Cross-Source Join Safety (when analysis involves merging data from multiple sources)

• Join key type compatibility: Document column types of join keys in each source. Flag type mismatches (e.g., INT vs VARCHAR) and document required CAST/conversion.
• Row cardinality: Verify whether join keys are unique per source. If 1:N, document deduplication strategy.
• Sample merge validation: Test-merge on sample data, verify output row count matches expectations.