Data Analysis

data-analysis

Use this skill when asked to explore, analyse, or visualise any dataset, CSV, table, or collection of numbers.

Analysis Workflow

Always follow this sequence:

1. Inventory - How many rows? How many columns? What are the column names and types?
2. Quality check - Missing values, duplicates, outliers, type mismatches.
3. Descriptive stats - Mean, median, std, min, max, percentiles for numeric columns.
4. Distribution - Are numeric columns skewed? Are categorical columns imbalanced?
5. Relationships - Correlations, group comparisons, time trends if a date column exists.
6. Key findings - The 3-5 most actionable insights from the above.

Never skip steps. Never jump to "Key findings" without the groundwork.

Output Format

## Dataset Overview
Rows, columns, source, date range if applicable.

## Data Quality
Missing values per column, duplicates found, anomalies flagged.

## Descriptive Statistics
Table of numeric column stats. Categorical column value counts (top 5).

## Key Patterns
Bullet points - one insight per bullet, quantified.
Bad:  "Sales seem higher in Q4"
Good: "Q4 sales average 34% higher than Q1-Q3 combined (mean: $2.1M vs $1.57M)"

## Recommendations
What to investigate further, or what action the data supports.

Rules

• Every insight must include a number. "Higher" without a figure is not an insight.
• Flag data quality issues before drawing conclusions - dirty data produces wrong insights.
• Distinguish correlation from causation explicitly when it matters.
• If asked to plot or chart, use the scripts in scripts/ - do not write raw matplotlib from scratch.
• If a column looks like a date but is typed as a string, note it and parse it before time-series analysis.

Scripts

Pre-built analysis scripts live in scripts/. Read them before writing any analysis code.

Usage: copy the relevant script, adapt the INPUT_FILE and column name variables at the top, then run.