# Data Analysis

> Data analysis workflows and patterns for exploring, transforming, and visualizing data. Use when working with data, creating reports, or when users mention "data analysis", "analyze data", "data exploration", or "reporting".

- Skill: `ihkreddy/data-analysis` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add ihkreddy/data-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ihkreddy/data-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools, Data & Analytics
- License: MIT
- Author: ihkreddy (https://skillmd.com/u/ihkreddy)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ihkreddy/data-analysis

---


# Data Analysis Skill

## When to Use This Skill

Use this skill when:
- Exploring and analyzing datasets
- Creating data reports
- Transforming or cleaning data
- Building visualizations
- Users mention "data analysis", "analyze data", or "reporting"

## Data Analysis Process

### 1. Data Understanding

Before analysis, understand your data:
- What is the source?
- What does each field represent?
- What is the data quality?
- What are the business questions to answer?

### 2. Data Loading

#### C# / .NET
```csharp
// Using CsvHelper
using var reader = new StreamReader("data.csv");
using var csv = new CsvReader(reader, CultureInfo.InvariantCulture);
var records = csv.GetRecords<DataRecord>().ToList();
```

#### TypeScript
```typescript
import { parse } from 'csv-parse/sync';
import { readFileSync } from 'fs';

const data = parse(readFileSync('data.csv'), {
  columns: true,
  skip_empty_lines: true
});
```

### 3. Data Exploration

Key questions to answer:
- How many records?
- What are the column types?
- Are there missing values?
- What are the value distributions?
- Are there outliers?

```csharp
// C# - Basic exploration
Console.WriteLine($"Total records: {data.Count}");
Console.WriteLine($"Columns: {string.Join(", ", data.First().GetType().GetProperties().Select(p => p.Name))}");
Console.WriteLine($"Missing values: {data.Count(r => r.SomeField == null)}");
```

### 4. Data Cleaning

Common cleaning tasks:
- Handle missing values
- Remove duplicates
- Fix data types
- Standardize formats
- Handle outliers

```csharp
// C# - Cleaning examples
var cleaned = data
    .Where(r => r.Date != null)  // Remove nulls
    .DistinctBy(r => r.Id)       // Remove duplicates
    .Select(r => new {
        r.Id,
        Date = DateTime.Parse(r.DateString),
        Amount = decimal.Parse(r.AmountString)
    })
    .ToList();
```

### 5. Data Transformation

#### Aggregation
```csharp
// C# - Group and aggregate
var summary = data
    .GroupBy(r => r.Category)
    .Select(g => new {
        Category = g.Key,
        Count = g.Count(),
        TotalAmount = g.Sum(r => r.Amount),
        AvgAmount = g.Average(r => r.Amount)
    })
    .OrderByDescending(x => x.TotalAmount);
```

#### Pivoting
```csharp
// C# - Pivot data
var pivot = data
    .GroupBy(r => new { r.Year, r.Month })
    .ToDictionary(
        g => $"{g.Key.Year}-{g.Key.Month:D2}",
        g => g.Sum(r => r.Amount)
    );
```

### 6. Statistical Analysis

Common metrics:
- **Mean**: Average value
- **Median**: Middle value
- **Mode**: Most frequent value
- **Std Dev**: Spread of values
- **Percentiles**: Distribution points

```csharp
// C# - Basic statistics
var values = data.Select(r => r.Amount).OrderBy(x => x).ToList();
var mean = values.Average();
var median = values[values.Count / 2];
var stdDev = Math.Sqrt(values.Average(x => Math.Pow(x - mean, 2)));
```

### 7. Reporting

#### Console Output
```csharp
Console.WriteLine("=== Sales Report ===");
Console.WriteLine($"Total Sales: {total:C}");
Console.WriteLine($"Average Order: {average:C}");
Console.WriteLine("\nTop Categories:");
foreach (var cat in topCategories.Take(5))
{
    Console.WriteLine($"  {cat.Name}: {cat.Amount:C}");
}
```

#### Export to CSV
```csharp
using var writer = new StreamWriter("report.csv");
using var csv = new CsvWriter(writer, CultureInfo.InvariantCulture);
csv.WriteRecords(reportData);
```

## Best Practices

1. **Document assumptions** about the data
2. **Validate data quality** before analysis
3. **Use appropriate data types** for accuracy
4. **Handle edge cases** (nulls, zeros, negative values)
5. **Version control** analysis scripts
6. **Create reproducible** workflows
7. **Visualize distributions** to understand data
8. **Test calculations** with known values