Data Analyzer

This skill performs comprehensive data analysis with statistical methods, visualizations, and automated reporting.

When to Use This Skill

Invoke this skill when the user:

• Asks to analyze a dataset
• Wants statistical insights
• Needs data visualization
• Requests pattern detection
• Mentions data analysis, statistics, or data science
• Wants to generate analysis reports

Analysis Workflow

Step 1: Data Understanding

Initial Assessment:

1. Identify data format (CSV, JSON, Excel, etc.)
2. Determine data size and structure
3. Understand business context
4. Clarify analysis objectives

Use the analysis script:

python scripts/analyze.py data.csv --explore

Step 2: Data Quality Check

Validation:

• [ ] Data loads successfully
• [ ] Required columns present
• [ ] Data types appropriate
• [ ] Missing values identified
• [ ] Outliers detected
• [ ] Duplicates checked

Quality Report:

python scripts/analyze.py data.csv --quality-report

Step 3: Statistical Analysis

Perform analysis based on data type and objectives.

For Descriptive Statistics:

• Mean, median, mode
• Standard deviation, variance
• Quartiles and ranges
• Distribution shape

For Correlation Analysis:

• Pearson correlation
• Spearman rank correlation
• Covariance matrix

For Advanced Analysis: See REFERENCE.md for:

• Hypothesis testing procedures
• Regression analysis methods
• Time series analysis
• Clustering algorithms

Step 4: Visualization

Create appropriate visualizations:

Univariate Analysis:

• Histograms for distributions
• Box plots for outliers
• Bar charts for categories

Bivariate Analysis:

• Scatter plots for relationships
• Line charts for trends
• Heatmaps for correlations

Multivariate Analysis:

• Pair plots
• 3D visualizations
• Dimensionality reduction plots

Generate visualizations:

python scripts/analyze.py data.csv --visualize --output-dir ./charts

Step 5: Report Generation

Create analysis report using templates from FORMS.md:

cat FORMS.md  # View available report templates
python scripts/analyze.py data.csv --report executive-summary

Analysis Types

Pattern 1: Exploratory Data Analysis (EDA)

Objective: Understand data characteristics and relationships

Steps:

1. Load and preview data
2. Generate summary statistics
3. Check distributions
4. Identify correlations
5. Detect outliers
6. Document insights

Quick EDA:

python scripts/analyze.py data.csv --eda

Pattern 2: Comparative Analysis

Objective: Compare groups or time periods

Steps:

1. Define groups/periods
2. Calculate group statistics
3. Test for significant differences
4. Visualize comparisons
5. Interpret results

See REFERENCE.md section "Statistical Testing" for test selection.

Pattern 3: Trend Analysis

Objective: Identify patterns over time

Steps:

1. Prepare time series data
2. Check for seasonality
3. Calculate moving averages
4. Fit trend lines
5. Forecast future values

See REFERENCE.md section "Time Series Methods" for details.

Pattern 4: Predictive Modeling

Objective: Build models to predict outcomes

Steps:

1. Feature engineering
2. Train/test split
3. Model selection
4. Training and validation
5. Performance evaluation

See REFERENCE.md section "Machine Learning" for model details.

Data Type Handling

Numerical Data:

• Summary statistics
• Distribution analysis
• Correlation analysis
• Regression modeling

Categorical Data:

• Frequency tables
• Cross-tabulations
• Chi-square tests
• Category encoding

Time Series Data:

• Trend decomposition
• Seasonality detection
• Autocorrelation
• Forecasting

Text Data:

• Frequency analysis
• Sentiment analysis
• Topic modeling
• See REFERENCE.md section "Text Analytics"

Common Issues and Solutions

Issue: Missing Values

• Strategy 1: Remove rows (if <5% missing)
• Strategy 2: Impute with mean/median/mode
• Strategy 3: Use advanced imputation (KNN, MICE)
• See REFERENCE.md section "Missing Data Handling"

Issue: Outliers

• Detection: IQR method, Z-score, isolation forest
• Action: Remove, cap, or transform
• Context: Business rules may define valid outliers

Issue: Imbalanced Data

• Resampling techniques
• Class weights
• Synthetic data generation (SMOTE)

Issue: High Dimensionality

• Feature selection
• PCA or t-SNE
• Domain knowledge filtering

Output Formats

The skill can generate reports in multiple formats:

Executive Summary:

• Key findings (3-5 bullets)
• Critical metrics
• Recommendations
• See FORMS.md template "Executive Summary"

Technical Report:

• Methodology
• Detailed results
• Statistical tests
• Visualizations
• See FORMS.md template "Technical Report"

Dashboard Format:

• Interactive visualizations
• Key metrics at a glance
• Drill-down capability

Generate specific format:

python scripts/analyze.py data.csv --format executive
python scripts/analyze.py data.csv --format technical
python scripts/analyze.py data.csv --format dashboard

Validation Checklist

Before finalizing analysis:

• [ ] Data quality verified
• [ ] Appropriate methods selected
• [ ] Assumptions validated
• [ ] Results interpreted correctly
• [ ] Visualizations clear and labeled
• [ ] Report matches requested format
• [ ] Recommendations actionable

Analysis Scope

Quick Analysis (5-10 min):

• Basic statistics
• Simple visualizations
• Key findings only

Standard Analysis (20-40 min):

• Comprehensive statistics
• Multiple visualizations
• Correlation analysis
• Formatted report

Deep Analysis (1-2 hours):

• Advanced modeling
• Hypothesis testing
• Multiple methodologies
• Executive + technical reports

Ask user for preferred scope if unclear.

Example Analysis

Input: sales_data.csv with columns: date, product, region, quantity, revenue

Output:

Key Findings

1. Revenue increased 23% year-over-year
2. Product A accounts for 45% of total revenue
3. Western region shows strongest growth (31%)
4. Seasonal peak in Q4 (38% of annual sales)

Statistical Summary

• Mean daily revenue: $12,450
• Median daily revenue: $11,200
• Standard deviation: $3,890
• 95% of days: $5,000 - $20,000

Visualizations Generated

• Revenue trend line (2023-2024)
• Product revenue pie chart
• Regional comparison bar chart
• Seasonal pattern heatmap

Recommendations

1. Increase inventory for Product A in Q4
2. Investigate Western region success factors
3. Plan marketing campaigns for Q2-Q3 (slower periods)

Advanced Features

For complex scenarios, this skill integrates with:

REFERENCE.md sections:

• Statistical Methods Library
• Machine Learning Algorithms
• Time Series Techniques
• Text Analytics Methods

FORMS.md templates:

• Executive Summary Template
• Technical Report Template
• Dashboard Layout Template

Scripts:

• scripts/analyze.py - Main analysis engine
• scripts/visualize.py - Visualization generator
• scripts/report.py - Report formatter

Getting Started

Simple analysis:

python scripts/analyze.py your_data.csv

With options:

python scripts/analyze.py your_data.csv \
  --explore \
  --visualize \
  --report executive \
  --output-dir ./results

Help:

python scripts/analyze.py --help

For detailed methodology and advanced techniques, see REFERENCE.md. For report templates and output examples, see FORMS.md.