Ai Data Analyst

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name: ai-data-analyst description: Performs comprehensive data analysis, visualization, and statistical modeling using Python. Use when analyzing datasets, performing statistical tests, creating visualizations, doing exploratory data analysis, or generating publication-quality analytical reports.

<when_to_use> Use this skill when you need to:

• Analyze datasets to understand patterns, trends, or relationships
• Perform statistical tests or build predictive models
• Create data visualizations (charts, graphs, dashboards) to communicate findings
• Do exploratory data analysis (EDA) to understand data structure and quality
• Clean, transform, or merge datasets for analysis
• Generate reproducible analysis with documented methodology and code </when_to_use>

<key_capabilities> Unlike point-solution data analysis tools:

• Full Python ecosystem: Access to pandas, numpy, scikit-learn, statsmodels, matplotlib, seaborn, plotly, and more
• Runs locally: Your data stays on your machine; no uploads to third-party services
• Reproducible: All analysis is code-based and version controllable
• Customizable: Extend with any Python library or custom analysis logic
• Publication-quality output: Generate professional charts and reports
• Statistical rigor: Access to comprehensive statistical and ML libraries </key_capabilities>

<required_inputs>

• Data sources: CSV files, Excel files, JSON, Parquet, or database connections
• Analysis goals: Questions to answer or hypotheses to test
• Variables of interest: Specific columns, metrics, or dimensions to focus on
• Output preferences: Chart types, report format, statistical tests needed
• Context: Business domain, data dictionary, or known data quality issues </required_inputs>

<out_of_scope>

• Real-time streaming data analysis (use appropriate streaming tools)
• Extremely large datasets requiring distributed computing (use Spark/Dask instead)
• Production ML model deployment (use ML ops tools and infrastructure)
• Live dashboarding (use BI tools like Tableau/Looker for operational dashboards) </out_of_scope>

2. Data Cleaning and Transformation

• Handle missing values (impute, drop, or flag)
• Address outliers if needed (cap, transform, or document)
• Create derived variables if needed
• Normalize or scale variables for modeling
• Split data if doing train/test analysis

3. Analysis Execution

• Choose appropriate analytical methods
• Check statistical assumptions
• Execute analysis with proper parameters
• Calculate confidence intervals and effect sizes
• Perform sensitivity analyses if appropriate

4. Visualization

• Create exploratory visualizations
• Generate publication-quality final charts
• Ensure all charts have clear labels and titles
• Use appropriate color schemes and styling
• Save in high-resolution formats

5. Reporting

• Write clear summary of methods used
• Present key findings with supporting evidence
• Explain practical significance of results
• Document limitations and assumptions
• Provide actionable recommendations

6. Reproducibility

• Test that script runs from clean environment
• Document all dependencies
• Add comments explaining non-obvious code
• Include instructions for running analysis

Code Structure

• Write self-contained scripts that can be re-run by others
• Use clear variable names and add comments for complex logic
• Separate concerns: data loading, cleaning, analysis, visualization
• Save intermediate results to files when analysis is multi-stage

Data Handling

• Never modify source data files – work on copies or in-memory dataframes
• Document data transformations clearly in code comments
• Handle missing values explicitly and document approach
• Validate data quality before analysis (check for nulls, outliers, duplicates)

Visualization Best Practices

• Choose appropriate chart types for the data and question
• Use clear labels, titles, and legends on all charts
• Apply appropriate color schemes (colorblind-friendly when possible)
• Include sample sizes and confidence intervals where relevant
• Save visualizations in high-resolution formats (PNG 300 DPI, SVG for vector graphics)

Statistical Analysis

• State assumptions for statistical tests clearly
• Check assumptions before applying tests (normality, homoscedasticity, etc.)
• Report effect sizes not just p-values
• Use appropriate corrections for multiple comparisons
• Explain practical significance in addition to statistical significance

<required_artifacts>

• Analysis script(s): Well-documented Python code performing the analysis
• Visualizations: Charts saved as high-quality image files (PNG/SVG)
• Analysis report: Markdown or text document summarizing:
  • Research question and methodology
  • Data description and quality assessment
  • Key findings with supporting statistics
  • Visualizations with interpretations
  • Limitations and caveats
  • Recommendations or next steps
• Requirements file: requirements.txt with all dependencies
• Sample data (if appropriate and non-sensitive): Small sample for reproducibility </required_artifacts>

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install dependencies
pip install -r requirements.txt

# Run analysis script
python analysis.py

# Check outputs generated
ls -lh outputs/

<success_criteria> The skill is complete when:

• Analysis script runs without errors from clean environment
• All required visualizations are generated in high quality
• Report clearly explains methodology, findings, and limitations
• Results are interpretable and actionable
• Code is well-documented and reproducible </success_criteria>

<safety_and_escalation>

• Data privacy: Never analyze or share data containing PII without proper authorization
• Statistical validity: If sample sizes are too small for reliable inference, call this out explicitly
• Causal claims: Avoid implying causation from correlational analysis; be explicit about limitations
• Model limitations: Document when models may not generalize or when predictions should not be trusted
• Data quality: If data quality issues could materially affect conclusions, flag this prominently </safety_and_escalation>