borghei__claude-skills/data-analytics/CLAUDE.md

Data Analytics Skills - Claude Code Guidance

This guide covers the 5 data analytics skills and planned Python automation tools for the domain.

Data Analytics Skills Overview

Available Skills:

1. data-analyst/ - SQL querying, data visualization, statistical analysis, business reporting, and data storytelling
2. data-scientist/ - Hypothesis testing, predictive modeling, experiment design, feature engineering, and causal inference
3. business-intelligence/ - Dashboard design, KPI frameworks, data modeling, self-service analytics, and executive reporting
4. analytics-engineer/ - Data pipeline design, dbt modeling, data warehouse architecture, testing, and documentation
5. ml-ops-engineer/ - Model deployment, ML pipelines, model monitoring, feature stores, and infrastructure automation

Current Status: 5 SKILL.md knowledge bases deployed. Python automation tools planned for next phase.

Skill Selection Guide

Need	Use This Skill
Ad-hoc SQL queries and business reporting	data-analyst
Statistical modeling and experiment design	data-scientist
Dashboard creation and KPI tracking	business-intelligence
Data pipeline and warehouse architecture	analytics-engineer
Production ML deployment and monitoring	ml-ops-engineer

Overlap Guidance:

• data-analyst vs business-intelligence: Use data-analyst for exploratory analysis; use business-intelligence for repeatable dashboards and KPI systems.
• data-scientist vs ml-ops-engineer: Use data-scientist for model development and experimentation; use ml-ops-engineer for production deployment and monitoring.
• analytics-engineer bridges data-analyst and business-intelligence by building the reliable data models both depend on.

Recommended Python Tools (Planned)

SQL and Query Tools

• SQL Generator (data-analyst/scripts/sql_generator.py) - Generate common query patterns (aggregation, window functions, CTEs) from natural-language descriptions
• Query Optimizer (data-analyst/scripts/query_optimizer.py) - Analyze SQL for performance issues, suggest indexes, and rewrite inefficient patterns

Visualization Helpers

• Chart Recommender (business-intelligence/scripts/chart_recommender.py) - Recommend chart types based on data shape, cardinality, and analysis goal
• Dashboard Spec Generator (business-intelligence/scripts/dashboard_spec_generator.py) - Generate dashboard layout specifications from KPI definitions

Data Quality and Testing

• Data Quality Validator (analytics-engineer/scripts/data_quality_validator.py) - Schema validation, null checks, uniqueness constraints, freshness monitoring
• Data Profiler (analytics-engineer/scripts/data_profiler.py) - Automated column profiling with distribution analysis, outlier detection, and completeness scores

ML Operations

• Model Health Monitor (ml-ops-engineer/scripts/model_health_monitor.py) - Track prediction drift, data drift, and model performance degradation
• Experiment Tracker (data-scientist/scripts/experiment_tracker.py) - Log experiment parameters, metrics, and artifacts in structured JSON format

Integration with Engineering Team

The data-analytics domain connects closely with engineering skills:

Data Analytics Skill	Engineering Skill	Integration Pattern
analytics-engineer	senior-data-engineer	Shared pipeline orchestration patterns; analytics-engineer focuses on transformation layer, data-engineer on ingestion and infrastructure
data-scientist	senior-data-scientist	Complementary scopes; data-analytics version emphasizes business context, engineering version emphasizes algorithm implementation
ml-ops-engineer	senior-ml-engineer	ml-ops-engineer handles deployment and monitoring; ml-engineer handles model architecture and training
business-intelligence	senior-fullstack	BI dashboards may embed in fullstack applications; use fullstack scaffolder for custom analytics UIs

Cross-Domain Workflow:

# 1. Profile raw data quality
python analytics-engineer/scripts/data_profiler.py raw_data.csv

# 2. Validate transformation output
python analytics-engineer/scripts/data_quality_validator.py transformed_data.json

# 3. Generate dashboard specification
python business-intelligence/scripts/dashboard_spec_generator.py kpi_definitions.json

# 4. Monitor deployed ML model
python ml-ops-engineer/scripts/model_health_monitor.py model_metrics.json

Quality Standards

All data analytics Python tools must:

• Use standard library only (no pandas, numpy, or external dependencies)
• Support both JSON and human-readable output via --format flag
• Provide clear error messages for malformed input data
• Return appropriate exit codes (0 success, 1 error)
• Process files locally with no API calls or network access
• Include argparse CLI with --help support
• Handle CSV and JSON input formats where applicable

Skill documentation must:

• Include realistic SQL examples with expected output shapes
• Provide industry-specific KPI definitions where relevant
• Reference authoritative methodologies (Kimball, Inmon, dbt best practices)
• Include data governance and privacy considerations

Related Skills

• Engineering: Data engineering, ML engineering -> ../engineering/
• Product Team: User analytics, metrics-driven product decisions -> ../product-team/
• Finance: Financial data analysis, forecasting -> ../finance/
• Business & Growth: Revenue analytics, customer health scoring -> ../business-growth/

Additional Resources

• Main Documentation: ../CLAUDE.md
• Standards Library: ../standards/

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Last Updated: February 2026 Skills Deployed: 5/5 data analytics skills (SKILL.md knowledge bases) Python Tools: Planned for next development phase