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---
name: statistical-data-analysis
description: Omics data forge
keywords:
- pandas
- R-tidyverse
- SQL
- visualization
- reproducible
measurable_outcome: Deliver a cleaned dataset + statistical summary + at least one visualization or dashboard spec for each request within 1 working session (≤30 minutes).
license: MIT
metadata:
author: BioSkills Team
version: "1.0.0"
compatibility:
- system: Python 3.9+ / R 4.0+
allowed-tools:
- run_shell_command
- read_file
- python_repl
---
# Biomedical Data Analysis
Run the cross-language data analysis workflows (Python, R, SQL, Tableau/Power BI) described in this module to clean, analyze, and visualize biomedical datasets end-to-end.
## Workflow
1. **Scope request:** Identify analysis_type (`exploratory`, `statistical`, `predictive`, `visualization`) and required language/tooling.
2. **Acquire data:** Load from CSV/Parquet/SQL using pandas, tidyverse, or connectors described in `README.md`.
3. **Process:** Apply wrangling, descriptive stats, modeling, or SQL aggregations as listed in the capability tables.
4. **Visualize:** Choose Matplotlib/Seaborn/Plotly for inline plots or emit Tableau/Power BI specs per need.
5. **Document:** Provide code snippets + outputs, noting package versions and any assumptions.
## Guardrails
- Use reproducible scripts or notebooks—avoid manual spreadsheet edits.
- Keep PHI secure; when touching EHR-level SQL list filters minimizing data exposure.
- Clearly separate exploratory findings from validated statistical conclusions.
## References
- Capability tables, code samples, and parameter definitions live in `README.md` (plus `tutorials/README.md` for step-by-step lessons).
