# Biomedical Data Analysis

> Omics data forge

- Skill: `fridrichmethod/biomedical-data-analysis` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add fridrichmethod/biomedical-data-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fridrichmethod/biomedical-data-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: FridrichMethod (https://skillmd.com/u/fridrichmethod)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fridrichmethod/biomedical-data-analysis

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# 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).


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