Biomedical data analysis
Skill FridrichMethod/awesome-skills/skills/biomedical-data-analysis
Omics data forgeFrom its SKILL.md
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SKILL.md
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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
- Scope request: Identify analysis_type (
exploratory,statistical,predictive,visualization) and required language/tooling. - Acquire data: Load from CSV/Parquet/SQL using pandas, tidyverse, or connectors described in
README.md. - Process: Apply wrangling, descriptive stats, modeling, or SQL aggregations as listed in the capability tables.
- Visualize: Choose Matplotlib/Seaborn/Plotly for inline plots or emit Tableau/Power BI specs per need.
- 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(plustutorials/README.mdfor step-by-step lessons).
What ships with it: 3 files
34.3 KB alongside SKILL.md
tutorials/
- README.md5.3 KB
- data_analysis.yaml10.3 KB
- README.md18.7 KB