statistical-data-analysis

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).