I have a full extract of Northern Territory health-record data for the most recent 12-month reporting period, and I need clear, evidence-based insights on how different patient demographic groups compare across key indicators. Scope • Source data: structured EHR export (CSV + SQL dump) containing age, gender, postcode, Indigenous status and visit counts. • Task: run a comparative analysis that highlights statistically significant differences between these demographic segments—e.g., age group vs. hospital stay length, Indigenous status vs. readmission rate. • Time window: data is limited to the last calendar year, so all calculations, charts and commentary should reference only that period. Deliverables 1. Cleaned analysis-ready dataset (code and final table). 2. Reproducible notebook or script (Python/pandas, R tidyverse or similar) showing each step. 3. Executive summary (≤2 pages) plus any supporting visualisations that make the contrasts intuitive for decision-makers. Acceptance Criteria • All demographic fields compared side-by-side with clear p-values or confidence intervals. • Visuals exported in both PNG and editable format. • Notebook runs end-to-end on a fresh environment and includes brief inline documentation. If you have recent experience with healthcare datasets, data privacy safeguards and comparative statistical methods (chi-square, ANOVA, logistic regression, etc.), I’d love to see your approach and timeline.