I have a set of healthcare clinical-trial datasets and I need a thorough exploratory data analysis that spotlights the underlying trends and patterns. The raw tables include patient demographics, treatment arms, time-series lab results and outcome measures; everything is already de-identified and ready to share in CSV format. Your job is to explore, clean where necessary, and surface insights that help me understand enrolment behaviour, response profiles and any unexpected relationships hidden in the variables. I am particularly interested in visual narratives—clear plots, heat maps, time-based charts and concise statistical summaries—that make it easy for non-technical colleagues to grasp the story the data tells. Deliverables • A well-commented Jupyter or RMarkdown notebook with all code (Python pandas, NumPy, seaborn / R dplyr, ggplot2—use whichever stack you prefer). • An executive-level slide deck or PDF summary of key findings, annotated graphs and take-away recommendations. • A brief note on data quality issues discovered and how you handled them, so we can plan any follow-up data collection. Acceptance criteria • Reproducible notebook runs end-to-end on my machine. • Visualisations clearly reveal at least three actionable trends or patterns. • All deliverables supplied in agreed formats within the timeline we confirm. If this aligns with your skill set, let’s discuss the dataset size and your proposed workflow so we can get started quickly.