I have a set of numerical data that needs to be explored, cleaned, and mined for meaningful insights. The end goal is not a flashy dashboard or a predictive model, but a clear, well-documented story about what the numbers reveal. You’ll dive into the data with Python—think pandas for wrangling, NumPy for efficient computation, and matplotlib or seaborn for quick visual sanity checks. After cleaning and exploring, I expect a concise report or Jupyter Notebook that highlights key findings, trends, outliers, and any correlations worth noting. If you spot opportunities for simple statistical testing, include those results as well. Deliverables: • A reproducible Python script or notebook with all code, comments, and visualizations. • A brief summary (markdown or PDF) explaining the main insights and recommended next steps. The dataset will be shared once we start; it’s reasonably sized, so performance shouldn’t be an issue on standard hardware. Accuracy, clear documentation, and readability of code are top priorities. Let me know your timeline and any questions you need answered before we begin.