I have a raw sales dataset covering several regions and product lines, and I need a clear descriptive analysis that tells the story of what has happened so far. Your task is to clean the data, calculate the key metrics (totals, averages, YoY and MoM growth, seasonality, top-performing SKUs, regional splits), and present the findings in an easy-to-digest format. Because this is strictly a descriptive project—no predictive or prescriptive modeling—clarity and accuracy matter more than complex algorithms. A well-structured SQL query, Python (pandas, matplotlib, seaborn) or R (dplyr, ggplot2) workflow is fine, as long as I can rerun it with future data. Please draw on your experience with similar sales data projects when you respond; that will be the deciding factor. Deliverables: • Reproducible script or workbook that ingests the raw sales file, performs cleaning, and outputs summary tables and charts • Concise report or slide deck (PDF/PowerPoint) highlighting trends and insights • Short hand-off walkthrough or documentation so I can update the analysis myself I’m ready to provide a sample of the dataset and any field definitions right away and can answer questions quickly to keep things moving.