I’m sitting on a growing stack of sales data and need it translated into clear, actionable insights. Your task is purely data analysis—no web interface required—so you can focus on writing clean, well-documented Python that lets the numbers speak. The raw files arrive as CSV and the occasional Excel sheet. I’d like you to load them into pandas, tidy up any inconsistencies, explore key metrics (revenue trends, product performance, customer cohorts, regional breakdowns, etc.), and then present the findings through meaningful visualisations using Matplotlib or Seaborn. If you see room for simple forecasting or segmentation with scikit-learn, feel free to include it, provided the results remain easy to interpret for non-technical stakeholders. Deliverables • Reproducible Jupyter Notebook or .py script with inline comments • Exported charts (PNG or SVG) and any dashboards created • Concise summary report (Markdown or PDF) highlighting the main insights and recommended next steps Acceptance criteria • Notebook/script runs end-to-end on a fresh environment with pip-installable requirements • All figures include clear titles, axis labels, and legends • Key metrics (e.g., YoY growth, average order value) are computed correctly and referenced in the report That’s the scope. If you’re comfortable turning raw sales figures into visuals and narratives that decision-makers can use immediately, I’m ready to get started.