An Excel spreadsheet containing raw sales figures is ready for a full-cycle makeover. The goal is to transform it into a reliable source of customer-trend insights and wrap the results in clear, interactive reports that anyone on the team can use. The work starts with rigorous data cleaning—Power Query in Excel or a quick Python pass with Pandas, whichever proves faster—followed by a tidy restructuring so every column and row lines up for smooth analysis. Once the data is pristine, I need customer-centric questions answered: How are buying patterns shifting? Which segments are growing or declining? What order frequencies, basket sizes, and seasonality signals can we detect? To surface these answers: • Advanced Excel dashboards—pivot tables, slicers, and linked charts—should update automatically when I drop in fresh data. • SQL scripts (MySQL or PostgreSQL syntax) must replicate the same logic in the database, allowing me to rerun customer-trend queries at will. • A compact Python notebook should automate repetitive tasks, produce supporting visuals with Matplotlib or Seaborn, and export clean datasets. Deliverables expected at hand-off: 1. A fully cleaned and documented dataset. 2. Reusable SQL files covering all customer-trend queries. 3. The automated Excel dashboard, complete with instructions for refresh. 4. The Python analysis notebook and a PDF or PowerPoint summary of key findings. Everything should be packaged so a colleague can reproduce results without guesswork. Turnaround desired: within one week of project start.