I manage a growing set of customer-information tables and need a versatile hand to keep the data clean, fast to query, and easy to explore. First, the MySQL layer: I will share the current schema along with sample data dumps. Your job is to rewrite or create queries that return the same results in less time, add any useful indexes, and document the rationale so I can maintain them later. Once the data is flowing smoothly, I want to pull it into Python. Using pandas and NumPy, you will clean up inconsistent customer fields, perform exploratory analysis, and build a few quick visualisations that highlight churn risk and purchase patterns. A Jupyter notebook with clear, commented cells is the expected deliverable here. I also frequently hand off extracts to non-technical teammates, so each week I need an Excel workbook where the data is already filtered by region and date range, with a pivot table that summarises revenue per customer segment. Finally, there’s a lightweight web dashboard that shows the freshest numbers. I’ll provide the existing HTML/CSS scaffold; you’ll only need to adjust styles, drop in a small JavaScript snippet for interactive sorting, and hook it to a simple REST endpoint I already expose. Deliverables • Optimised MySQL query set with brief documentation • Clean, well-commented Python notebook covering data cleaning, analysis, and basic visualisations • Excel workbook featuring predefined filters and a pivot table ready for weekly refresh • Updated HTML/CSS/JS files connected to the provided API endpoint If you are comfortable jumping between SQL, pandas, and a little front-end glue code, I’d love to get started.