This repository contains in-depth data analysis projects focused on commercial performance, operations, and decision support.
Each project is structured as a complete analytical case study. The emphasis is on understanding what is driving performance, validating those drivers with appropriate metrics and visuals, and translating findings into practical recommendations.
This repository is intended to showcase work aligned with reporting, commercial, or insights analyst roles.
Projects here focus on:
- analysing multi-year business performance
- separating revenue growth from profitability
- identifying structural drivers rather than one-off effects
- presenting insights in a way that supports decision-making
This is not a collection of dashboards or isolated notebooks. Each project follows a clear analytical narrative from context through to recommendations.
Projects in this repository typically cover:
- Sales and revenue performance
- Profitability and margin drivers
- Customer and segment behaviour
- Product and category economics
- Regional and operational performance
- Cost-to-serve and fulfilment efficiency
The focus is on revenue quality and sustainability, not top-line growth alone.
Each project generally includes:
- Business or client background
- Key stakeholders and north star metrics
- Exploratory and diagnostic analysis
- Supporting visual analysis to validate findings
- Actionable recommendations tied to commercial outcomes
This structure is used consistently across projects to ensure clarity and comparability.
- Python for data analysis and modelling
- pandas and numpy for data manipulation
- matplotlib and Power BI for visualisation
- SQL for data extraction and transformation
- Statistical analysis, segmentation, and time-based analysis
Superstore Retail Performance Analysis A multi-year retail analysis examining customer segments, product profitability, discount behaviour, and regional fulfilment performance to identify margin and operational improvement opportunities.
Additional projects will be added over time using the same analytical standards.
I am a data analyst with a focus on turning complex datasets into clear, commercially useful insights. My work prioritises analytical rigour, clean visual design, and practical recommendations that align with how businesses actually operate.