Data Analyst | Power BI Developer | Business Intelligent Analyst
I'm a data professional with expertise in transforming complex datasets into actionable insights. My work focuses on creating interactive visualizations and data-driven stories that help organizations make informed decisions.
With experience in Power BI, SQL, and data modeling, I specialize in developing comprehensive reports that reveal hidden patterns and trends in data. This portfolio showcases my projects across various sectors.
This project examines healthcare operational and financial data to identify trends in patient visits, provider performance, and revenue generation (in £). Using Power BI, Power Query, and DAX, the dataset was analyzed to uncover low patient retention, uneven provider workload, and strong reliance on insurance-based revenue. The findings highlight opportunities to improve patient retention, optimize resource allocation, and diversify revenue streams, ultimately enhancing both efficiency and service quality..
This project analyzes the effectiveness of multiple marketing campaigns by measuring return on investment (ROI), conversion rates, and customer engagement. Using Power BI and DAX, campaign data was modeled to compare performance across channels and audience segments. The analysis showed that certain campaigns delivered significantly higher ROI while others underperformed despite high spend. Insights from this project help in reallocating marketing budgets, improving targeting strategies, and maximizing campaign profitability.
This project analyzes personal financial data from 2023–2024 to uncover patterns in income, expenses, and spending behavior. Using structured transaction data, the analysis highlights cash flow trends, identifies major cost drivers, and evaluates overall financial health. The goal is to move beyond raw numbers and answer a simple question: where is the money actually going, and how can it work better?.
A data analysis project focused on evaluating sales performance, customer purchasing patterns, and product demand within a coffee shop business. Using Excel, data was cleaned and transformed to track key metrics such as total revenue, peak sales hours, and top-selling products. The analysis revealed that a small group of products generated the majority of revenue, with clear peak periods during mornings and weekends. These insights support better inventory planning, targeted promotions, and staffing optimization.
