I am a Research Scientist and Data Analyst passionate about transforming complex datasets into actionable insights. My expertise spans machine learning, data visualisation, and environmental monitoring, with a proven track record of delivering high-impact analytical solutions. From predictive modelling to sensor development, in battery, I bridge scientific research and data science to drive evidence-based decision-making.
Throughout my academic and professional journey, I have developed projects that include customer segmentation, financial forecasting, supply chain optimisation, and strategic research funding analysis. I thrive on solving real-world problems and empowering organisations with data-driven strategies.
- Data Science & Analysis: Python (Pandas, NumPy, Scikit-learn), SQL (PostgreSQL)
- Data Visualisation: Power BI, Matplotlib, Seaborn, Excel, Tableau
- Machine Learning: Predictive Modelling, Churn Prediction, Regression Analysis
- Business Intelligence: Dashboard Development, KPI Analysis, Strategic Reporting
- Environmental Monitoring: Water Treatment, Pollutant Detection, Electrochemical Sensors
Welcome to my data portfolio! Here, I document a summary of my projects across business analytics and scientific research.
| Project | Tools | Description |
|---|---|---|
| π ODA Research Funding Dashboard (MODARI-UKCDR) | Power BI | Single-page Power BI dashboard analyzing global ODA research funding allocations by region, funder, and theme. |
| π UKRI Research Output Dynamics | SQL, Power BI | Dynamic dashboard tracking research outputs and collaborations funded by UKRI, supporting strategic insights. |
| π° Financial Forecasting & Scenario Planning | SQL, Power BI | Financial scenario modeling platform to forecast revenues, costs, and profitability under varying assumptions. |
| π Supply Chain Anomaly Detection & Optimization | SQL (PostgreSQL), Power BI | Built a near real-time supply chain analytics suite detecting bottlenecks and optimizing logistics operations. |
| ποΈ Customer Segmentation & LTV Prediction | SQL, Python, Power BI | Dynamic customer segmentation and lifetime value prediction engine enabling targeted retention strategies. |
| Project | Tools | Description |
|---|---|---|
| π§ Environmental Pollutant Analysis of LCMS and GCMS Datasets | Python, Pandas, Seaborn, Matplotlib | Advanced data analysis of neonicotinoid pollutant concentrations; created interactive geospatial visualizations. |
| π Neonicotinoid Concentration Analysis in Freshwater Bodies | Python, Monte Carlo Simulation | Developed predictive models to forecast pollutant concentrations and assess future risks. |
| Publication | Description |
|---|---|
| π An Electrochemical Screen-Printed Sensor Based on Gold-Nanoparticle-Decorated Reduced Graphene OxideβCarbon Nanotubes Composites for the Determination of 17-Ξ² Estradiol | Musa, A.M., Kiely, J., Luxton, R., & Honeychurch, K.C. (2023). Biosensors, 13(4), 491. |
| π Graphene-Based Electrodes for Monitoring of Estradiol | Musa, A.M., Kiely, J., Luxton, R., & Honeychurch, K.C. (2023). Chemosensors, 11(6), 337. |
| π Recent Progress in Screen-Printed Electrochemical Sensors and Biosensors for the Detection of Estrogens | Musa, A.M., Kiely, J., Luxton, R., & Honeychurch, K.C. (2021). TrAC Trends in Analytical Chemistry, 139, 116254. |
| π€ Water Silent Hormone Monitoring: A Novel Electrochemical Sensor for On-Site Detection of Estradiol in Water | Musa, A. (2023). Presented at Sensing in Water 2023. |
British Airways, Boston Consulting Group, Cognizant, Commonwealth Bank, Forage | 2024
- Completed real-world data management and analytics simulations.
- Applied predictive modeling in business contexts.
- Enhanced data-driven decision-making capabilities.
GSK Digdata Step Up Career Challenge - Clinical Trial Data Analysis | 2024
- Utilized machine learning models to predict patient treatment response.
- Analyzed clinical trial datasets to improve healthcare insights.
π Feel free to explore my projects and publications. Let's connect and collaborate on data-driven solutions!