Data Analyst & Educator
Turning raw data into decisions - across healthcare, aviation, logistics, and operations.
I'm a data analyst with 5+ years of combined experience in analysis, visualisation, and teaching. My work sits at the intersection of technical rigour and practical impact - I don't just build dashboards, I build the analytical case around them.
My background spans Python, SQL, Power BI, and Excel, with a growing focus on data engineering. I've trained over 1,500 learners, facilitated workshops for 200+ global participants, and delivered end-to-end solutions for compliance, operations, and executive reporting.
This portfolio is a living record of projects where I've gone from raw data to a story worth telling.
Power BI · DAX · Healthcare Analytics · Python
An 18-month analysis of 9,216 ER visits. The headline: a 50% admission rate, a systemic wait time problem that no single department is causing, and a satisfaction dataset so incomplete it renders the metric almost meaningless. This project diagnoses all three — and proposes where to actually start fixing them.
📂 View Project → · 📁 Google Drive
Power BI · DAX · Power Query · Aviation Analytics
Across 1.9 million US domestic flights, fewer than 6 in 10 passengers experienced an on-time departure. This project breaks down why - by carrier (a 29-point performance gap between best and worst), by cancellation cause (weather vs. carrier-driven), and by day of week (Monday is structurally the worst day to fly, and the data explains exactly why).
📂 View Project → · 📁 Google Drive
Power BI · Excel · Operations Analytics · Python
5,000 calls. 946 unanswered. 1,354 unresolved. The 3.40/5.0 average satisfaction score sounds manageable until you see that 1 in 5 customers is rating the experience a 1 or 2 — and that the miss rate holds constant regardless of call volume. This analysis finds the structural constraints, benchmarks every agent, and surfaces a counterintuitive finding: speed of answer has virtually zero correlation with customer satisfaction.
Power BI · Logistics Analytics · Python · Process Mapping
A full analytical case study on 2,760 FedEx deliveries from Power BI dashboard through to BPMN process mapping, root cause analysis, and a structured recommendations framework. The core finding: stormy weather drives a 40.2% disruption rate vs. 13.4% in clear conditions, and the current process has no weather-contingency routing at all. A predictable failure, repeatedly absorbed.
| Project | Tools | Description |
|---|---|---|
| Global COVID-19 Analysis | Python, Pandas, Seaborn | Cleaned and visualised global case, mortality, and recovery trajectories across countries |
| Customer Churn Prediction | Scikit-learn, Pandas | Binary classification model with full EDA, feature engineering, and ROC-AUC evaluation |
| National Budget & Security Analytics | Power BI, Excel | Public policy analytics on Nigerian national budget allocation and security spending trends |
| ETL Pipeline - Financial Reporting | Python, SQL, Power Query | End-to-end automated ETL workflow feeding executive Power BI dashboards |
Analytics & Visualisation Power BI · DAX · Python (Pandas, NumPy, Matplotlib, Seaborn) · SQL · Excel (Power Query, Power Pivot)
Data Engineering (in progress) Linux/Bash · Git · ETL Design · Data Modelling · Pipeline Automation
Certifications Google Data Analytics · Microsoft Azure Fundamentals (AZ-900) · Microsoft Azure Data Fundamentals (DP-900)
Open to data analyst and junior data engineer roles · Available for freelance projects