Data analyst and aspiring data scientist with a passion for finding patterns, telling stories through data, and building tools that drive real decisions. Proficient in Python, Power BI, and Excel — with hands-on experience delivering dashboards, predictive models, and structured analysis across business and social contexts. Brings strong analytical thinking, clear communication of complex findings, and a growing command of machine learning fundamentals. Motivated by a long-term mission to apply data science toward solving pressing challenges in education, business, and finance across Africa. Equally capable in operational and administrative roles requiring precision, organisation, and cross-functional coordination — demonstrated through leading a full-cycle publication project end-to-end.
Conducted an end-to-end analysis of a 700-transaction financial sales dataset — loading raw Excel data into PostgreSQL, investigating performance across segments, products, countries, and discount bands via SQL, and delivering findings across six Matplotlib charts. Analysis surfaces profitability gaps, discount inefficiencies, and market-level pricing misalignments — each paired with a concrete business recommendation.
Built a 3-page interactive Power BI dashboard tracking enrolments and revenue across 19 courses in 5 categories — simulated after Literate Nigeria's digital skills programme. Enables drill-down by course category and time period, giving programme leads the visibility needed to make data-driven decisions at a glance.
Designed an interactive Excel dashboard analysing H1 2025 sales performance across 4 regions, 6 products, and 6 salespersons — complete with dynamic slicers and visual KPIs. Transforms raw sales data into a clean, navigable view that makes regional and rep-level performance immediately comparable.
Analysed multi-year retail sales data to identify top-performing products, seasonal trends, and regional profit distribution across a structured visual report. Surfaces the patterns that matter most to sales strategy — making it easy to see not just what sold, but why certain periods and regions consistently outperformed others.
Performed end-to-end exploratory analysis on the Titanic dataset — cleaning missing data, visualising survival patterns by gender, age, and passenger class, and engineering features for modelling. Built a Random Forest classifier achieving ~82% accuracy on unseen test data, demonstrating a full pipeline from raw data to deployable prediction.
Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn), SQL
Prompt Engineering
Version Control, Project Publishing
Power BI, MS Excel (PivotTables, dashboards, data modelling), Google Sheets
Microsoft 365, Google Workspace.
Project Coordination · Stakeholder Communication · Attention to detail · Report Writing
Second class honours, upper division.