I am a data analyst skilled in Python, SQL, Power BI and Excel, with a background in clinical pharmacy that sharpens how I think about complex data. I turn raw, scattered information into clear insights that help businesses make faster and better decisions.
I work across the full analytics pipeline cleaning and transforming data with Python and Pandas, querying databases in MySQL, and building interactive dashboards in Power BI using DAX, Power Query, Star Schema modeling and What-If analysis. In Excel I deliver clean reports using Pivot Tables, VLOOKUP, XLOOKUP and advanced functions.
My clinical training means I ask the right questions, communicate findings precisely, and focus on outputs that are actually useful to the people making decisions.
End-to-end pipeline on the Lagos property market. Scraped raw listings, cleaned thousands of rows with Pandas, structured data in MySQL, and built an executive Power BI dashboard surfacing pricing trends and neighborhood value across the city.
SQL-based ETL and clinical analysis identifying key drivers of 30-day hospital readmissions. Covers diagnosis groups, discharge disposition, average LOS, medication adherence and readmission rates across patient cohorts.
Two-page Power BI dashboard tracking maternal mortality and child health outcomes across Nigeria's 37 states over a decade. Covers U-5 mortality trends, vaccination coverage, skilled birth attendance and geopolitical zone comparisons against WHO targets.
Executive dashboard with What-If scenario modeling, YoY/MoM time intelligence, and AI Decomposition Tree. Identified a 349% YoY revenue spike and showed a 5% cost cut could add $1.2M in net profit.
Power BI dashboard analyzing $37M in sales across global markets from 2015 to 2018. Tracks profit margin, order volume, shipping mode performance and monthly sales trends.
Interactive Excel dashboard tracking net revenue, return rates and category trends across 8 fashion brands. Built with pivot tables, dynamic charts and brand-level filtering.
End-to-end SQL analysis of a large Brazilian e-commerce dataset using CTEs, window functions and aggregate queries to surface customer behavior, delivery performance and revenue trends.
Python pipeline using BeautifulSoup and Requests to scrape live world population data from the web, clean and transform it with Pandas for downstream analysis.
Open to remote data analyst roles. I respond quickly.