I am a Data Analyst with an engineer’s mindset, I build systems, not just spreadsheets. My work is defined by a commitment to enhance efficiency, ensure smooth workflows, and drive better business decisions.
I thrive on the challenge of taking raw, "messy" data and transforming it into a reliable asset. Whether I’m automating processes with Python or architecting SQL databases, my goal is to deliver clear reports and interactive dashboards that empower stakeholders and executives to act with confidence.
Data Analyst | Engineer | Strategist
Excel • SQL • Power BI • Python
To understand the primary drivers of heart disease, I analyzed a dataset of patient health metrics to identify key physiological risk factors. I leveraged Python for rigorous data cleaning and exploratory analysis (EDA), then transformed the processed data into an interactive Power BI dashboard to visualize critical correlations between attributes like cholesterol, blood pressure, and heart failure risk.
To evaluate the competitive landscape of the soft drink industry in Lagos West, I analyzed field data to track product availability and stock health. I utilized Microsoft Excel and Power Query to clean, standardize, and model the raw audit data, creating an interactive dashboard that visualizes brand dominance and merchandising metrics across various outlet types.
To optimize library operations, I designed a normalized SQL database to handle inventory, borrowing, and returns. I utilized advanced SQL techniques including Window Functions and CTEs to explore member activity trends and analyze branch performance, transforming raw transaction logs into actionable insights on high-demand books and overdue risks.
To bypass the need for manual price monitoring on high-demand items, I built an automated scraper to track real-time pricing trends. I utilized Python (BeautifulSoup) to extract and clean raw HTML data—converting unstructured text into structured price metrics—and implemented a logic loop that logs history to a CSV and triggers instant email alerts via SMTP when prices drop below a specific target.
To understand the volatility of the post-pandemic tech sector, I analyzed a global dataset of workforce reductions (2020–2023) to identify risk profiles across industries and funding stages. I utilized SQL (MySQL) to clean and standardize the raw data—removing duplicates and handling null values—and performed exploratory data analysis (EDA) using Window Functions and Rolling Sums to visualize the momentum of layoffs over time.
To optimize inventory and marketing strategies, I analyzed a retail transaction dataset to uncover purchasing patterns across demographics and timeframes. I utilized SQL (MySQL) to clean the data handling null values and standardizing date formats and performed advanced exploratory analysis using Window Functions and CTEs to identify peak sales shifts, high-value customer segments (age 46–60), and top-performing product categories
To demystify the actual state of the data profession, I analyzed survey responses from over 630 professionals to uncover trends in salaries, skills, and entry barriers. I utilized Power Query to clean and standardize the raw survey data, normalizing job titles and country names, and used Power BI with advanced DAX measures to create an interactive dashboard that visualizes the "break-in" difficulty and the dominance of Python in the industry.