MIS student turning raw data into business intelligence. Building predictive models, crafting dashboards, and finding the story in every dataset.
I'm an MIS student at the University of North Carolina at Charlotte with a passion for uncovering the stories hidden in data. From building predictive models that project six-figure retention value to driving million-dollar sales through performance analytics, I thrive at the intersection of business and technology.
My toolkit spans SQL, Python, SAS, Tableau, and BigQuery — but what sets me apart is translating technical findings into actionable business strategy. Whether it's a churn prediction model or a logistic regression campaign optimizer, I focus on the "so what?" that moves the needle.
Tools and technologies I use to extract, transform, analyze, and visualize data.
From driving measurable growth to managing high-stakes operations.
Academic and personal projects where I applied analytics to solve real problems.
Built a decision tree classification model achieving 80% accuracy on 12,500 records, identifying key churn drivers. Cost-benefit analysis projected $575K in net retention value — a 101% improvement over baseline.
80% accuracy $575K valueBuilt a logistic regression model on 22,000+ observations to predict organic product purchases. Cost-benefit analysis projected $968K in marketing gain — an 81% improvement in ROI over no-model baseline.
22K+ observations 81% ROI liftDesigned and implemented a 15+ table normalized relational database for a real business. Developed ERD diagrams, business rules, and data dictionary. Led a 5-member team across four project phases.
15+ tables 5-member teamOpen to data analyst roles, internships, and collaborative projects.