CHERYL WEKARE

Aspiring data and finance analyst seeking to apply technical and analytical skills to drive insight and innovation in data-driven businesses.

I am a data driven and detail oriented graduate with a strong foundation in business data analytics and finance. I bring hands-on experience from academic projects and internships, and I am passionate about applying analytics tools to solve real world problems, improve financial decision making, and optimize business performance. I have worked with R, Python, SQL, and STATA to conduct statistical analysis, build predictive models, test hypotheses, and support stakeholder decision making. My recent capstone project involved building an interactive fare-checking dashboard using R Shiny to help commuters identify incorrect TransLink charges and predict future travel spending. I also have experience in real estate analytics, including feasibility modeling for aged care development projects using risk adjusted financial projections.

• Capstone Project – TransLink Fare Checker App
  Built a predictive fare tracking and overcharge detection dashboard using R, R Shiny, and flexdashboard. The app enables users to upload travel data, view visualizations of frequent trips, and receive fare refund guidance and budget forecasts.
• Real Estate Feasibility Assessment – Aged Care Development
  Conducted a financial feasibility analysis for a proposed aged care center using discounted cash flow and scenario modeling. The project involved evaluating risk adjusted returns, operating costs, and long-term occupancy projections to assess viability.
• Targeted Marketing Model for Banks - Developed a linear regression model in R to help banks optimize marketing resource allocation. The model improved targeting and reduced campaign wastage.
• Advanced Data Visualization for Shareholders - Used R to present key financial insights to stakeholders, focusing on interactive visuals, colour theory, and storytelling techniques.
• Machine Learning for Retail Stock Optimization - Implemented simulation models in R and Python to help retail firms reduce overstock and improve supply chain efficiency.
• Deep Learning for Business Efficiency - Built deep learning models to identify performance bottlenecks in finance and healthcare scenarios.
• Business Analytics Tools & UI Design - Explored UI/UX principles in analytics apps, emphasizing ease of use and effective communication through interface design.

Brisbane, QLD | Available on request