Detail-oriented professional specializing in data analysis and statistical modeling. Expertise in programming languages such as R, Python, SQL, and C++, with a strong focus on hypothesis testing and regression analysis. Proven ability to create impactful visualizations and support decision-making through data-driven insights. Experienced in ETL processes and cloud solutions, committed to optimizing workflows and delivering results in fast-paced environments.
WIC Program Fund Allocation Analysis | University of Hertfordshire | 2024, Business Intelligence: Investigated disparities in monthly fund allocations to postpartum women across US states to assess resource distribution fairness., ETL & Programming: Developed robust R scripts to automate the loading of large datasets, converting strings to date-time formats and pivoting data for longitudinal analysis., Statistical Modelling: Performed Spearman’s Rho non-parametric correlation tests to evaluate the relationship between time and funding trends (ρ=0.6437$)., Advanced Visualisation: Engineered right-skewed histograms with fitted density estimation curves and scatterplots with linear trendlines to identify funding outliers., Collaborative Development: Orchestrated version control via GitHub, managing project commits and merging code updates to ensure transparency and reproducibility.
Google Analytics Certified, AWS Certified.