Dr. VDB Computational Biology course from YouTube

This project examines how retail fuel station performance drives daily liquidity within the treasury function of a Nigerian downstream oil and gas company. Using station-level sales data for October–December 2025, the analysis applies exploratory data analysis, visualisation, hypothesis testing, correlation, and regression to identify the true drivers of cash inflows. The study finds significant revenue concentration risk among a few stations, shows that revenue is primarily volume-driven rather than price-driven, and confirms seasonal growth in diesel (AGO) demand. A regression model highlights PMS throughput as the strongest predictor of revenue and identifies stations that over- or under-perform relative to expectations, providing practical insights for treasury forecasting and funding decisions.

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Análisis de modelos de regresión lineal

This report analyzes global electric vehicle adoption trends using data from the International Energy Agency (IEA). The project includes multiple visualizations exploring EV sales growth, country-wise adoption patterns, powertrain comparisons, market trends, and interactive analysis using R and Plotly.

Final Exam R code