Project 1: Sponsorship ROI Foundations

I established a fundamental understanding of NASCAR sponsorship structures and ROI metrics, equipping myself with the necessary knowledge and infrastructure to analyze sponsorship effectiveness. This foundational work set the stage for subsequent data analysis.

Project 3: Exploratory Data Analysis

I conducted exploratory data analysis on the master dataset, deriving actionable insights through statistical methods and visualizations. This included calculating correlations between race performance and sponsorship visibility, leading to a detailed EDA report with multiple visualizations and

Project 5: Strategy and Presentation

I translated my analytical findings into strategic recommendations, identifying top-performing sponsors and crafting scenario analyses. I compiled a comprehensive report and designed an executive presentation deck, effectively communicating insights to stakeholders.

Project 2: Data Collection & Integration

I collected and integrated a comprehensive dataset that included race performance data, social media exposure metrics from Reddit and YouTube, and news coverage. This analysis-ready dataset formed the backbone for my visibility scoring model.

Project 4: Visibility Scoring Model

I developed a visibility scoring model that ranked sponsors based on their visibility efficiency. This involved selecting variables, designing the scoring formula in Python, calculating visibility scores, and conducting a sensitivity analysis to validate the model's robustness.