I have a raw dataset that must be transformed into an academically sound exploratory study. The work begins with clarifying research questions and auditing the data, moves through cleaning and statistical exploration, and culminates in an interactive Power BI dashboard that communicates key findings with clarity and rigour. Although Python (Pandas, NumPy, Seaborn) could be used behind the scenes for heavy wrangling if it speeds things up, the final narrative and visuals need to live inside Power BI so the project can be shared easily with instructors and peers. Throughout the process I expect careful documentation—explaining assumptions, recording each transformation step, and citing any external sources—so the entire workflow is fully reproducible and suitable for an academic submission. Strong communication is important; quick check-ins around milestones will keep everything on track and let us adjust questions or visuals before we go too far in the wrong direction. Deliverables • Cleaned, well-commented dataset (CSV or Excel) • Power BI file (.pbix) with interactive visuals, clear labels, and polished styling • Concise written report (Markdown or PDF) that summarises methodology, insights, and limitations • README or notebook containing the full data-prep script if Python is used Acceptance criteria – No missing or duplicate records remain unless justified and flagged – Visuals correspond directly to the stated research questions and reveal at least three substantive insights – All code and steps reproduce identical results on a fresh run – Final package opens without errors on the current public release of Power BI If this aligns with your skill set and timeline, let’s map out the milestones and get started.