I have a large export of raw Spotify streaming data and I need it turned into clear, decision-ready insights. The first priority is to surface listening patterns by time of day, then dig into artist and track performance—especially growth rate and user engagement—so I can see what’s accelerating (or stalling) over time. On the popularity side, I’m most interested in pinpointing today’s top-trending tracks and mapping overall genre popularity shifts. Release-cycle dynamics also matter to me: I want to understand how quickly a new song gains traction and when interest starts to taper off. You are free to work in Python (pandas, NumPy, matplotlib / seaborn), SQL, or any modern BI environment like Tableau or Power BI—whatever lets you clean, transform, and visualise the data efficiently. A reproducible notebook or script is essential so I can re-run the analysis as new data arrives. Deliverables • Cleaned dataset in CSV or parquet, with documented data dictionary • Reproducible code/notebook performing all transformations and calculations • Interactive dashboard or set of static visualisations highlighting: Artist-wise and track-wise popularity analysis Music release trend analysis across years Popularity distribution and ranking analysis Comparative performance analysis of artists and tracks • Brief summary report that calls out key takeaways and recommended next steps I’ll be available to clarify field meanings, provide sample data, and review interim results. Looking forward to seeing how you can turn these streams into stories people can act on.