Spotify Data Visualization in Tableau

In the modern music industry, streaming platforms have transformed how audio content is produced, distributed, and consumed. This presents a comprehensive data visualization project utilizing a rich dataset of 85,000 Spotify tracks released between 2015 and 2025. The dataset encompasses 19 distinct variables, bridging metadata (artists, albums, and record labels), distribution metrics (stream counts, country-specific performance, and popularity indices), and high-dimensional acoustic features (including danceability, energy, tempo, loudness, and instrumentalness). Through exploratory data analysis (EDA) and an array of advanced graphical visualizations—such as correlation matrices, distribution plots, time-series trend lines, and categorical comparisons—this project examines the evolution of 12 distinct musical genres (including Pop, Rock, EDM, and Hip-Hop) over a decade. The resulting visualizations uncover critical patterns in listener preferences, the relationship between audio characteristics and commercial success, and the shifting dynamics of global distribution across independent and major record labels. Ultimately, this documentation serves as a visual framework for translating complex acoustic and behavioral metrics into actionable industry insights.

Using data visualization techniques, this project explores and answers key questions: How have genre trends evolved over the last ten years? What acoustic features make a song more streamable? How do major record labels compare to independent ones across different countries? Through clean, well-structured charts and exploratory plots, this documentation walks through the process of turning raw tabular music data into meaningful visual narratives that capture the pulse of a decade of streaming history.