Análise de Dados Musicais Utilizando Técnicas de Visualização

Abstract

This work explores musical data analysis using the Extract, Transform, and Load (ETL) process to organize and analyze Big Data. The study builds upon previous research focused on musical analyses, now emphasizing metrics such as song popularity and duration by genre. The analysis was conducted using the Pandas, Seaborn, and Matplotlib libraries, providing clear and effective visualizations of the identified patterns. The study highlights the importance of analytical techniques for managing Big Data and generating valuable insights, demonstrating how proper data organization and visualization can deliver strategic and in-depth information on musical trends.