Step into the world of data-driven music exploration with this project
where I delve into Spotify's top songs of 2023.
(Skills used: SQL and Tableau)
Primary Questions
Firstly, the focus was on identifying what were the top songs and artists, aiming to uncover the most popular tracks and the artists behind them. Secondly, I wanted a better understanding of the distribution of musical keys among these songs, exploring whether certain keys were more prevalent than others and if there were any correlations with popularity. Lastly, I wanted to know if there are any differences between older songs with earlier releases and more recent songs with respect to things like instrumentation, danceability, energy, etc.
The Dataset
This dataset is sourced from Kaggle, focusing on the most streamed songs from Spotify in 2023. With over 390K views and 83K downloads, this dataset has received a 10/10 usability score as calculated by Kaggle. This dataset provides a comprehensive glimpse into the musical landscape of that year, and features attributes such as track name, artist, stream totals, musical key, and some metrics that try to gauge how danceable, how much energy, and how much of the instrumentation is live performance relative to most other songs. These specific metrics were web scraped from the popular website Tunebat.com
Exploratory Data Analysis
Utilizing SQL, I delved into the Spotify dataset to conduct exploratory data analysis (EDA), gaining valuable insights into the most streamed songs of 2023. Leveraging SQL queries, I identified the top artists and tracks, explored patterns across things like musical key and popularity, and found the number of songs present from each decade to gain a better understanding of the makeup of this dataset. This SQL-driven EDA illuminated some overarching trends but also helped lay the groundwork for further analysis with some visualizations.
Tableau Dashboard
The Tableau dashboard crafted for this project offers a dynamic and visually engaging exploration of this dataset. It features two bar charts where users can easily identify the ten biggest songs and artists, providing a snapshot of the most popular tracks and the artists dominating the streaming platform. The tree map visualization offers a comprehensive view of the distribution of musical keys, allowing for quick comparisons and insights into the prevalence of different keys within popular music. A pie chart illustrates the distribution between major and minor keys, offering a succinct representation of the two tonal characteristics of the top songs. Next is a line graph which showcases changes over time in metrics such as energy and danceability, enabling users to track the evolution of these attributes across the years. Lastly there is a search box that returns the top song from any given artist within the dataset.
Takeaways
Through rigorous analysis and visualization, this project unearthed several key insights into the musical preferences of Spotify users in 2023. It revealed the dominance of certain artists and tracks, highlighted the prevalence of specific musical keys, and showcased the evolving characteristics of top songs over time. By leveraging data analytics and visualization techniques, this project not only provided a comprehensive snapshot of the musical landscape but also offered valuable insights for understanding trends and patterns in the streaming industry.