Motivation for this is many music listeners don't have a deep understanding of music theory and might not know what makes a song popular.
We often choose things based on how they make us feel, and music is no different. It’s a big part of our lives, and we each have our favorite genres, tracks, and bands. We might listen to soothing melodies when we need to focus, upbeat tunes for a workout, or exciting tracks at a party. We’re surrounded by a huge variety of music and sounds.
Users who want to know their favorite artists on Spotify are typically music enthusiasts who enjoy discovering trends and personalizing their listening experience. They value insights into their musical preferences and want an easy way to track and enjoy their favorite artists. These users seek tools that provide clear, engaging visuals and data about their listening habits.
In my previous role as an dashboard designer, I worked with a lot of data sets but realized I needed to dive deeper into visualizations. So, I updated my skills through courses, read some books, and chatted with peers to get a better grasp.
Goals:
- To figure out the best ways to represent data clearly and effectively.
- To use that knowledge with tools like Tableau and build context-based frameworks for data visualizations.
I imported data into Tableau from Spotify's annual top 100 songs, which includes features like BPM, Energy, Danceability, Loudness, Liveness, Acousticness, and Speechiness. To make the data easier to visualize, I scaled Danceability, Energy, Speechiness, Liveness, and Acousticness by multiplying them by 100.
Here's what each feature means:
- Danceability: Measures how suitable a song is for dancing (0.0 to 1.0).
- Energy: Shows the song's energy level (0.1 to 1.0).
- BPM: Tempo of the song (0 to 200 beats per minute).
- Loudness: Volume of the song in decibels.
- Liveness: Indicates if the song was recorded live (higher values suggest live recordings).
- Speechiness: Measures vocal clarity (values above 0.66 indicate spoken content).
- Acousticness: Determines if the track is acoustic.
- Make the data visualization responsive for different screen sizes.
- Allow users to upload personalized music playlists.
- Enhance the visualization’s appeal with motion design and enrich the data by comparing it with other music streaming services.
- Calculate the duration of each song.
