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2020 Spotify Hit Song Analysis
Data Science and Analytics
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About
An exploration of the top 50 most streamed tracks on Spotify for the year 2020. This data provides a detailed look into the musical characteristics and attributes that defined the year's most popular songs. It is ideal for analysing trends in music, understanding what features contribute to a song's success, and performing statistical analysis on musical data.
Columns
- ID: An identifier for the entry.
- artist: The name of the recording artist.
- album: The name of the album the track belongs to.
- track_name: The name of the song.
- track_id: The unique identifier for the song on Spotify.
- energy: A measure from 0 to 1 indicating how energetic the song is.
- danceability: A score from 0 to 1 that describes how suitable a track is for dancing.
- key: The primary musical key of the track.
- loudness: The overall loudness of a track in decibels (dB).
- acousticness: A value from 0 to 1 indicating the likelihood the track is acoustic.
- speechiness: Detects the presence of spoken words in a track.
- instrumentalness: Predicts whether a track contains no vocals, with values closer to 1 representing a higher likelihood of being instrumental.
- liveness: A value indicating the probability that the song was recorded during a live performance.
- valence: A measure from 0 to 1 describing the musical positiveness conveyed by a track.
- tempo: The overall estimated tempo of a track in beats per minute (BPM).
- duration_ms: The duration of the track in milliseconds.
- genre: The genre of the song.
Distribution
The data is provided in a single CSV file,
spotifytoptracks.csv, with a size of 7.78 kB. It contains 50 records, each representing one of the top tracks, and has 17 distinct columns detailing various attributes of the songs.Usage
This dataset is well-suited for a variety of applications, including:
- Music Trend Analysis: Identifying which genres, artists, and musical features were most popular in 2020.
- Data Visualisation: Creating charts and graphs to illustrate the relationships between song attributes like danceability, energy, and valence.
- Statistical Modelling: Building models to predict a song's popularity based on its characteristics.
- Educational Purposes: Serving as a practical dataset for teaching data analysis, statistics, and programming with libraries like Pandas and Matplotlib.
Coverage
The data covers the top 50 most-streamed songs globally on Spotify for the calendar year 2020. It does not have a specific geographic or demographic focus beyond the platform's global user base during that time. The data is a static snapshot and is not expected to be updated.
License
CC0: Public Domain
Who Can Use It
- Data Analysts and Scientists: Can explore musical trends and build predictive models to understand what makes a song a hit.
- Music Journalists and Enthusiasts: Can use the data to support articles and research on 2020's music landscape.
- Students and Educators: Can use this as a clean, real-world dataset for projects in data science, statistics, and computer science courses.
- App Developers: Can leverage the data to understand user preferences for building music recommendation features.
Dataset Name Suggestions
- Spotify Top 50 Tracks of 2020
- 2020 Spotify Hit Song Analysis
- Musical Attributes of Top Spotify Songs 2020
- Spotify Global Charts 2020: A Dataset
Attributes
Original Data Source: 2020 Spotify Hit Song Analysis
QUALITY