About Exploratory Data Analysis on "Top Songs of Spotify" between 2010-2022. This report explores on different data relations which can be formed from the given dataset.
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Spotify is a digital music streaming service that provides users access to over 82 million songs, podcasts and audio books. The app was developed by Daniel Ek and Martin Lorenzton in 2006. This app has become a family name over the years and boasts over 457 million subscribers as of 2022, rivaling SoundCloud and Apple Music.
Spotify measures the popularity of its' artists based on their monthly listeners and number of streams they receive on songs produced. These streams are then multipled by (0.003) and paid to artists as "Royalties", it is a modernized system of monetizing digital sales from traditional album sales (100 streams = 1 album). Ed Sheeran was Spotify's most streamed artist in 2019, however, the rank placements change rapidly depending on album relases, EP's, mixtapes and so forth!
Spotify is a perfect dataset to measure the popularity of songs against various music elements, across a large set of songs throughout the decades. This analysis can be used to demonstrate how peoples music tastes have been translated throughout the past two decades!
I will be creating an exploratory analysis by creating data visualizations and conducting statistical analyses to investigate the relationship between the use of non-traditional musical elements and the popularity of Spotify hits from 2000 to 2019.
Track Metadata
| column | description |
|---|---|
| track_name | Song title |
| artist_name | Song artist |
| artist_genre | Song genre category |
| year | Song Billboard chart entry year |
Audio Numerical Quantitive Data
| column | description |
|---|---|
| loudness | Loudness - How loud a song is (db) |
| duration_ms | Duration - How long the song is (seconds) |
| tempo | Tempo - How fast a song is (bpm) |
Audio Qualitative Data
| column | description |
|---|---|
| energy | Energy level - How energetic the song is |
| danceability | How easy it is to dance to |
| valence | How positive the mood of the song is |
| acousticness | How accoustic sounding the song is |
| speechiness | How much of a song is spoken word |
| track_popularity | How popular a song is (as of time of data collection) |
Link to Dataset: https://www.kaggle.com/datasets/josephinelsy/spotify-top-hit-playlist-2010-2022
- Connecting Database with Power BI Desktop
- Analyzing the tables and relations
- Data Cleaning using Power Query Editor
- Developing an Interactive BI Dashboard / Report
- Power BI
- After being down for 2 years, It looks like Spotify are back on track with their new tracks and artists.
- The Major reason for Spotify coming back in trend is because of addition of artists like Taylor Swift, Bad Bunny, Drake, The Weeknd and Others.
- Canadian Pop, Hip pop, pop and Rap Genre are majorly in trend currently on Spotify.
- Also with ongoing trend, It can be expected that Low energy, slow songs are more danceable (i.e: They are more used for dance performances/videos).
- Currently the Average track popularity on Spotify ranges between 70-80 out of 100.
- The Average time signature for current songs is 4.
Thank you!