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Data Analysis on the Spotify Dataset using Microsoft Excel and VBA.

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Spotify Excel Dashboard

The dashboard is created using the data from Kaggle, Spotify - All Time Top 2000s Mega Dataset. The dataset contains 15 columns and 1994 songs.

By simply clicking a button, you can effortlessly switch from the Spotify dashboard to the artist dashboard. Once in the artist dashboard, the slicer tool allows you to easily toggle between different artists, providing a focused view of one artist's analysis at a time. This seamless transition between the two dashboards empowers you with a comprehensive overview of all of Spotify's song analysis, as well as in-depth insights into individual artists' analytics, all with just a click of a button.

Artist Overview

The Artist Overview of the Dashboard is currently filtered or sliced to focus on ABBA, a popular Swedish music group. This selection allows for a detailed analysis and visual representation of ABBA's performance, statistics, and other relevant information within the dashboard.

We have the stats of ABBA:

  • Total no of releases each year - The group has been consistently releasing music for the past 20 years, with the highest number of releases occurring in 1979.
  • Popular Songs - The well-known songs by ABBA, comprising their top 5 hits.
  • Popular Genre - As mentioned, ABBA is a popular Swedish music group, their music leans towards Europop.
  • Energy Levels - The radar chart for music production identifies six distinct aspects: BPM, loudness, danceability, energy, liveliness, and acoustic qualities. The average BPM is 117.86, indicating a slightly slower tempo in the Allegro Moderato range. The average loudness is -8.50, suggesting a lower range. This implies that the music tends to have a calming and soothing quality. However, variations in danceability, energy, liveliness, and acoustic qualities are possible within this production. Overall, the data suggests that the music falls within a lower range, creating a calming listening experience.
  • Average Valence throughout the years - The years 1977 and 1980 featured various musical releases that displayed contrasting emotional tones. In 1977, songs were prevalent with lower valence, which often conveyed a sense of sadness or less upbeat music. However, starting in 1980, there was a significant shift in musical compositions towards a more joyous direction, as reflected by a noticeable increase in valence.

We also have metrics such as

  • Unique no of tracks - As we can see, ABBA has 22 unique tracks.
  • Popularity - ABBA is quite popular with a score of 53.9.

Artist Dashboard

Spotify Overview

The Spotify Overview of the Dashboard is currently not filtered or sliced. This allows for a detailed analysis and visual representation of the entire dataset's statistics, and other relevant information within the dashboard.

We have the stats of all 1994 songs from the dataset:

  • Total no of releases each year - The line graph shows a consistent and gradual increase in the number of songs released since 1960 when only one song was released. The trend continues to rise, reaching its highest point in 1967 with 37 releases, which is the largest number of releases in the dataset for that period. The dataset records the highest number of releases in 2008, with 54 songs, indicating a substantial growth in music production over the years.
  • Popular Songs - The bar chart displays the top 5 songs, ranked in order of popularity. Interestingly, there are multiple artists with songs of the same title, but each song is completely different. Among these songs, "Feeling Good" by Nina Simone is the most popular, while "One" by Johnny Cash is the least popular among the top 5.
  • Popular Genre - The doughnut chart depicts the top 5 most popular music genres. Album Rock and Alternative Rock occupy the first and third positions, respectively. Adult Standards, Alternative Metal, and Dance Pop follow, with Dance Pop notably securing the fourth spot. It is interesting to observe that rock and metal genres played a significant role in the past, considering the prominence of the two rock genres in the top positions.
  • Energy Levels - The analysis reveals an interesting contrast in the data. While the average loudness is measured at -9.01 dB, which might seem lower than expected for genres like rock and metal, other factors come into play. The beats per minute (BPM) averaging in the 120 range indicate a fast tempo, suggesting higher energy and danceability in the music. It's important to note that the average loudness, liveness, and acoustic qualities are influenced by various genres included in the analysis. The loudness averaging at -9, liveness at 19, and acoustic at 28 align with the diverse range of genres examined.
  • Average Valence throughout the years - The analysis of music valence across different years revealed interesting trends. Specifically, the year 1960 had the lowest valence score compared to other years, indicating a less positive or "happy" sound or music in the tracks. On the other hand, the year 1962 had the next immediate highest valence score, suggesting a more upbeat sound. Another notable finding was the dip in valence scores in the year 1994, which returned to a level comparable to 1960.
  • Popular Artists based on Popularity score - The graph illustrates the top 10 artists ranked by their popularity score. The Beatles claim the first position with a popularity score of 2451, while Fleetwood Mac holds the other end of the spectrum with a score of 1083.

We also have metrics such as

  • Unique no of Artists - There are 731 different artists included in the dataset
  • Unique no of tracks - The dataset contains a total of 1994 songs.
  • Longest Song - An intriguing find in the dataset is the song "Echoes" by Pink Floyd, which stands out as the longest track at 23 minutes and 53 seconds, significantly surpassing the typical song durations of 2:30 to 3:30 minutes or sometimes even 4 or 5 minutes.

Spotify Dashboard