Welcome to my Spotify Data Analysis Project, where I explore and analyze Spotify music data to gain meaningful insights and solve business problems. This project focuses on analyzing various metrics related to tracks, artists, albums, and user engagement using SQL queries.
- Conducted comprehensive data analysis using advanced SQL queries.
- Performed Exploratory Data Analysis (EDA) to clean and understand the dataset.
- Generated insights and provided data-driven solutions to solve business challenges.
- Extracted meaningful insights to support strategic decision-making.
- Counting unique artists and tracks
- Identifying distinct album types
- Cleaning records with invalid duration values
- Exploring data dimensions for further insights
- Retrieve tracks with more than 1 billion streams
- List all albums along with their respective artists
- Get the total number of comments for licensed tracks
- Identify tracks that belong to the album type "single"
- Count the number of tracks by each artist
- Calculate the average danceability of tracks per album
- Find the top 5 tracks with the highest energy values
- List tracks with views and likes where the official video is available
- Calculate the total views of all tracks for each album
- Compare track streams on Spotify vs YouTube
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Use window functions to find the top 3 most-viewed tracks for each artist
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Identify tracks with a liveness score above the average
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Calculate the difference between the highest and lowest energy values for tracks in each album
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Email: arundeepp9393@gmail.com
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LinkedIn: linkedin.com/in/arun
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GitHub: github.com/ArunCooksData