SQL for Music Streaming Analysis: A Hands-On Approach to Spotify User Engagement. Master key SQL queries to explore Spotify’s user behavior, identify listening trends, and optimize engagement. delivering data-driven insights for better content and marketing strategies.
git clone https://github.com/jayantibhanushali007/Spotify_Data_Analysis_SQL.gitSQL for Music Streaming Analysis: A Hands-On Approach to Spotify User Engagement. Master key SQL queries to explore Spotify’s user behavior, identify listening trends, and optimize engagement. delivering data-driven insights for better content and marketing strategies.
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git clone https://github.com/jayantibhanushali007/Spotify_Data_Analysis_SQLCopy the install command above and run it in your terminal.
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I want to analyze Spotify user engagement data using SQL. I have a dataset with user listening history, including track IDs, timestamps, and user IDs. Write SQL queries to help me identify top tracks, user listening patterns, and trends over time. Focus on [COMPANY] in the [INDUSTRY] sector. Provide queries for the following: 1) Top 10 most streamed tracks in the last 30 days, 2) User listening patterns by time of day, 3) Trends in user engagement over the past year.
# Spotify User Engagement Analysis ## Top 10 Most Streamed Tracks (Last 30 Days) ```sql SELECT track_id, COUNT(*) as stream_count FROM user_listening_history WHERE timestamp >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) GROUP BY track_id ORDER BY stream_count DESC LIMIT 10; ``` ## User Listening Patterns by Time of Day ```sql SELECT HOUR(timestamp) as hour_of_day, COUNT(*) as stream_count FROM user_listening_history GROUP BY hour_of_day ORDER BY hour_of_day; ``` ## Trends in User Engagement (Past Year) ```sql SELECT DATE_FORMAT(timestamp, '%Y-%m') as month, COUNT(*) as stream_count FROM user_listening_history WHERE timestamp >= DATE_SUB(CURRENT_DATE(), INTERVAL 1 YEAR) GROUP BY month ORDER BY month; ```
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