User Engagement Dataset
Group Category: Dataset
Product Category: Database Design & Development
Sub Category: PostgreSQL
Track listening trends, discover power users, and personalize experiences with structured, SQL-ready user engagement datasets.
Business Overview:
The User Engagement Dataset captures interaction behavior on a music streaming platform, including listening sessions, song likes, and playlist activity. Structured across multiple entities, this dataset is ideal for analyzing user retention, content engagement, and personalization signals.
Product Highlights:
- Structured CSV datasets including
users,songs,playlists,listening history, andlikes - Ideal for modeling user behavior over time
- Ready for advanced SQL filtering, JOINs, and aggregations
- Suitable for building behavioral dashboards and personalization engines
Learning Outcomes:
By working with this dataset, you will:
- Analyze listening trends and user engagement levels
- Practice multi-table joins with playlists and listening history
- Identify behavioral insights from liked songs and session data
- Build features like top listeners and playlist activity analysis
songs_liked
| CSVCaptures the songs liked by each user, reflecting personal preferences and positive engagement signals. Ideal for recommendation systems and preference scoring.
Key Features:
- Includes
user_id,song_id, and timestamped like events - Can be used to compute like-to-play ratios or build user affinity models
- Useful for modeling song popularity or individual tastes
users
| CSVContains registered user information including personal identifiers, contact details, and subscription types. This file supports user segmentation, onboarding analysis, and account-based filtering.
Key Features:
- Records core user attributes such as
user_id,username,email, and subscription type - Can be used to group users by plan type (e.g., free vs paid)
- Ideal for building user cohorts or joining with behavioral or transactional tables
user_listening_history
| CSVTracks every listening event by users, linking them to specific songs and timestamps. This file supports session analysis, listening trends, and behavioral modeling.
Key Features:
- Granular data including
user_id,song_id, andlisten_date - Enables analysis of repeat listens, time-based usage, and user activity frequency
- Ideal for calculating daily/weekly active users, retention curves, or song popularity
songs
| CSVContains detailed metadata for individual songs, including identifiers, duration, album and artist associations. Supports music catalog modeling, duration analysis, and track-level filtering.
Key Features:
- Fields include
song_id,title,duration, and foreign keys to album and artist - Useful for analyzing song lengths, catalog depth, and artist productivity
- Enables joins with listening history or song preferences
playlists
| CSVDetails all playlists created by users, including metadata such as title, description, and owner. Supports playlist analytics, content strategy, and user-curated content analysis.
Key Features:
- Links playlists to creators (
user_id) - Stores playlist names and optional descriptions
- Useful for studying user curation patterns or collaborative behaviors
playlist_songs
| CSVMaps songs to playlists, defining the curated content within each user-generated playlist. Supports content inclusion analysis and playlist similarity studies.
Key Features:
- Each row links a
playlist_idto asong_id - Enables identification of most common playlisted songs
- Useful for collaborative filtering and playlist ranking
$1.60 $1.00 37% OFF
About this Dataset:
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