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, and likes
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

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users

| CSV

Contains 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

songs

| CSV

Contains 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

songs_liked

| CSV

Captures 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

user_listening_history

| CSV

Tracks 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, and listen_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

playlists

| CSV

Details 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

| CSV

Maps 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_id to a song_id
Enables identification of most common playlisted songs
Useful for collaborative filtering and playlist ranking

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