Analyzing Music Discovery and Recommendation Engine Using SQL
Group: Use Case
|Product Category: Database Design & Development
|Sub Category: PostgreSQL
About this Product
Discover music listening patterns and improve your SQL skills with real user data.
Business Overview:
Analyzing Music Discovery and Recommendation Engine Using SQL helps you explore how users interact with music on a streaming platform. You’ll use real-like data to find out what genres people prefer, how playlists are curated, and how listening habits change over time. This product is ideal for people who want to learn SQL by solving real business problems in the music industry and better understand how recommendation systems work.
Product Highlights:
- Engaging SQL guide with 5 real-world use cases focused on music discovery and user listening behavior
- Showcase clear sample outputs to help you understand expected results from each query
- Based on a dynamic dataset of 15000+ records covering users, songs, genres, playlists, and more
- A strong portfolio project for showcasing SQL and content analytics skills
Learning Outcomes:
By working through this product, you’ll be able to:
- Write SQL queries to analyze music listening trends and user preferences
- Discover how different users prefer different genres and playlists
- Understand seasonal listening habits and genre shifts
- Use SQL to support music discovery, curation, and personalization strategies
- Build confidence in analyzing real-world media datasets using SQL
Resources
Description:
This CSV provides user-level demographic and registration data, foundational for customer profiling and segmentation.
- Includes user IDs, registration details, and basic demographics
- Supports cohort analysis, growth trends, and user lifecycle modeling
- Useful for personalization, segmentation, and retention strategies
- Enables linking with playlists, listening history, and interactions
- Complements subscription, content, and activity datasets
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