Spotify Streaming Insights Dashboard
Budget: ₹12,500 – ₹37,500 INR
I need an end-to-end Spotify analytics dashboard, ready for production use between March 2026 and April 2026. The data arrives as raw streaming logs; you will prepare it in Python (pandas, NumPy, Matplotlib/Seaborn where helpful) and feed a clean model into Power BI.
The finished report must speak the language of Analysts, exposing deep drill-through while staying light and fast. Core visuals include KPI cards for Distinct Songs, Average Popularity, and Position-1 Hits, an area graph that tracks monthly growth, plus a mix of charts, tables, slicers and other interactive elements that let users pivot by artist, track, album type or time period. Consistent colour and typography should guide the eye and keep the music theme alive.
Key objectives
• Transform messy CSV/JSON exports into a tidy star schema.
• Build calculated measures for each KPI and any supporting ratios.
• Design intuitive report pages with cross-filtering, custom tooltips and clear navigation.
• Document every step so another analyst can refresh or extend the work in the future.
Acceptable delivery
• Python ETL scripts and a brief README explaining dependencies and run steps.
• A self-contained .pbix file with all visuals wired to the model and sample data loaded.
• One-page guide that summarises insights revealed by the dashboard and highlights how to use it.
After my final data sample is shared, we will agree naming conventions and any additional bookmarks or storytelling elements you feel would elevate the experience.
The finished report must speak the language of Analysts, exposing deep drill-through while staying light and fast. Core visuals include KPI cards for Distinct Songs, Average Popularity, and Position-1 Hits, an area graph that tracks monthly growth, plus a mix of charts, tables, slicers and other interactive elements that let users pivot by artist, track, album type or time period. Consistent colour and typography should guide the eye and keep the music theme alive.
Key objectives
• Transform messy CSV/JSON exports into a tidy star schema.
• Build calculated measures for each KPI and any supporting ratios.
• Design intuitive report pages with cross-filtering, custom tooltips and clear navigation.
• Document every step so another analyst can refresh or extend the work in the future.
Acceptable delivery
• Python ETL scripts and a brief README explaining dependencies and run steps.
• A self-contained .pbix file with all visuals wired to the model and sample data loaded.
• One-page guide that summarises insights revealed by the dashboard and highlights how to use it.
After my final data sample is shared, we will agree naming conventions and any additional bookmarks or storytelling elements you feel would elevate the experience.
Related categories:
Python
Data Processing
Business Analysis
Data Warehousing
NumPy
Data Visualization
Data Analysis
Power BI
ETL
Data Modeling