Challenge 22: Analyzing Top Streaming Artists
Level: Medium
Description: You are part of a music analytics team exploring what drives global listening trends. Using a dataset with the most streamed songs up to 2024, your goal is to uncover which artists dominate the charts. Clean and transform the data, and then answer these key questions: (1) Which artists release the most music? (2) Who ranks highest in average track score? (3) Who leads the streams overall?. Finally, discover which artists make the cut on both popularity and consistency.
Beginner-friendly objective(s): 1. Load the dataset and filter for only these columns: Track, Artist, Release Date, Track Score, and Spotify Streams. 2. Convert necessary data types for accurate analysis, such as transforming string-based numbers to numerical formats. 3. Handle missing values and perform data transformations, such as converting date strings to date formats and extracting specific date parts. 4. Aggregate data to answer the question: How many tracks were generated per year? Visualize the results using a bar chart.
Intermediate-friendly objective(s): 1. Implement filtering operations to identify top-performing artists based on: Top 10 artists with the highest mean track score (minimum of 10 tracks), and Top 10 artists with the highest total streams. 2. Find artists who appear in both of these lists. How many artists make the final list?
Author: Keerthan Shetty
Dataset: Streaming music data on KNIME Community Hub
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