NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Student Works > Finding Music Around the World

Finding Music Around the World

Joshua Litven
Posted on Oct 23, 2016

As our world becomes increasingly globalized, the ability to understand and connect with people from other cultures becomes more important than ever. Currently, the most common approach is to taste ethnic cuisines or watch bizarre game shows.

But what if we could connect through music?

In this exploratory analysis, my goal is to find niche artists popular in various countries that remain relatively unknown to the rest of the world. You can check out the resulting playlist here, which shows niche artist from across the globe.

The data consisted of two data sets scraped from the website last.fm, a website wherein users "scrobble" music they listen to, keeping a log of their listening history. Users can then find similar users or artists. The first data set consists of the entire listening history of 1000 users. The second data set consists of user's favorite artist play counts for 360,000 users. The data was scraped starting from the signup date of the user until 2009.

I had three primary questions about this dataset:

  1. How diverse are the users' music collection?
  2. Where are the most popular artists listened to?
  3. Who are the niche artists in each country?

It is important to remember before reading on that last.fm users don't necessarily represent the population at large and are almost certainly more pretentious. Also, it is unknown how the users were scraped so there could potentially be sample bias, as well as a bias among the population of last.fm users themselves.

How Diverse are the Users' Music Collection?

The number of performing artists in the world is vast. According to one estimate, there are 97 million and counting, or as George Carlin put it: "There's too many  **** songs". Due to technology making music creation tools more accessible, this number will surely continue to rise.

But how much does a typical music listener actually listen to?

Let's sample a random user and aggregate their listening history over the week to get a sense of their listening habits.

random_user_listening_activity

We can see a couple interesting trends. First, it is clear that the listening habits on the weekdays compared to the weekends are different. On the weekdays, there appears to be two peaks at around 9 am and 8pm. Less listening happens on the weekend, and music is spread out throughout the afternoon. Clearly, we are creatures of habit.

Let's break this down by the top 10% of artists in terms of songs listened to by day.

random_user_listening_activity_breakdown

We can see that the top 10% of artists make up the majority of the user's listening experience. In other words, they are mostly listening to the same artists. Does this trend generalize to other users?

To understand the diversity of users, I plotted the percentage of a subset of 100 user's listening history as a function of the number of artists.

diversity_of_user_history

We see from this plot a similar trend to the sample user: A small number of artists comprise most of the user's listening history. For most users, about 100 artists make up over 80% of their history.

In other words, most listeners are not adventurous!

Where are the most popular artists listened to?

To get the top artists, I defined the popularity of an artist to be the proportion of  users who favourited an artist. I then sorted by popularity, yielding the following plot:

most_popular_artists

The most popular artist is Radiohead, appearing in over 20% of the user's favorite artists, followed closely by The Beatles. Most of the top artists are from the United States or the UK, which isn't surprising given the demographics of users:

users_choropleth

Breakdown of users by country. Most users are from America.

What happens when we look at top artists in each country? Below is a list of top artists from a few countries:

top_artists_across_countries

The takeaway here is that countries share many of the same artists. In particular, Radiohead, The Beatles and Coldplay show up in all countries. To investigate this further, I extracted the top 10 most popular artists from each country and created a network  showing the relationship between artists and countries (interactive network here). The network for countries with over 500 users is shown below.

country_artist_network

Network showing connections between countries and their top 10 artists. Only countries with more than 500 users are shown for clarity.

Note that node size for artists represents the degree, i.e. the number of countries connected to the artist. The network shows artists popular worldwide in the center, with less known artists on the periphery. The top artists from the previous plot also show up in the center cluster:

country_artist_network_zoom

That is, artists with high worldwide popularity tend to be popular in the most number of countries.

The popularity network suggests that top artists of each country can be broken into two categories: Artists that are popular worldwide and artists that are only popular in the country. My hypothesis was that the latter category comprised of artists originating from the country, but the datasets did not provide artist origin. I was able to find a subset of artist location data from the Million Song Datset, which I used to generate a comparison of origin popularity and world popularity:

origin_popularity_vs_world_popularity

We see here that the majority of artists have higher popularity in their country of origin than the world, indicated by the fact that most points lie above the y = x line. This suggests some truth to my hypothesis. The niche artists are represented here in the upper left area with high origin popularity and low world popularity.

Who are the niche artists in each country?

To find niche artists, I created a new popularity network with a country's top 10 artists and sorted by country popularity - world popularity, as suggested by the previous plot. I then removed any artists that had more than one edge. That is, artists that were popular in more than one country. This yielded the following, much sparser, "niche artist network" (interactive network here):

Network showing connections between countries and their niche artists. Only countries with more than 500 users are shown for clarity.

Network showing connections between countries and their niche artists. Only countries with more than 500 users are shown for clarity.

Each country is now a disconnected graph containing artists relatively unpopular in other countries. Note that vertex size corresponds to their relative popularity in that country. Canada, for example, has the following graph:

niche_artist_network_zoom

Unsurprisingly, all of these artists are from Canada.  Moreover, they are fantastic bands, and I urge any non-Canadians to rock out to them! You can also check out a playlist I made on Github which shows the top niche artist for each country and a youtube link to one of their songs.

Conclusions

From the preceding analysis, we see that

  • A few number of artists make up the majority of a user's listening history
  • The most popular artists in the world are generally from the United States and the United Kingdom
  • Countries share many of the same top artists
  • Artists are more popular in their country of origin than the rest of the world
  • Popular niche artists can be discovered by pruning the popularity network

Future Work

It would be interesting to investigate new artists by connecting with a dataset that contains artist formation dates, and scrape data from more recent years to get more relevant artists. It would also be interesting to see how certain bands  get big in countries outside of their origin countries (e.g. artists Big in Japan).

A future interactive application could be made with Shiny to allow users to select different countries and listen to the niche artists found from this analysis.

References

  • Music Recommendation Datasets for Research
  • Million Song Dataset
  • 97 Million and Counting (marsbands.com blog)
  • Discover New Musc from Around the World! (musicnotes.com blog)
  • Big in Japan (Wikipedia article)

 

About Author

Joshua Litven

Joshua Litven received his Master's degree in Computer Science at the University of British Columbia where he worked on developing parallel algorithms to simulate realistic collisions between highly deformable objects. In practice, this meant watching lots of virtual...
View all posts by Joshua Litven >

Leave a Comment

Cancel reply

You must be logged in to post a comment.

Find Music Around the World | Joshua Litven October 31, 2016
[โ€ฆ] In the meantime, check out my first project: Finding Music Around the World! [โ€ฆ]

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application