Data Analytics in the Music Business
Authored by Liv Buli
Course Code: OMBUS-537
Level 5 (Proof of a Bachelor's Degree Required)
This course will help you gain a deep understanding of the role of data in the business of music, balancing theoretical concepts, illustrative case studies, and practical application. When used correctly, data about artists and music serve as invaluable tools, allowing companies to measure performance accurately and understand the current market, informing decisions with real facts and figures, and providing insight into how existing processes may be made more efficient. You will learn how to implement a data strategy, including its benefits and risks, and gauge how difficult it can be to obtain accurate data. You will apply data analysis, extract data, and perform queries, and you’ll also learn to represent data visually in ways that help with communication and understanding.
By the end of the course, you will be able to:
- Select the appropriate methods, fundamentals, environments, and occasions to apply data analysis
- Write SQL (Structured Query Language) and Excel formulas in order to extract and analyze data
- Determine which types of questions it is possible to ask of the data available
- Apply data to the business of music from album releases to promotional strategies and touring
- Analyze statistical concepts
- Assess any available data set, its source, and suggested application
- Visually represent and communicate data
Lesson 1: What’s Data Got to Do with It?
- Introduction to Data for the Music Industry
- Volume, Variety, and Velocity
- Sales, Radio Spins, Streaming, and Social Data
- What Is a Data Strategy?
- Case Study: Bombadil
- Assignment 1: Find a Data Strategy Example
Lesson 2: Introduction to Statistics
- Introduction to Statistics and the Need for Context
- How Do We Present Data?
- Variables: Categorical or Numerical
- Data Collection and Sampling
- Correlation Does Not Mean Causation
- Assignment 2: Statistics
Lesson 3: Charts, Graphs, and Plots: Tools for Examining our Data
- DataGeek Records
- The Value of Context: Mean and Median
- Determining Distribution and Skew for Numerical Data
- Exploring Multiple Variables with Scatterplots
- Deriving Insights
- Variability in Your Data Set: Standard Deviation and Variance
- Summarizing Categorical Data:Contingency Tables and Bar Charts
- What Is an Outlier and Why Is It Important?
- Assignment 3: Select an Artist or Band for Case Study Analysis
Lesson 4: Data-Driven Research A&R
- The Gut vs. Data Debacle
- What Does Research A&R Entail?
- The Job Description
- Skillset of a Research A&R
- It’s Not a Science: Methodologies of A&R Analysis
- What Defines Success?
- Different Tracks to Success
- Assignment 4:Analyze Data
Lesson 5: Finding, Cleaning, and Storing Data
- Meet the Data Pyramid
- The Trouble with Data Is . . .
- Common Issues with Music Industry Data
- Where Do ‘Datas’ Come From?
- Building a Scraper
- What Is an API?
- API Tutorial
- Assignment 5: Define Success for Your Chosen Artist/Band
Lesson 6: Introduction to Predictive Analytics
- Why Do We Want to Predict and How Do We Do It?
- The Difficulty of Predicting
- How Do We Build Predictive Models
- Why Is Prediction so Damn Hard?
- Potential Issues and Questions
- What Can the Music Industry Learn from Baseball?
- Communicating Technical Concepts to a Non-Technical Audience
- Bridging the Gap
- Is Data Ruining the Game That Is Baseball?
- Assignment 6: Data for Your Artist Case Study
Lesson 7: SQL: Extracting Data
- Introduction to Programming Languages Including SQL, Python, R, and D3.js
- Getting Started with SQL and Relational Databases
- Extract Relevant Information from a Rational Database
- The Fundamentals of SQL—SELECTing Our Data
- More SQL Functions
- Narrowing Our Search—SELECT * FROM artist WHERE genre IS LIKE “country”
- Narrowing Our Search Further
- In the Event of Multiple Conditions
- Using "SQL" in Google Sheets
- Assignment 7: Define the Ideal Structure for a SQL Query
Lesson 8: In Which SQL Meets Data Science . . . Performing Complex Functions on Multiple Tables
- Aggregate Functions
- Basic Arithmetic in SQL
- HAVING Specific Needs
- JOINing Tables
- Cleaning Data (INSERT and DELETE) and UPDATing Information
- The Data Scientist’s Job Description
- Interview with Claire Dorman, Sr. Data Scientist with Pandora Music
- The Music Genome Project
- Assignment 8: Submit Queries for Musical Tutorial
Lesson 9: Show, Don’t Tell: Data Visualization and Information Graphics
- It’s Not the Pie Chart, It’s You
- We Are Not yet Data Literate
- From Bar Charts to Box Plots: The Fundamentals of Data Visualization
- Designing Data Viz: A Talk with Lisa Charlotte Rost
- Tools for Building Data Viz: From Static to Interactive
- Assignment 9: Create a Case Study Chart
Lesson 10: Why Transparency and Context Matter
- Why Transparency Matters!
- Tour Planning Logistics
- Using Data to Improve Tour Planning
- Making Smarter Decisions
- How Could This Data Benefit the Business of Music and Strategies?
- Leveraging Data to Better Understand How Fans Interact with Artists
- Looking for Data Where Data Isn't
- The Historic Power of Labels
- Streaming Services and Social Media
- Looking Beyond the Data
- TIDAL Case Study: Data Manipulation and Lack of Transparency
- Assignment 10: Build a Proposed Tour Schedule or Marketing Strategy
Lesson 11: What On Earth Is the Blockchain and Why Does it Matter?
- Blockchain 101—the Tech Fundamentals
- How Blockchain Works
- The Problem of Digital Rights Management—Why We Need a Data Standard
- Copyright Review
- The Case for Using Blockchain for the Music Business
- A New Type of File
- How-To Guide:How The Artist Actually Gets Paid
- Potential Obstacles
- Assignment 11: Finalize Your Artist/Band Case Study
Lesson 12: Indexes and Benchmarks: Summarizing What We’ve Learned and Setting New Standards for the Industry
- What Is the Ideal Data Set or Data Point?
- Streaming Data
- Overlooked Sources of Information
- Context Is King—Building Benchmark Values and Setting New Industry Standards
- Important Milestones
- The Current State of Music Data Intelligence and Where Do We Go from Here?
- Conclusion—Can Data and Intelligence Really Add Value to the Music Industry?
Proof of a Bachelor's Degree
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Prerequisites and Course-Specific Requirements
Prerequisite Courses, Knowledge, and/or Skills
This course does not have any prerequisites.
- OpenIntro Statistics (4th Edition) by David M. Diez, Christopher D Barr, Mine Cetinkaya-Rundel (OpenIntro Inc., 2019)
- The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures by Dona M. Wong (W. W. Norton & Company, 2013)
- The Signal and the Noise: Why So Many Predictions Fail—but Some Don't by Nate Silver (Penguin Books, 2015)
- Structured Query Language (printable Wikibook)
Media and Subscriptions
- Medium membership
- Google account
General Course Requirements
Below are the minimum requirements to access the course environment and participate in Live Chats. Please make sure to also check the Prerequisites and Course-Specific Requirements section above, and ensure your computer meets or exceeds the minimum system requirements for all software needed for your course.
- Latest version of Google Chrome
- Zoom meeting software
- Speakers or headphones
- External or internal microphone
- Broadband Internet connection
Liv Buli is a journalist, author, and entrepreneur. Having spent time in the traditional newsroom, Buli became fascinated by the phenomenon of “big data” early on in her career and soon found herself working at the intersection of journalism, tech, and creative industries. As the world's first music data journalist, she helped grow Next Big Sound to the leading provider of online music analytics by publishing industry-defining research. The analytics provider was acquired by Pandora Music, where Buli joined as a senior data journalist. Buli is also the author of Vizkidz: a series of books that teach the fundamental concepts of data visualization and analysis to children. She has spoken in classrooms and conferences around the world, from SXSW to NYU, and considers herself a champion of data literacy.
Carlos “Charly” Arana is a guitarist, producer, and researcher. His specialties range from Latin American rhythms to the application of machine learning and artificial intelligence techniques for music. As a guitarist, arranger, and musical director he has worked and recorded with artists from all over the world, including legendary Bossa Nova singer Maria Creuza, whose band he was a member of. He has edited a number of books for Hal Leonard and Warner Bros. Publications (Alfred Publications), and for his studies and research on machine learning and AI he has been invited as a speaker in congresses and seminars at some of the most prestigious technology universities, such as MIT and UC Berkeley.
When taken for credit, Data Analytics in the Music Business can be applied towards these associated programs: