3-Credit, Graduate Level Course
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
- Sales, Radio Spins, Streaming, and Social Data
- What Is a Data Strategy? Using Data for the Business of Music: Tour Planning, Promotion, Campaigns, and More
- Case Study: Bombadil
- Assignment 1: Identify and Research an Existing Example of a Data Strategy Employed within the Music Industry
Lesson 2: The Trouble with Percentages Is . . . (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: OpenIntro Statistics, Exercises 1.9.1-1.9.3
Lesson 3: Charts, Graphs, and Plots - Tools for Examining our Data
- The Value of Context: Mean vs. Median and More
- Determining Distribution and Skew for Numerical Data
- Exploring Multiple Variables with Scatterplots
- Variability in Your Data Set: Standard Deviation and Variance
- Summarizing Categorical Data: Frequency and Contingency Tables
- What Is an Outlier and Why Is It Important?
- Assignment 3: Select an Artist or Band as the Focus for Your Case Study
Lesson 4: Data-Driven Research A&R
- The Gut vs. Data Debacle
- The Job Description
- It’s Not a Science: Methodologies of A&R Analysis
- What Defines Success?
- Assignment 4: Why Should a Label Should Sign This Particular Artist?
Lesson 5: Finding, Cleaning, and Storing Data
- Meet the Data Pyramid
- The Trouble with Data Is . . .
- Where Do ‘Datas’ Come From?
- What Is an API?
- API Tutorial
- Assignment 5: Build Your Own Scraper to Collect Data, or Access an API with Data That You Find Interesting
Lesson 6: Introduction to Predictive Analytics
- Why Do We Want to Predict and How Do We Do It?
- Why Is Prediction so Damn Hard?
- What Can the Music Industry Learn from Baseball?
- Communicating Technical Concepts to a Non-Technical Audience
- Assignment 6: Design Your Research Question, as Well as Evaluate the Viability of Your Research
Lesson 7: Getting Hands on with SQL - Extracting Data
- Introduction to Programming Languages Including SQL, Python, R, and D3.js
- Getting Started with SQL and Relational Databases
- The Fundamentals of SQL - SELECTing Our Data
- Narrowing Our Search - SELECT * FROM artist WHERE genre IS LIKE “country”
- Using SQL in Google Sheets
- Assignment 7: SQL Query of a Publicly Available Data Set
Lesson 8: In Which SQL Meets Data Science . . . Performing Complex Functions on Multiple Tables
- Aggregate Functions, Basic Arithmetic, and HAVING Specific Needs
- Cleaning Data (INSERT and DELETE) and JOINing Tables
- The Data Scientist’s Job Description
- Interview with Claire Dorman, Sr. Data Scientist with Pandora Music
- Assignment 8: SQL Query of a Publicly Available Data Set with Aggregate Functions, and More Than One Table in Your Database
Lesson 9: Show, Don’t Tell - Data Visualization and Information Graphics
- It’s Not the Pie Chart, It’s You
- From Bar Charts to Box Plots - the Fundamentals of Data Visualization
- Designing Data Viz - a Talk with Lisa Charlotte Rost
- Taking It to the Next Level - Interactive versus the Static Graph
- Why (and How) Matt Daniels Built the Pudding
- Assignment 9: Build a Static Graphic Using Google Sheets
Lesson 10: Why Transparency and Context Matter
- Why Transparency Matters!
- How Could This Data Benefit the Business of Music and Strategies?
- Looking for Data Where Data Isn’t: An Interview with Music Tech Journalist Cherie Hu
- Tidal Case Study - Data Manipulation and Lack of Transparency
- What Limitations a Data Scientist Sees in a Data-Driven Approach to Business Strategy
- Assignment 10: Build a Proposed Tour Schedule or Build a Proposed Marketing Strategy
Lesson 11: What On Earth Is the Blockchain and Why Does it Matter?
- Blockchain 101 - the Tech Fundamentals
- The Problem of Digital Rights Management - Why We Need a Data Standard
- The Case for Using Blockchain for the Music Business
- How-To Guide - How The Artist Actually Gets Paid . . .
- Assignment 11: Finalize Your Artist 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?
- Context Is King - Building Benchmark Values and Setting New Industry Standards
- 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?
OpenIntro Statistics, Third Edition by David M. Diez, Christopher D Barr, Mine Cetinkaya-Rundel
Structured Query Language: Standard Track Print by Various Authors, Wiki Books
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
The Signal and the Noise: Why So Many Predictions Fail—but Some Don't by Nate Silver, Penguin Books
- Google account
- Next Big Sound (student) account
- 500 MB hard drive space
- Speakers or headphones
- Internet connection with at least 4 Mbps download speed (http://www.speedtest.net to verify or download the Speedtest by Ookla app from your mobile app store)
Author & Instructor
Liv Buli is a data journalist and author living in Olive, New York. Previously a senior music data journalist with Pandora Media and Next Big Sound, Buli has spent most of her career working at the intersection of storytelling, data science, and visualization, thinking about how best to tell stories with data and speaking at conferences around the world. She is also the author of Penelope Pie's Pizza Party, the first book in the Vizkidz series: a collection of books that teach the fundamental concepts of data visualization and analysis to children.
Daniella Bradley OʼBrien has been working with data professionally in various settings for the past decade. As a research associate at the New York City Department of Health and Mental Hygiene (NYC DOHMH) Bureau of Alcohol and Drug Use, Prevention, Care and Treatment, Daniella used several data sources to examine morbidity and mortality related to substance use and misuse in New York City with a focus on opioids. She then used a data-driven approach to identify sources of revenue for the Town of Orleans, MA. Currently, Daniella uses financial data to support the day-to-day activities of a small marine company. Her teaching experience includes guest lectures in substance use epidemiology at CUNY/Hunter School of Public Health and various data training workshops and lectures at NYC DOHMH.