Syllabus

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: The Holly+ Project: Exploring AI and Data-Driven Artistry
• Empowering Artists with Data
• 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 vs. 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 Scatter Plots
• 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
• The Process of Analyzing Data
• What Defines Success?
• Different Tracks to Success
• Is Research A&R Democratizing the Music Industry?
• Assignment 4: Build Context for Your Chosen Artist/Band

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?
• Spotify API
• Getting Started with Spotify API
• Get Artist
• Retrieving Data
• Assignment 5: Collect and Analyze Data

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
• Moneyball!
• 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 Relational 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, former Sr. Data and Machine Learning 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
• The Process of Designing a Data Visualization
• Designing Data Viz: A Talk with Lisa Charlotte Muth
• 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
• Example Use Case of Using Data in Tour Planning
• Making Smarter Decisions
• How Could This Data Benefit the Business of Music and Strategies?
• The Future of Data and Music
• 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: The Artist Actually Gets Paid
• Potential Obstacles
• What Is Artificial Intelligence?
• What Does This Have to Do with the Business of Making Music?
• 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 Key—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?