Data analytics is in every industry - even music. Music analytics is one of the coolest ways to use data skills. Let’s learn about it. 👇

Every major music company is hiring data analysts right now.

Spotify. Apple Music. Live Nation. Record labels. Publishing companies. Streaming platforms.

They all need people who can predict what songs will go viral, analyze listener behavior, track trends across platforms like TikTok, optimize playlists and recommendations, and measure artist performance.

And the best part is, the skills you already have (SQL, Python, Excel, visualization tools) are exactly what these companies want.

Let me show you what that actually looks like.

Meet Chris Riva: A Music Data Analyst

I recently chatted with Chris Riva, who works as a Senior Product Manager of Data at Audiomack, a music streaming platform.

His job is to understand listener behavior.

• What songs do people skip?

• What makes them hit replay?

• How do trends spread on TikTok?

• Why do certain playlists perform better than others?

This is what music analytics looks like in real life.

Data Reveals The Hidden Patterns In Music

People think music is all emotion. All feeling. All vibes.

And it is. But underneath all that emotion, there are patterns.

For example, in my interview with Chris, he talks about how he analyzed over 1,000 number one hit songs from 1958 to today, and found that key changes (that moment when a song suddenly goes UP at the end) used to be in 20% of hit songs in the 70s and 80s. Today, it's almost zero.

Why? Hip hop became more popular, and hip hop focuses on rhythm and lyrics, not melody shifts.

He also found that TikTok now controls what becomes a hit, just like MTV did in the 1980s. Almost every number one song in the 2020s went viral on TikTok first.

Data reveals the hidden rhythms in human behavior.

And music companies need analysts who can find those patterns.

Hopefully, you.

What Music Analysts Actually Do

If you work in music analytics, your day might look like this:

• You pull data on how many people listened to a new song in the first 24 hours.

• You analyze which playlists are driving the most streams.

• You track how a song is performing across different countries.

• You predict which artists are about to blow up based on early listening trends.

You write SQL queries to pull listener data. You use Python or Excel to spot patterns. You build dashboards so teams can see what's working. You present findings to marketing teams, artists, and executives.

It's the same data skills you're learning. Just applied to music.

And you don't need to be a musician. You just need to be curious about music and good with data.

Where To Find Music Data Jobs

Music analytics roles exist at streaming platforms like Spotify, Apple Music, YouTube Music, Audiomack, and Tidal. They exist at record labels like Universal, Sony, and Warner. They exist at live event companies like Live Nation and AEG. They exist at music marketing agencies and publishing companies. And they're hiring right now.

So, Why Does This Matter?

Music analytics is real. And it's just one example of how data is EVERYWHERE.

The skills you're learning (SQL, Python, Excel, visualization) can take you to any industry.

Tech companies. Finance companies. Healthcare companies. Or music companies.

You just have to decide where you want to go.

P.S.Want to level up your analytics skills while exploring creative datasets like this? The Accelerator is full of soon-to-be-analysts doing exactly this. We’ve even analyzed our own Spotify data before. If you’re trying to pivot into data, this is your home!

It’s where analysts explore creative data, learn new skills, and have fun while growing together. You'll fit right in.

🎁 Giveaway Winner - Storytelling w/ Data

As promised last week, I'm giving away my signed copy of Cole Knaflic's newest Storytelling with databook!

Unfortunately, I didn't have time to feed all the new emails to my Python script that picks a winner randomly / based on how engaged of a reader you are.

So please excuse me while I push this to NEXT week's episode.

For all my giveaways, I weight your odds be a) how long you've been a reader b) how often you open my emails and c) how often you click the links. `

So if you're reading this a) thanks for being a subscriber b) thanks for opening and reading this email and c) click all the links you can 😉

I promise to have a winner for next week.