What a Data Scientist does in the music industry, and my thoughts on what a data scientist is. Presented at the March 2012 Data Science London meetup

1. So, What Does a Data Scientist do?
A Data Scientist in the Music Industry
Dr Jameel Syed
March 2012
http://jasyed.com/datascience/

2. Overview
– Musicmetric CTO
– InforSense founding member
• PhD in Workflows for Life Sciences Analysis
– Co-organiser Big Data London meetup

3. Some questions...

4. Music has moved online
• The world has changed
– Do you buy vinyl/tapes/CDs of music?
– Do you buy music downloads?
– Do you download illegal content from bittorrent?
– Do you listen to music on YouTube?
– Do you “like” bands on Facebook?
– Do you subscribe to Spotify?
– Do you listen on the radio to the weekly charts on a
Sunday afternoon?
• What’s happening online?

5. How popular am I?

6. Who are my fans?

7. Where are my fans?

8. What is the press saying?

9. Who is popular?

10. A Data Scientist in the Music Industry
• Raw Data -> Derived Data -> Insight
– Who is popular right now/in the immediate future?
– What was the effect of appearing at a festival?
– Which artists are (becoming) popular with listeners
with certain demographics (in a region)?
• Data processing, machine learning & statistical
methods
– Sentiment analysis
– Named Entity Recognition
– Ranking
– Segmentation
• One-offs
– Infographics and microsites for events
– Brand alignment via demographics
– Music Hack Days
• Product
– Daily charts
– Sentiment scoring web crawled reviews

11. What is a Data Scientist?

12. Have we been here before?
• Statistician
• Data Analyst
• Quantitative analyst
• Bioinformatician
• Data Miner
• Business Intelligence consultant
• Computational physicst

13. A Life Sciences digression...

14. What’s new?
• Data provides the opportunity
– Old: Collect and store data presupposing how it will be used
– New: Collect raw data & explore which derivations are
interesting; integrating data from multiple online sources.
– Big Data technology to cope with data volume
• Programming is essential
– APIs
– Heterogeneous environment(s)
• Method of presentation
– Infographics
– Interactive (web) applications
– (Raw data)

15. Data Scientist
• “Jack of all trades”
– “Hacker” mentality: learn new technology and
approaches for a project on short notice
– Creative self-starters
– Work alongside other experts
(data, domain, software engineering)

16. A Data Scientist is good at knitting?
• Not building from scratch, knitting together pre-existing parts
• Data
– Databases (relational/NoSQL)
– Files
– APIs
• Algorithms
– Open source libraries
– Off the shelf tools
• Compute
– Linux
– AWS?
• Languages
– Many, especially “scripting” languages