A Data Scientist in the Music Industry
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The document discusses the role of a data scientist in the music industry. It describes how music consumption has shifted online, creating large amounts of digital data. A data scientist in this field collects raw data from various online sources, derives insights from it using techniques like sentiment analysis and machine learning, and creates products like daily charts and tools for analyzing an artist's popularity, fans, and press mentions. The data scientist acts as a "jack of all trades", learning new technologies and bringing together different experts and components to solve problems.
A Data Scientist in the Music Industry
- 1. So, What Does a Data Scien/st do? A Data Scien/st in the Music Industry Dr Jameel Syed March 2012 h>p://jasyed.com/datascience/
- 2. Overview – Musicmetric CTO – InforSense founding member • PhD in Workflows for Life Sciences Analysis – Co-‐organiser Big Data London meetup
- 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 bi>orrent? – Do you listen to music on YouTube? – Do you “like” bands on Facebook? – Do you subscribe to Spo/fy? – Do you listen on the radio to the weekly charts on a Sunday aWernoon? • 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?
- 10. A Data Scien/st 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 fes/val? – Which ar/sts are (becoming) popular with listeners with certain demographics (in a region)? • Data processing, machine learning & sta/s/cal methods – Sen/ment analysis – Named En/ty Recogni/on – Ranking – Segmenta/on • One-‐offs – Infographics and microsites for events – Brand alignment via demographics – Music Hack Days • Product – Daily charts – Sen/ment scoring web crawled reviews
- 11. What is a Data Scien/st?
- 12. Have we been here before? • Sta/s/cian • Data Analyst • Quan/ta/ve analyst • Bioinforma/cian • Data Miner • Business Intelligence consultant • Computa/onal 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 deriva/ons are interes/ng; integra/ng data from mul/ple online sources. – Big Data technology to cope with data volume • Programming is essen/al – APIs – Heterogeneous environment(s) • Method of presenta/on – Infographics – Interac/ve (web) applica/ons – (Raw data)
- 15. Data Scien/st • “Jack of all trades” – “Hacker” mentality: learn new technology and approaches for a project on short no/ce – Crea/ve self-‐starters – Work alongside other experts (data, domain, soWware engineering)
- 16. A Data Scien/st is good at knieng? • Not building from scratch, knieng together pre-‐exis/ng parts • Data – Databases (rela/onal/NoSQL) – Files – APIs • Algorithms – Open source libraries – Off the shelf tools • Compute – Linux – AWS? • Languages – Many, especially “scrip/ng” languages