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?
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
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)
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)
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