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?
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
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)
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)
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