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A Data Scientist in the Music Industry

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Jameel Syed, Cheif Scientist @Musicmetric presentation @ds_ldn March 21st, 2012

Published in: Technology, Education
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A Data Scientist in the Music Industry

  1. 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. 2. Overview  –  Musicmetric  CTO  –  InforSense  founding  member   •  PhD  in  Workflows  for  Life  Sciences  Analysis  –  Co-­‐organiser  Big  Data  London  meetup  
  3. 3. Some  ques/ons...  
  4. 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. 5. How  popular  am  I?  
  6. 6. Who  are  my  fans?  
  7. 7. Where  are  my  fans?  
  8. 8. What  is  the  press  saying?  
  9. 9.  Who  is  popular?    
  10. 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. 11. What  is  a  Data  Scien/st?  
  12. 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. 13. A  Life  Sciences  digression...  
  14. 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. 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. 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  

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