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What Managers Need to Know about Data Science

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What Managers Need to Know about Data Science

  1. 1. What  Managers   Need  to  Know   about  Data  Science   Annie  Flippo  
  2. 2. Outline   •  What is data science •  Industry trends •  What is data •  The Optimal Data Scientist •  The Optimal Manager •  Topics in Data Science •  Topics in Cloud Computing
  3. 3. Who  am  I?   Annie Flippo Data Scientist Software Engineer Product Manager Database Developer
  4. 4. What  is  Data  Science?  
  5. 5. Usage  of  Data  Science   Finance:  fraud  detecAon,  score   buying  habits,  calculate  risks   Insurance:  inspect  driving  habits,   assess  risks,  determine  premiums  
  6. 6. Usage  of  Data  Science   Biometrics:  wearable  devices  to   monitor  and  improve  health   Digital  MarkeAng:  recommender   systems,  audience  segmentaAon,   retargeAng,  churn  predicAon  
  7. 7. Usage  of  Data  Science   Retail:  Walmart  launches   compeAAon  to  solve  business   problems  and  to  recruit  talent   Online:  NeHlix  launched  $1   million  prize  to  improve   recommendaAon  system  
  8. 8. Usage  of  Data  Science   Healthcare:  Heritage  Network     launched  a  compeAAon  to   predict  the  probability  of   hospitalizaAon  of  paAents.   ScienAfic:  NaAonal  Data  Science   Bowl  to  predict  ocean  health:   one  plankton  at  a  Ame  
  9. 9. Why  Should  YOU  Care?   According  to  McKinsey1  (2011),  Big  Data:   The  next  fron5er  for  innova5on,   compe55on,  and  produc5vity.   “By  2018,  the  United  States  alone  could   face  a  shortage  of  140,000  to  190,000   people  with  deep  analyAcal  skills  as  well   as  1.5  million  managers  and  analysts  with   the  know-­‐how  to  use  the  analysis  of  big   data  to  make  effecAve  decisions”  
  10. 10. Why  Should  YOU  Care?   According  to  Forbes2  (Oct  2015),  The  Hunt   For  Unicorn  Data  Scien5sts  LiCs  Salaries  For   All  Data  Analy5cs  Professionals   •  Experienced  data  scienAsts  are  paid  more  than  $200k   per  year   •  Median  salary  for  data  scienAst  increased  from   $115,250  to  $125,000  in  one  year   •  Managers  managing  large  teams  can  expect  a  median   salary  of  $235,000  
  11. 11. Because  it’s  a   growing  and   exciAng  field   with  high   compensaAon!  
  12. 12. Explosion  of  Data  Science Why  now?   •  Storage  cost  has  decreased  dramaAcally   •  CompuAng  power  has  increased  exponenAally   •  People  are  carrying  smartphones,  mini   supercomputers  in  their  pockets   •  Perfect  intersecAon  of  data  availability  and   compuAng  power  for  analyAcs
  13. 13. Massive  amount  of  data Streaming  into  your  company  …  
  14. 14. What  is  data? It  can  be  raw  web  traffic  logs  …  
  15. 15. What  is  data? Semi-­‐structured  data  from  APIs  …  
  16. 16. What  is  data? Or,  structured  data  from  databases…   …  what  to  do  with  all  this  data?  
  17. 17. The  Data  ScienAst Can  wrangle   data  from   many   sources  or   formats  
  18. 18. The  Data  ScienAst do  deep  data   exploraAons  …   and  perform   thorough  analyses    
  19. 19. DS  Skills  Inferred  by  Job  Openings •  Ph.D.  in  math,  staAsAcs,  engineering  or   physical  science  (Is  it  really  required?)   •  Has  5+  years  in  programming  experience  in   Java,  Scala,  Python,  R,  SQL,  MapReduce,  etc.   •  Has  5+  years  experience  in  most  of  the   Apache  Open  Source  Technologies  (e.g.   Hadoop,  Spark,  Hive,  Pig,  Kaka,  etc)*   •  Tell  a  story  like  a  novelist  (coherently  and   beauAfully)   *  By  the  Ame  you  read  this  footnote,  the  Apache  stack  has  already  grown.  
  20. 20. The  OpAmal  Data  ScienAst Is  a  person  with  deep  staAsAcal  and  machine   learning  knowledge,  extensive  somware   engineering  skills  and  well-­‐versed  in  business   strategy!  
  21. 21. The  OpAmal  Data  ScienAst  –  Take  2 Personality  Traits3   •  Compulsive   •  Propulsive  laziness   •  Drive  to  create  and  learn   •  Irritable  determinaAon   •  InsensiAvity  to  pain  (hmm…)   •  Integrity   •  Humility  
  22. 22. The  OpAmal  DS  Manager •  Former  data  scienAst  (good  to  have  but  not   necessary;  that’s  just  asking  for  another   unicorn!)   •  Actually  interested  in  managing  people   •  Thirst  to  learn     •  Apt  in  managing  different  projects   •  PaAent  and  diplomaAc  to  manage  a  diverse   group  of  data  scienAsts  and  business  owners   •  Understand  when  to  go  with  an  80/20   approach    
  23. 23. Data  ScienAsts:  The  Challenge  of  Managing   Stubbornly  Autonomous  Experts4     “I  no5ced  …  that  data   scien5sts,  but  also  sta5s5cians   and  top  coders,  oCen  have   difficul5es  accep5ng  orders   from  managers  who  don’t   have  technical  skills   themselves.”  -­‐  Istvan  Hajnal  
  24. 24. Journey  to  become  a  DS  Manager     Nate  Silver  on  Finding  a  Mentor,  Teaching   Yourself  StaAsAcs,  and  Not  Sesling  in  Your   Career5   •  Find  a  Mentor  (Yes,  even  if  you’re  already  a   senior  manager)   •  Teach  Yourself  (online  resources,  MOOCs)   •  Understand  the  life-­‐cycle  of  a  data-­‐driven   project   •  Just  do  it!  
  25. 25. Why  Just  Do  It?     Why  do  I  need  to  learn  about  data  science   and  manage  data  projects?     “I  have  [insert  #  of  years]  years  of   experience  in  [insert  my  industry].     I’m  comfortable  and  successful   being  a  [insert  your  Atle  here].”  
  26. 26. Company  Structures
  27. 27. Data  Sources Data  projects  are  lurking  everywhere  …  
  28. 28. Machine  Learning
  29. 29. Machine  Learning
  30. 30. Machine  Learning
  31. 31. Machine  Learning Google  X  laboratory5  
  32. 32. Machine  Learning Google  Research6  
  33. 33. Data  Science  Concepts PredicAve  AnalyAcs   ClassificaAon   RecommendaAon  Systems  
  34. 34. Big  Data  Technology
  35. 35. Topics  in  Cloud  CompuAng New  services  added:  
  36. 36. Your  Job:  Provide  Guidance Tell  us  a  data  story     …  about  your  business   Do  you  understand   the  outcome?     What  is  your   recommendaAon  to   the  business?  
  37. 37. Gezng  Started:  Locally Meetups   •  LA  R  users  group   •  LA  Machine  Learning   •  LA  Data  Warehouse,  BI  &  AnalyAcs   •  LA  Big  Data  Users  Group   Conferences:   •  datascience.la   •  bigdatadayla.org  
  38. 38. Gezng  Started:  Podcasts dataskepAc.com   thetalkingmachines.com  
  39. 39. Gezng  Started:  MOOCs
  40. 40. Good  Places  to  Start Data  Science  for  Business     by  Foster  Provost     &  Tom  Fawces  
  41. 41. Good  Places  to  Start Doing  Data  Science     by  Rachel  Schus  &  Cathy   O’Neil  (mathbabe.org)     Free  at   www.columbiadatascience.com  
  42. 42. Good  Places  to  Start The  Art  of  Data  Science       by  Roger  Peng  &  Elizabeth   Matsui     hsps://leanpub.com/artofdatascience  
  43. 43. Get  Kids  Started scratch.mit.edu   www.ixl.com  
  44. 44. Thank  You! Annie  Flippo   @ACflippo   Slides  are  available  at  goo.gl/1X2NMH  
  45. 45. References    1.  hsp://www.mckinsey.com/insights/business_technology/ big_data_the_next_fronAer_for_innovaAon   2.  hsp://www.forbes.com/sites/gilpress/2015/10/09/the-­‐hunt-­‐for-­‐unicorn-­‐ data-­‐scienAsts-­‐lims-­‐salaries-­‐for-­‐all-­‐data-­‐analyAcs-­‐professionals/   3.  hsp://cdn.oreillystaAc.com/en/assets/1/event/119/Data%20Science %20Bootcamp%20PresentaAon.pdf   4.  hsp://www.ibmbigdatahub.com/blog/data-­‐scienAsts-­‐challenge-­‐managing-­‐ stubbornly-­‐autonomous-­‐experts   5.  hsps://hbr.org/2013/09/nate-­‐silver-­‐on-­‐finding-­‐a-­‐mentor-­‐teaching-­‐yourself-­‐ staAsAcs-­‐and-­‐not-­‐sesling-­‐in-­‐your-­‐career/   6.  hsp://www.nyAmes.com/2012/06/26/technology/in-­‐a-­‐big-­‐network-­‐of-­‐ computers-­‐evidence-­‐of-­‐machine-­‐learning.html   7.  hsp://research.google.com/archive/unsupervised_icml2012.html  

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