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Presentation IT4BC 2012

Presentation IT4BC 2012

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  • 1. Let  your  data  free   Empowering  others  to  use  the  data  you  store  
  • 2. Big  Data  and  Data  Visualiza5on  as  seen  at  Google,  Facebook,  Ne=lix,   Yahoo  and  TwiCer.     The  future  of  Educa5onal   Technologies  is  here  
  • 3. My  big  data  journey   Happy  New  Year!  
  • 4. Why  Big  Data?  •  Spi<ng  out  a  log  that  my  monkey  ate  a   banana  is  interes@ng  •  Charts  and  plots  of  it,  cool  •  But  tell  me  why?  And  predict  the  next?   Supercool  
  • 5. Data  stored  on  your  iPhone  
  • 6. We  live  in  the  age  of  data  
  • 7. Your  next  job  will  depend  on  data  
  • 8. Your  life  will  depend  on  data  
  • 9. Big  Data    Data  Mining   Analy5cs  Visualiza5on  
  • 10. Anyone  using  Big  Data?  
  • 11. Google  Big  Data  
  • 12. Big  Data  Machine  Learning  •  Machine  learning  is  about  finding  paJerns  in   the  data  •  Machine  learning  is  about  finding  meaningful   informa@on  
  • 13. TwiCer  Big  Data  
  • 14. "Data  visualiza@on  is  the  last  mile  between  computers  and  our  brains."   —@Edd    
  • 15. Ne=lix  Big  Data  
  • 16. Facebook  Big  Data  
  • 17. My  toolbox  •  Data  manipula@on:  iPython,  Vim  •  Visualiza@ons:  Gephi,  Tableau  •  Sta@s@cal  Toolkit:  R  Studio  •  Version  Control:  git  
  • 18. I  dream  of  this  
  • 19. What  does  this  mean?  •  Student  #1,192,187   •  Student  #11,192,173  •  Student  #2,160,143   •  Student  #12,164,151  •  Student  #3,183,180   •  Student  #13,184,165  •  Student  #4,136,100   •  Student  #14,189,184  •  Student  #5,162,180   •  Student  #15,183,170  •  Student  #6,165,159   •  Student  #16,181,176  •  Student  #7,181,162   •  Student  #17,188,163  •  Student  #8,188,   •  Student  #18,191,185  •  Student  #9,150,146   •  Student  #19,190,175  •  Student  #10,163,159   •  Student  #20,184,171  
  • 20. Learning  about  Visualizing  Data  
  • 21. Uses  of  Big  Data  in  EDU  •  When  online  learning  systems  use  data  to   change  in  response  to  student  performance,   they  become  adap$ve  learning  environments  
  • 22. But  we  work  for  a  school!  
  • 23. Anyone  BI?  
  • 24. Educa@onal  data  mining  (EDM)    •  EDM  are  methods  in  sta@s@cs,  machine   learning,  and  data  mining  to  analyze  data  •  Data  that  is  collected  during  teaching  and   learning  
  • 25. Learning  analy@cs  •  Learning  analy@cs  applies  sociology,   psychology,  sta@s@cs,  computer  science   concepts  to  data  •  Learning  analy@cs  creates  applica@ons  that   directly  influence  educa@onal  prac@ce  
  • 26. What  is  big  data?  
  • 27. Hadoop•  Open-source framework for running applications on large clusters built of commodity hardware•  Distributed storage and OS•  Way bigger than traditional databases•  Petabytes vs gigabytes 82
  • 28. Yahoo  
  • 29. Learning  about  Data  Mining  
  • 30. What  is  data  mining?  Its  all  about  discovery:  •  Grouping  similar  data  •  Iden@fying  interes@ng/unique  data  •  Detec@ng  rela@onships  •  Discovering  previously  unknown  paJerns  
  • 31. Examples  of  Machine  Learning  •  SPAM  detec@on  •  Handwri@ng  •  Google  Streetview  •  Speech  recogni@on  •  Neilix  recommenda@on  •  Robo@c  naviga@on  
  • 32. Reasons  for  Analy@cs  •  Predict  the  future  •  Understand  Risk  and  Complexity  •  Embrace  complexity  •  Iden@fy  the  unusual  •  Think  beJer  
  • 33. Visualiza5ons  
  • 34. Dashboards  
  • 35. Big  Data  and  You  
  • 36. Example  Big  Data  Use  Cases   Data   High-­‐frequency   Lower-­‐frequency   Source   opera@ons   opera@ons   Write/index  all  trades,   Show  consolidated  risk   Capital  markets   store  @ck  data   across  traders   Call  ini@a@on  request   Real-­‐@me  authoriza@on   Fraud  detec@on/analysis   Inbound  HTTP   Visitor  logging,  analysis,   Traffic  paJern  analy@cs   requests   aler@ng   Rank  scores:   Online  game   • Defined  intervals   Leaderboard  lookups   • Player  “bests”   Real-­‐@me  ad  trading   Match  form  factor,   Report  ad  performance   systems   placement  criteria,  bid/ask   from  exhaust  stream   Mobile  device   Loca@on  updates,  QoS,   Analy@cs  on  transac@ons   loca@on  sensor   transac@ons  
  • 37. The  best  examples  
  • 38. Example  machine  learning  
  • 39. Examples  in  the  news  
  • 40. What  is  a  data  scien@st?  •  Person  who  understands  data-­‐driven  world  •  Person  who  can  make  sense  of  big  data  •  Person  who  has  tools,  skills  and  mindset  to   see  data  as  the  new  "oil"  fueling  a  company  •  Person  who  programs  •  Person  who  analyses  data  •  Person  who  visualizes  data    
  • 41. Big  data,  Analy5cs  and  Visualiza5on