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What	
  Managers	
  
Need	
  to	
  Know	
  
about	
  Data	
  Science	
  
Annie	
  Flippo	
  
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
Who	
  am	
  I?	
  
Annie Flippo
Data Scientist
Software Engineer
Product Manager
Database Developer
What	
  is	
  Data	
  Science?	
  
Usage	
  of	
  Data	
  Science	
  
Finance:	
  fraud	
  detecAon,	
  score	
  
buying	
  habits,	
  calculate	
  risks	
  
Insurance:	
  inspect	
  driving	
  habits,	
  
assess	
  risks,	
  determine	
  premiums	
  
Usage	
  of	
  Data	
  Science	
  
Biometrics:	
  wearable	
  devices	
  to	
  
monitor	
  and	
  improve	
  health	
  
Digital	
  MarkeAng:	
  recommender	
  
systems,	
  audience	
  segmentaAon,	
  
retargeAng,	
  churn	
  predicAon	
  
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	
  
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	
  
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”	
  
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	
  
Because	
  it’s	
  a	
  
growing	
  and	
  
exciAng	
  field	
  
with	
  high	
  
compensaAon!	
  
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
Massive	
  amount	
  of	
  data
Streaming	
  into	
  your	
  company	
  …	
  
What	
  is	
  data?
It	
  can	
  be	
  raw	
  web	
  traffic	
  logs	
  …	
  
What	
  is	
  data?
Semi-­‐structured	
  data	
  from	
  APIs	
  …	
  
What	
  is	
  data?
Or,	
  structured	
  data	
  from	
  databases…	
  
…	
  what	
  to	
  do	
  with	
  all	
  this	
  data?	
  
The	
  Data	
  ScienAst
Can	
  wrangle	
  
data	
  from	
  
many	
  
sources	
  or	
  
formats	
  
The	
  Data	
  ScienAst
do	
  deep	
  data	
  
exploraAons	
  …	
  
and	
  perform	
  
thorough	
  analyses	
  	
  
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.	
  
The	
  OpAmal	
  Data	
  ScienAst
Is	
  a	
  person	
  with	
  deep	
  staAsAcal	
  and	
  machine	
  
learning	
  knowledge,	
  extensive	
  somware	
  
engineering	
  skills	
  and	
  well-­‐versed	
  in	
  business	
  
strategy!	
  
The	
  OpAmal	
  Data	
  ScienAst	
  –	
  Take	
  2
Personality	
  Traits3	
  
•  Compulsive	
  
•  Propulsive	
  laziness	
  
•  Drive	
  to	
  create	
  and	
  learn	
  
•  Irritable	
  determinaAon	
  
•  InsensiAvity	
  to	
  pain	
  (hmm…)	
  
•  Integrity	
  
•  Humility	
  
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	
  	
  
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	
  
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!	
  
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].”	
  
Company	
  Structures
Data	
  Sources
Data	
  projects	
  are	
  lurking	
  everywhere	
  …	
  
Machine	
  Learning
Machine	
  Learning
Machine	
  Learning
Machine	
  Learning
Google	
  X	
  laboratory5	
  
Machine	
  Learning
Google	
  Research6	
  
Data	
  Science	
  Concepts
PredicAve	
  AnalyAcs	
  
ClassificaAon	
  
RecommendaAon	
  Systems	
  
Big	
  Data	
  Technology
Topics	
  in	
  Cloud	
  CompuAng
New	
  services	
  added:	
  
Your	
  Job:	
  Provide	
  Guidance
Tell	
  us	
  a	
  data	
  story	
  	
  
…	
  about	
  your	
  business	
  
Do	
  you	
  understand	
  
the	
  outcome?	
  
	
  
What	
  is	
  your	
  
recommendaAon	
  to	
  
the	
  business?	
  
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	
  
Gezng	
  Started:	
  Podcasts
dataskepAc.com	
   thetalkingmachines.com	
  
Gezng	
  Started:	
  MOOCs
Good	
  Places	
  to	
  Start
Data	
  Science	
  for	
  Business	
  
	
  
by	
  Foster	
  Provost	
  	
  
&	
  Tom	
  Fawces	
  
Good	
  Places	
  to	
  Start
Doing	
  Data	
  Science	
  
	
  
by	
  Rachel	
  Schus	
  &	
  Cathy	
  
O’Neil	
  (mathbabe.org)	
  
	
  
Free	
  at	
  
www.columbiadatascience.com	
  
Good	
  Places	
  to	
  Start
The	
  Art	
  of	
  Data	
  Science	
  	
  
	
  
by	
  Roger	
  Peng	
  &	
  Elizabeth	
  
Matsui	
  
	
  
hsps://leanpub.com/artofdatascience	
  
Get	
  Kids	
  Started
scratch.mit.edu	
   www.ixl.com	
  
Thank	
  You!
Annie	
  Flippo	
   @ACflippo	
  
Slides	
  are	
  available	
  at	
  goo.gl/1X2NMH	
  
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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What Managers Need to Know about Data Science

  • 1. What  Managers   Need  to  Know   about  Data  Science   Annie  Flippo  
  • 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. Who  am  I?   Annie Flippo Data Scientist Software Engineer Product Manager Database Developer
  • 4. What  is  Data  Science?  
  • 5. Usage  of  Data  Science   Finance:  fraud  detecAon,  score   buying  habits,  calculate  risks   Insurance:  inspect  driving  habits,   assess  risks,  determine  premiums  
  • 6. Usage  of  Data  Science   Biometrics:  wearable  devices  to   monitor  and  improve  health   Digital  MarkeAng:  recommender   systems,  audience  segmentaAon,   retargeAng,  churn  predicAon  
  • 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. 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. 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. 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. Because  it’s  a   growing  and   exciAng  field   with  high   compensaAon!  
  • 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. Massive  amount  of  data Streaming  into  your  company  …  
  • 14. What  is  data? It  can  be  raw  web  traffic  logs  …  
  • 15. What  is  data? Semi-­‐structured  data  from  APIs  …  
  • 16. What  is  data? Or,  structured  data  from  databases…   …  what  to  do  with  all  this  data?  
  • 17. The  Data  ScienAst Can  wrangle   data  from   many   sources  or   formats  
  • 18. The  Data  ScienAst do  deep  data   exploraAons  …   and  perform   thorough  analyses    
  • 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. 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. 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. 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. 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. 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. 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].”  
  • 27. Data  Sources Data  projects  are  lurking  everywhere  …  
  • 31. Machine  Learning Google  X  laboratory5  
  • 33. Data  Science  Concepts PredicAve  AnalyAcs   ClassificaAon   RecommendaAon  Systems  
  • 35. Topics  in  Cloud  CompuAng New  services  added:  
  • 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. 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. Gezng  Started:  Podcasts dataskepAc.com   thetalkingmachines.com  
  • 40. Good  Places  to  Start Data  Science  for  Business     by  Foster  Provost     &  Tom  Fawces  
  • 41. Good  Places  to  Start Doing  Data  Science     by  Rachel  Schus  &  Cathy   O’Neil  (mathbabe.org)     Free  at   www.columbiadatascience.com  
  • 42. Good  Places  to  Start The  Art  of  Data  Science       by  Roger  Peng  &  Elizabeth   Matsui     hsps://leanpub.com/artofdatascience  
  • 44. Thank  You! Annie  Flippo   @ACflippo   Slides  are  available  at  goo.gl/1X2NMH  
  • 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