Le Big Data entre dans une nouvelle phase où le prédictif est roi. Plutôt que de chercher à collecter une big quantité de données, on se concentre maintenant sur comment utiliser les données de façon à avoir un big impact. D'autant plus que les technologies Big Data deviennent désormais accessibles à des experts métiers qui peuvent les mettre à profit dans leurs domaines respectifs. Présentation donnée le 11 juin 2014 au Node à Bordeaux lors de la 1ère #datanight.

1. # D A T A
N I G H T

2. –Eric Schmidt
“The biggest disruptor that we’re sure
about is the arrival of big data and
machine intelligence everywhere.”

3. –Someone posted this slide on Twitter
“Big data is like teenage sex:
everyone talks about it, nobody really
knows how to do it, everyone thinks
everyone else is doing it, so everyone
claims they are doing it…”

4. “big data”

5. attribut 1 attribut 2 attribut 3
client 1
client 2
client 3

6. attribut 1 attribut 2 attribut 3 …
client 1
client 2
client 3
…

7. “open data”

8. nom latitude longitude
station 1
station 2
station 3
…

9. Big Data 1.0

10. collecter
stocker
traiter
visualiser

12. collecter
stocker
traiter
visualiser

13. collecter!
stocker
traiter
visualiser

14. “big”

16. “Ce n’est pas la taille qui compte…”

18. collecter
stocker
traiter
visualiser
comprendre

19. Minutes Textos Achats Data Age Churn?
148 72 0 33.6 50 TRUE
85 66 0 26.6 31 FALSE
183 64 0 23.3 32 TRUE
89 66 9.4 28.1 21 FALSE
115 0 0 35.3 29 FALSE
166 72 17.5 25.8 51 TRUE
100 0 0 30 32 TRUE
118 84 23 45.8 31 TRUE
171 110 24 45.4 54 TRUE
159 64 0 27.4 40 FALSE

20. Big Data 2.0

21. –Data Science for Business
Once firms have become capable of
processing massive data in a flexible
fashion, they should begin asking: “What
can I now do that I couldn’t do before, or
do better than I could do before?”

22. –Waqar Hasan, Apigee Insights
“Predictive is the ‘killer app’ for big data.”

23. –Mike Gualtieri, Principal Analyst at Forrester
“Predictive apps are the next big thing in
app development.”

25. • “Quel est le sentiment de ce tweet?”
• “Ce client va-t’il nous quitter dans le mois qui
vient?”
• “Cet email est-il du spam?”
=> classification

27. • “Combien vaut cette maison?”
=> régression

28. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($)
3 1 860 1950 house 565,000
3 1 1012 1951 house
2 1.5 968 1976 townhouse 447,000
4 1315 1950 house 648,000
3 2 1599 1964 house
3 2 987 1951 townhouse 790,000
1 1 530 2007 condo 122,000
4 2 1574 1964 house 835,000
4 2001 house 855,000
3 2.5 1472 2005 house
4 3.5 1714 2005 townhouse
2 2 1113 1999 condo
1 769 1999 condo 315,000

29. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($)
3 1 860 1950 house 565,000
3 1 1012 1951 house
2 1.5 968 1976 townhouse 447,000
4 1315 1950 house 648,000
3 2 1599 1964 house
3 2 987 1951 townhouse 790,000
1 1 530 2007 condo 122,000
4 2 1574 1964 house 835,000
4 2001 house 855,000
3 2.5 1472 2005 house
4 3.5 1714 2005 townhouse
2 2 1113 1999 condo
1 769 1999 condo 315,000

31. Bedrooms Bathrooms Surface (foot²) Year built Type Price ($)
3 1 860 1950 house 565,000
3 1 1012 1951 house
2 1.5 968 1976 townhouse 447,000
4 1315 1950 house 648,000
3 2 1599 1964 house
3 2 987 1951 townhouse 790,000
1 1 530 2007 condo 122,000
4 2 1574 1964 house 835,000
4 2001 house 855,000
3 2.5 1472 2005 house
4 3.5 1714 2005 townhouse
2 2 1113 1999 condo
1 769 1999 condo 315,000

32. Machine Learning

34. ??

35. –McKinsey & Co.
“A significant constraint on realizing value
from big data will be a shortage of talent,
particularly of people with deep expertise
in statistics and machine learning.”

42. HTML / CSS / JavaScript

43. HTML / CSS / JavaScript

44. squarespace.com

47. The two phases of machine learning:
• TRAIN a model
• PREDICT with a model

48. The two methods of prediction APIs:
• TRAIN a model
• PREDICT with a model

49. The two methods of prediction APIs:
• model = create_model(dataset)!
• predicted_output
= create_prediction(model, new_input)

51. Talk Text Purchase
s
Data Age Churn?
148 72 0 33.6 50 TRUE
85 66 0 26.6 31 FALSE
183 64 0 23.3 32 TRUE
89 66 94 28.1 21 FALSE
115 0 0 35.3 29 FALSE
166 72 175 25.8 51 TRUE
100 0 0 30 32 TRUE
118 84 230 45.8 31 TRUE
171 110 240 45.4 54 TRUE
159 64 0 27.4 40 FALSE
click me

53. –Bret Victor
"Until machine learning is as accessible
and effortless as the word ‘learn,’ it will
never become widespread."

54. –Dr Kiri L. Wagstaff, Researcher at NASA
“If we can get usable, flexible, dependable
machine learning software into the hands
of domain experts, benefits to society are
bound to follow.”

57. !
• model = create_model(dataset)!
• predicted_output
= create_prediction(model, new_input)

58. www.louisdorard.com