Big Data 2.0
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The document discusses the rise of big data and machine intelligence, emphasizing its disruptive nature and the challenges of effectively utilizing it. It highlights the importance of data science for business, predictive applications, and the need for talent in machine learning to harness the potential of big data. Additionally, it mentions the accessibility of machine learning as a critical factor for widespread adoption.
Big Data 2.0
- 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
- 14. “big”
- 16. “Ce n’est pas la taille qui compte…”
- 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)