Yhat at the San Francisco Data Science Meetup (02/26/2014)

1. Applied Data Science with
Yhat
SF Data Science Meetup
Feb 26, 2014

2. 1) Intro (1 min)
2) The Problem (3 mins)
3) Case Study: Beer Recommender (5 mins)
4) Demo (3 min)
5) Q/A (5 min)

3. Founders Company
Investors
Greg Lamp, CTO
Austin Ogilvie, CEO ● Launched in 2013
● HQ in Brooklyn

5. Data science
in the real world.
regression

6. Get Raw Data
Strategic
Insights
Real World
Scoring
Data Driven Products
Business Impact
Clean Data
Stages of the Analytics Project Life Cycle
Expert data teams
Management
Customers & Front Line Employees

7. What makes building
analytical apps hard?

8. Hi, I’m Trey.
Meet Trey, the Data Scientist

9. We need to reduce
churn. Okay. I'll look into it.

10. I figured out that....some
complex stuff about
vector space that'll
improve...
....and that's how we'll reduce churn.
Sounds good.
Let's do that...

11. Any of you know
what Gradient
Boosting is?
So when can we go
live with the new
model?

12. Now what?

14. use your tools

15. use your tools move quickly

16. use your tools move quickly
any workflow

17. use your tools move quickly
any workflow no translating

18. Case Study

19. + = ?

20. A Beer Recommender in Python

21. http://beers.yhathq.com

22. The Data

23. http://snap.stanford.edu/data/web-BeerAdvocate.html

34. Beers

35. Users

36. Ratings

37. Distance

38. vs

44. vs

46. calculating distance

47. eeny
? ?

48. eeny meeny
?

49. ?
Cosine
eeny meeny miny

50. ?
Cosine
moe

51. pick one.
you can always
change

54. Thank you,

59. Scoring

61. Aggregate

62. Sort

63. Filter

64. Return

66. Deployment

67. What does this mean?

69. Import Yhat

70. Create a YhatModel

71. Define execute

72. Grab incoming data

73. Call your function

74. Format and return results

78. Demo
http://cloud.yhathq.com/
http://beers.yhathq.com/

80. deploy your own
IPython Notebook

81. Thanks!
@yhathq
greg@yhathq.com
yhathq.com

82. Questions?