Applied Data Science with Yhat

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Austin Ogilvie

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

Tecnologia

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

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)

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

Data science
in the real world.
regression

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

What makes building
analytical apps hard?

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

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

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...

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

use your tools move quickly
any workflow

use your tools move quickly
any workflow no translating

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

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

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

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Applied Data Science with Yhat

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?

13.

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