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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?
Now what?
use your tools
use your tools move quickly
use your tools move quickly
any workflow
use your tools move quickly
any workflow no translating
Case Study
+ = ?
A Beer Recommender in Python
http://beers.yhathq.com
The Data
http://snap.stanford.edu/data/web-BeerAdvocate.html
Beers
Users
Ratings
Distance
vs
vs
calculating distance
eeny
? ?
eeny meeny
?
?
Cosine
eeny meeny miny
?
Cosine
moe
pick one.
you can always
change
Thank you,
Scoring
Aggregate
Sort
Filter
Return
Deployment
What does this mean?
Import Yhat
Create a YhatModel
Define execute
Grab incoming data
Call your function
Format and return results
Demo
http://cloud.yhathq.com/
http://beers.yhathq.com/
deploy your own
IPython Notebook
Thanks!
@yhathq
greg@yhathq.com
yhathq.com
Questions?

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