How to submit a standout Adobe Champion Application
Yhat - Applied Data Science - Feb 2016
1. Applied Data Science
Making insights accessible and actionable
PRESENTED BY
Colin Ristig
Product Manager
colin@yhathq.com
Austin Ogilvie
Founder & CEO
a@yhathq.com
2. Agenda
Quick Intro to Data Science
Understanding the Value Chain
Designing Your Data Science Process
8. Data Science in 30 Seconds
Broadly…
A multidisciplinary field concerning
problem solving using data,
statistics & software.
9. “ What distinguishes data science itself from
the tools and techniques is the central goal
of deploying effective decision-making
models to a production environment. ”
Data Science is not “Interesting Research”
~ Nina Zumel & John Mount, Practical Data Science with R
12. Explanation isn’t always important
Carl wants to watch
a good movie.
Carl
Cindy
http://courses.washington.edu/css490/2012.Winter/lecture_slides/08b_collaborative_filtering_1_r1.pdf
Carl would like Frozen
because Cindy liked it.
18. Hey, Trey. Online sales
are down. What can
we do to keep users
engaged and shopping
carts full?
Trey is asked to “look into something”
I’ll look into it.
19. Hm...cool. Can
you talk to the
dev team?
Here’s what
we should do:
Trey uncovers a bunch of things we didn’t know
21. “Throw it over the wall” projects
Execs Data Science Application Developers
22. Common reasons these types of projects stall
- Unclear benefits
- Skepticism about effectiveness
- Too complex to operationalize
- Too time-consuming
- Unclear how to measure ROI
24. Making data valuable
Collect and display individual records
Structure, link,
metadata, interact, share
Understand,
infer, learn
Drive
value,
change
Clean, aggregate, visualize
Actions
Predictions
Reports
Charts
Records
Extracting value
from data is like any
other value chain.
Value
25. Like a raw material,
data has no obvious
utility to start out.
Collect and display individual records
Structure, link,
metadata, interact, share
Understand,
infer, learn
Drive
value,
change
Clean, aggregate, visualize
Actions
Predictions
Reports
Charts
Records
Value
Making data valuable
26. We make it valuable
through sequential
refinement.
Collect and display individual records
Structure, link,
metadata, interact, share
Understand,
infer, learn
Drive
value,
change
Clean, aggregate, visualize
Actions
Predictions
Reports
Charts
Records
Value
Making data valuable
27. Cost of Creating that Value
Building data products requires lots of work
28. Cost of Creating that Value
But most of the value is generated at the end
29. Cost of Creating that Value
Data Teams
Managers
Customers
Everyone has to see past a lot of challenges
36. 1. Focus on the customer
5 Attributes of Successful Data Science Teams
37. 1. Focus on the customer
2. Identify practical constraints
5 Attributes of Successful Data Science Teams
38. 1. Focus on the customer
2. Identify practical constraints
3. Start small but ship quickly
5 Attributes of Successful Data Science Teams
39. 1. Focus on the customer
2. Identify practical constraints
3. Start small but ship quickly
4. Measure the impact
5 Attributes of Successful Data Science Teams
40. 1. Focus on the customer
2. Identify practical constraints
3. Start small but ship quickly
4. Measure the impact
5. Relentless iteration
5 Attributes of Successful Data Science Teams
41. 1. Focus on the customer
2. Identify practical constraints
3. Start small but ship quickly
4. Measure the impact
5. Relentless iteration
5 Attributes of Successful Data Science Teams