yhat - applied data science - feb 2016
TRANSCRIPT
Applied Data ScienceMaking insights accessible and actionable
PRESENTED BY
Colin RistigProduct [email protected]
Austin OgilvieFounder & [email protected]
Agenda
Quick Intro to Data Science
Understanding the Value Chain
Designing Your Data Science Process
About Us
We help data scientists build & deploy apps
Founded 2013Headquarters in NYC
You may know us from
Data Sciencein 30 seconds
Data Science in 30 Seconds
Broadly…
A multidisciplinary field concerning
problem solving using data,
statistics & software.
“ 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
It’s about day-to-day problems
Carl wants to watch a good movie.
And practical, real-world solutions
Carl wants to watch a good movie.
Hey, Carl. Check these out!
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.
Data ScienceChallenges
30%
Why?
Key obstacles data science teams face
Lack of Understanding
Key obstacles data science teams face
Difficulty of Experimentation
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.
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
Trey hands his work to deployment engineers
“Throw it over the wall” projects
Execs Data Science Application Developers
Common reasons these types of projects stall
- Unclear benefits- Skepticism about effectiveness- Too complex to operationalize- Too time-consuming- Unclear how to measure ROI
Data ScienceValue Chain
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
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
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
Cost of Creating that Value
Building data products requires lots of work
Cost of Creating that Value
But most of the value is generated at the end
Cost of Creating that Value
Data Teams
Managers
Customers
Everyone has to see past a lot of challenges
DataScienceCustomers
- Consumers
Several types of customers
Carl wants to watch a good movie.
- Consumers- App Developers
Cambria needs to call credit models from Salesforce.
Several types of customers
Douglas needs 3 AM server outages to stop.
Several types of customers
- Consumers- App Developers- Infrastructure Admins
Gordon wants sales reps calling the hottest leads.
Several types of customers
- Consumers- App Developers- Infrastructure Admins- Sales & Marketing
DataScience5 Attributes for Success
1. Focus on the customer
5 Attributes of Successful Data Science Teams
1. Focus on the customer2. Identify practical constraints
5 Attributes of Successful Data Science Teams
1. Focus on the customer2. Identify practical constraints3. Start small but ship quickly
5 Attributes of Successful Data Science Teams
1. Focus on the customer2. Identify practical constraints3. Start small but ship quickly4. Measure the impact
5 Attributes of Successful Data Science Teams
1. Focus on the customer2. Identify practical constraints3. Start small but ship quickly4. Measure the impact5. Relentless iteration
5 Attributes of Successful Data Science Teams
1. Focus on the customer2. Identify practical constraints3. Start small but ship quickly4. Measure the impact5. Relentless iteration
5 Attributes of Successful Data Science Teams
Demo
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
Trey hands his work to deployment engineers
“Throw it over the wall” projects
Data Science Application Developers
Deploy Models Faster
Data Science Application Developers
