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