Data science for product managers: What does it mean? What can it do for your product?
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Transcript of Data science for product managers: What does it mean? What can it do for your product?
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DATA SCIENCE FOR PRODUCT MANAGERSWhat does it mean? What can it do for your product?
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Alberto Vargas Talavera
phone: (512) 300-4975
blog: bit.ly/Azblog
twitter: @vargastalavera
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Big Data
Statistical Analysis
Smart Data Database Decision Making
Predic
tive
Analys
is
Business Intelligence
Data Mining
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DATA SCIENCE IS
The use of technology and data to help make better decisions.
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APPLIED DATA SCIENCE IS NOT NEW
Walter Shewhart
1920s
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WHAT HAS CHANGED?
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WHAT HAS CHANGED?
Explosion in the creation of data and the sources of data
Web and browsing dataSmart PhonesGPSVideoGenomicsGovernment Open DataDevices with sensorFinancial transactionseMail
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“… Just two days of the current global data production, from all sources — five
quintillion bytes — is about equal to the amount of information created by all the world’s conversations, ever …”
New York Times on research conducted by theUniversity of California, Berkeley.
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Data is not the answer … It is what leads you to the answer.
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Data Science
Objective is to Evaluate and Optimize
DesignPerformance
ToolsA/B TestingHypothesis TestingDescriptive statisticsCharts, graphs
Objective is to ForecastUser behaviorTastePreferences
ToolsPredictive ModelsRegressionDecision Trees
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DESIGN OPTIMIZATION A/B TESTINGOBAMA’S GOOGLE CONNECTION, DAN SIROKER
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THE WINNING DESIGN
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NETFLIX AND THE $1M CONTEST
Mission: Improve recommendation
engine by 10%
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NETFLIX AND THE $1M CONTEST
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TARGET STORES AND PREGNANCY
Mission: Find relevant messages for each customer
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TARGET
Predictive ModelDataAlberto Vargas
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Build a Data Science Strategy Framework
1. Define your objective
2. Define success
3. Define what data you need
4. Define data strategies for data collection
5. Analyze the data
6. Model the data
7. Verify the analysis and modeling
8. Determine how you will integrate results into your process or
decision making.
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The Framework – PM Domain
1. Define your objective
2. Define success
3. Define what data you need
4. Define data strategies for data collection
5. Analyze the data
6. Model the data
7. Verify the analysis and modeling
8. Determine how you will integrate results into your process or
decision making.
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THE FRAMEWORK – STEP 1
1. Define your objective
What is the problem? What do you want to
accomplish?
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THE FRAMEWORK – STEP 2
2. Define success How do you know that you
met your objective? How does success look
like? How do you measure it
quantitatively?
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THE FRAMEWORK – STEP 3
3. Determine what data you need to measure or collect
What variables, factors, or models support your objectives?
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THE FRAMEWORK – STEP 4
4. Define and carryout strategies to collect data
How do you get it?
Where do you get it?
When do you get it?
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THE FRAMEWORK – STEPS 5 - 7
Technical Skills Data Mining Statistics DOE Regression Analysis Advanced
mathematics
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THE FRAMEWORK – STEP 8
8. Define how results will be integrated into your process or decision making
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THE FRAMEWORK – SUMMARY
1. Define your objective
2. Define success
3. Define what data you need
4. Define data strategies for data collection
5. Analyze the data
6. Model the data
7. Verify the analysis and modeling
8. Determine how you will integrate results into your
process or decision making.
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THE FRAMEWORK – SUMMARY
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THE FUTURE
Quantify the unquantifiable. Measure the hard to measure
Integrate data from different sources
Expand machine learning
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RECOMMENDED READING
Video: The importance of A/B testing: Dan Siroker
http://www.youtube.com/watch?feature=player_embedded&v=yY-HBW5CNcg
Video: How Target gets the most out of it guest data, Andrew Pole
http://www.rmportal.performedia.com/node/1373
Book: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Eric Siegel, Thomas H. Davenport
Book: The Signal and the Noise: The Art and Science of Prediction
Nate Silver
Book: Moneyball: The Art of Winning an Unfair Game
Michael Lewis