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Practical problem solving with data involves more than just visualization or applying the latest machine learning techniques. Intuition, domain knowledge, and reasonable approximations can mean the difference between a successful model and a catastrophic failure. Good problem solvers deeply analyze available data, improvise solutions using their unique assets, anticipate outcomes and issues, and adapt their techniques over time.
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Practical Problem Solving With Data
Pete Skomoroch@peteskomoroch
Onlab Data ConferenceJune 22, 2012
To solve hard problems:
Think like a street fighter
Analyze
Improvise
Anticipate
Adapt
How does this apply to startups?
Analyze
The Men Who Stare at Charts
Look at your data
Averages Suck
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Build a Viewer App
Algorithmic Intuition
Improvise
Extract
Anticipate
Use Your Viewer App
Adapt
Look at your errors
• Sanity check row counts
• Track errors over time
• Find patterns in the error data
• Add missing features to models
• Replace models entirely
Analyze
Improvise
Anticipate
Adapt
Think like a street fighter
Pete Skomoroch@peteskomoroch
Onlab Data ConferenceJune 22, 2012
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