The document discusses the challenges and methodologies in data science beyond just leveraging big data, highlighting the importance of data cleanliness, accurate representation of populations, and statistical modeling. It emphasizes that big data serves as raw material that requires refinement through techniques such as surveys, experiments, and visualization. Data scientists should be aware of potential pitfalls like observational bias and overfitting while seeking statistical expertise to enhance their analyses.

1. Revolution Confidential
Data Science
Not just for big data!
David Smith
Revolution Analytics
@revodavid
October 16, 2013

2. Big Data: the new oil?
Photo: Sarah&Boston (flickr: pocheco) Creative Commons BY-SA 2.0
Revolution Confidential
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3. Big Data is just raw material
Revolution Confidential
 Data Distillation
 Extract quantities of interest
 Find complete cases
 Derive missing information
 Big Data Pitfalls:
 Data cleanliness & accuracy
 Observational bias
 Do the data I have represent the population I’m
interested in?
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4. Surveys & Experiments
Revolution Confidential
 Even with Big Data, the data you need isn’t
always in the building!
 … so ask (survey)!
 Survey design
 Stratified sampling
 … or experiment!
 A/B Testing
 Experimental Design
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5. Data Exploration & Visualization
Revolution Confidential
 Limited by pixels
 Big data = a big black
blob
 Extract signal from
noise
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Aggregations
Heat maps
Smoothing
Small multiples
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6. Statistical Modeling & Forecasting
Revolution Confidential
 You don’t always need big data
 Sampling can help with observational bias
 Model selection
 Feature extraction
 Confounding?
 Interactions?
 Model validation
 Overfitting
 Prediction
 Extrapolation
 Confidence
http://xkcd.com/605/
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7. Summary
Revolution Confidential
 Big Data is great, but think of it as the “raw
materials” for data science
 After refining, “big” isn’t always so “Big”
 Use statistical insight to avoid pitfalls:
 Inferences: Observational bias / Sampling bias
 Predictions: Confounding / Overfitting
 Think about variances and means (risk!)
 Some data scientists may miss these issues
 Look for statistical expertise
 Further reading:
 ComputerWorld: 12 predictive analytics screw-ups
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