Revolution Confidential

Data ScienceNot just for big

data!David SmithRevolution Analytics@revodavid

October 16, 2013

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Big Data: the new oil?

Photo: Sarah&Boston (flickr: pocheco) Creative Commons BY-SA 2.0

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Big Data is just raw material

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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Surveys & Experiments

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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Data Exploration & Visualization

Limited by pixels Big data = a big black

blob Extract signal from

noise Aggregations Heat maps Smoothing Small multiples

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Statistical Modeling & Forecasting

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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Summary

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