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10 Pitfalls in Data Science
Szilárd Pafka, PhDChief Scientist, Epoch
LA Machine Learning MeetupData Science TrackFeb 2014
About me
Data Science
(Some) Pitfalls
● DS = IT project
● DS isolated from business
● Restricted access to data
● Not enough EDA/cleaning
● Data leakage
● Overfitting
● Optimizing wrong metric
● Skip model validation
● Too complex to deploy
● Poor communication
Contact
[email removed from slideshare]
www.linkedin.com/in/szilard
