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The Art of Data Science
- 1. THE ART OF DATA SCIENCE Josh Wills O P E N D A T A S C I E N C E C O N F E R E N C E_ BOSTON 2015 @opendatasci
- 2. 2© Cloudera, Inc. All rights reserved. Or, A Less Pretentious Title Josh Wills | Senior Director of Data Science The Art of Data Science
- 3. 3© Cloudera, Inc. All rights reserved. The Data Science State of the Union
- 4. 4© Cloudera, Inc. All rights reserved. Data Scientists At Work
- 5. 5© Cloudera, Inc. All rights reserved. Data Scientists at Home
- 6. 6© Cloudera, Inc. All rights reserved. Data Scientists…Everywhere?
- 7. 7© Cloudera, Inc. All rights reserved. Creating Some Definition
- 8. 8© Cloudera, Inc. All rights reserved. The Mismeasure of Data Scientists
- 9. 9© Cloudera, Inc. All rights reserved. The Tremendous Promise of Big Data
- 10. 10© Cloudera, Inc. All rights reserved. The Unfortunate Reality of Big Data
- 11. 11© Cloudera, Inc. All rights reserved. Like Urban Planning, but for Data
- 12. 12© Cloudera, Inc. All rights reserved. Data Modeling for Data Science
- 13. 13© Cloudera, Inc. All rights reserved. Event Series Analytics
- 14. 14© Cloudera, Inc. All rights reserved. A Simple Star Schema for Spell Correction
- 15. 15© Cloudera, Inc. All rights reserved. A Supernova Schema for Search
- 16. 16© Cloudera, Inc. All rights reserved. Spell Correction in SQL
- 17. 17© Cloudera, Inc. All rights reserved. The Operational/Analytical Impedance Mismatch
- 18. 18© Cloudera, Inc. All rights reserved. Exhibit: http://github.com/jwills/exhibit
- 19. 19© Cloudera, Inc. All rights reserved. Pushing Beyond The Limits of Our Tools
- 20. 20© Cloudera, Inc. All rights reserved. Thanks! jwills@cloudera.com
Notas do Editor
- Data cleansing, preparation, feature engineering. The dirty work we do all day at the insight factory.
- Does everyone have to be a data scientist? Are there tools that can make anyone into a data scientist?
- http://www.quora.com/What-is-the-difference-between-a-data-scientist-and-a-statistician
- Data scientists can do two things better than data analysts: ask great questions and answer them faster than other people would think possible.
- Everyone gets a Ferrari!
- Oh no! Everyone has a Ferrari! Induced demand: as you increase the supply of something, the demand for it increases as well.
- We need the equivalent of public transit infrastructure for analytic queries: low marginal cost for asking one more question, goes the places most people need to go, removes load from the roadways.
- The spell correction example as a model for what the public transit infrastructure should look like.
- Can we create a data model that makes this kind of powerful analysis available to people who only know SQL?
- Even better, can a common data model enable us to seamlessly move models from the offline, analytical world to the online, operational world? Because the supernova data model is essentially the HBase/Cassandra/Mongo/etc. data model.
- http://github.com/jwills/exhibit
- No tool can make you a data scientist, because it’s the ability to push beyond the limits of your tools that makes you a data scientist.