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Data Driven Action : A Primer on Data Science

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Approaches and Open Source Tools for Wrangling and Modeling Massive Datasets (Sarah Aerni)
Text Analytics at Scale on MPP (Srivatsan Ramanujam)
A Scalable Framework For Real Time Monitoring & Prediction Of Sensor Data (Jarrod Vawdrey)

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Data Driven Action : A Primer on Data Science

  1. 1. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ SPRINGONE2GX WASHINGTON, DC Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attributio n-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Data Driven Action: A Primer on Data Science Sarah Aerni (@iTweetSarah) Srivatsan Ramanujam (@being_bayesian) Jarrod Vawdrey (@jjvawdrey)
  2. 2. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Agenda • Approaches and Open Source Tools for Wrangling and Modeling Massive Datasets • Sarah Aerni • Text Analytics at Scale on MPP • Srivatsan Ramanujam • A Scalable Framework For Real Time Monitoring & Prediction Of Sensor Data • Jarrod Vawdrey 2
  3. 3. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Our everyday devices are smart and talk to us
  4. 4. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Our everyday devices are smart and talk to us
  5. 5. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Connected devices take action to make daily life easier. But what else?
  6. 6. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How can IoT help prevent accidents like the Macondo Disaster ?
  7. 7. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Gene Sequencing Smart Grids COST TO SEQUENCE ONE GENOME HAS FALLEN FROM $100M IN 2001 TO $10K IN 2011 TO $1K IN 2014 READING SMART METERS EVERY 15 MINUTES IS 3000X MORE DATA INTENSIVE Stock Market Social Media FACEBOOK UPLOADS 250 MILLION PHOTOS EACH DAY In all industries billions of data points represent opportunities for the Internet of Things Oil Exploration Video Surveillance OIL RIGS GENERATE 25000 DATA POINTS PER SECOND Medical Imaging Mobile Sensors
  8. 8. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Smart Systems = Sensors + Digital Brain + Actuators Problem Formulation Modeling Step Data Step Application Step Data Science for Building Models Sensors & Actuators Data Lake
  9. 9. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How can data drive true, automated action? How does this…
  10. 10. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How can data drive true, automated action? …become this? How does this…
  11. 11. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How can data drive true, automated action? • How is data collected? • Where is it stored and processed? • Is there real signal or just noise? • How can we build a predictive model? • When is the right time to take action?
  12. 12. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Critical considerations for successful modeling How to build a predictive model at scale Data-driven paradigms, data cleansing and feature engineering
  13. 13. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Critical considerations for successful modeling How to build a predictive model at scale Data-driven paradigms, data cleansing and feature engineering Tradeoffs between model accuracy and timeliness
  14. 14. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Critical considerations for successful modeling How to build a predictive model at scale Data-driven paradigms, data cleansing and feature engineering Derive insight from models to change processes Tradeoffs between model accuracy and timeliness
  15. 15. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Critical considerations for successful modeling How to build a predictive model at scale Data-driven paradigms, data cleansing and feature engineering Use Cases Oil Drilling Vaccine Manufacturing Derive insight from models to change processes Tradeoffs between model accuracy and timeliness Treating Patients
  16. 16. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ Drilling into the San Andreas Fault at Parkfield California. Credit: Stephen H. Hickman, USGS Data: The New Oil • Oil & gas generates large amounts of data from sensors enabling data- driven approaches to improve operations Predictive maintenance • Motivation: Failure costs estimated at $150,000/incident (billions annually)* • Goals – Early warning system – Insights into prominent features impacting operation and failure – Reduction of non-productive drill time – Reduced incidents *http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry
  17. 17. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How are models built using sensor data? Integrating & Cleansing Feature Building Modeling
  18. 18. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How are models built using sensor data? Integrating & Cleansing Feature Building Modeling Integrated Data Operator Data ( thousands of records ) • Failure details • Component details • Drill Bit details Drill Rig Sensor Data ( billions of records ) • Rate of Penetration (ROP) • RPM • Weight on Bit (WOB) Primary data sources
  19. 19. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How are models built using sensor data? Integrating & Cleansing Operator Data ( thousands of records ) • Failure details • Component details • Drill Bit details Drill Rig Sensor Data ( billions of records ) • Rate of Penetration (ROP) • RPM • Weight on Bit (WOB) Primary data sources ROP Time
  20. 20. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How are models built using sensor data? Integrating & Cleansing Operator Data ( thousands of records ) • Failure details • Component details • Drill Bit details Drill Rig Sensor Data ( billions of records ) • Rate of Penetration (ROP) • RPM • Weight on Bit (WOB) Primary data sources ROP Time Drill bit changes
  21. 21. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How are models built using sensor data? 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  22. 22. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How are models built using sensor data? Operator Data ( thousands of records ) • Failure details • Component details • Drill Bit details Drill Rig Sensor Data ( billions of records ) • Rate of Penetration (ROP) • RPM • Weight on Bit (WOB) Primary data sources WOB Time Integrating & Cleansing
  23. 23. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How are models built using sensor data? Operator Data ( thousands of records ) • Failure details • Component details • Drill Bit details Drill Rig Sensor Data ( billions of records ) • Rate of Penetration (ROP) • RPM • Weight on Bit (WOB) Primary data sources WOB Time Integrating & Cleansing ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● 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  24. 24. Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/ How are models built using sensor data? Operator Data ( thousands of records ) • Failure details • Component details • Drill Bit details Drill Rig Sensor Data ( billions of records ) • Rate of Penetration (ROP) • RPM • Weight on Bit (WOB) Primary data sources WOB Time Integrating & Cleansing ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 00:00 10:00 20:00 30:00 40:00 50:00 00:00 101520 df$ts_utc df$wob ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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