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Yhat - Applied Data Science - Feb 2016

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Yhat - Applied Data Science - Feb 2016

  1. 1. Applied Data Science Making insights accessible and actionable PRESENTED BY Colin Ristig Product Manager colin@yhathq.com Austin Ogilvie Founder & CEO a@yhathq.com
  2. 2. Agenda Quick Intro to Data Science Understanding the Value Chain Designing Your Data Science Process
  3. 3. About Us
  4. 4. We help data scientists build & deploy apps
  5. 5. Founded 2013 Headquarters in NYC
  6. 6. You may know us from
  7. 7. Data Science in 30 seconds
  8. 8. Data Science in 30 Seconds Broadly… A multidisciplinary field concerning problem solving using data, statistics & software.
  9. 9. “ What distinguishes data science itself from the tools and techniques is the central goal of deploying effective decision-making models to a production environment. ” Data Science is not “Interesting Research” ~ Nina Zumel & John Mount, Practical Data Science with R
  10. 10. It’s about day-to-day problems Carl wants to watch a good movie.
  11. 11. And practical, real-world solutions Carl wants to watch a good movie. Hey, Carl. Check these out!
  12. 12. Explanation isn’t always important Carl wants to watch a good movie. Carl Cindy http://courses.washington.edu/css490/2012.Winter/lecture_slides/08b_collaborative_filtering_1_r1.pdf Carl would like Frozen because Cindy liked it.
  13. 13. Data Science Challenges
  14. 14. 30%
  15. 15. Why?
  16. 16. Key obstacles data science teams face Lack of Understanding
  17. 17. Key obstacles data science teams face Difficulty of Experimentation
  18. 18. Hey, Trey. Online sales are down. What can we do to keep users engaged and shopping carts full? Trey is asked to “look into something” I’ll look into it.
  19. 19. Hm...cool. Can you talk to the dev team? Here’s what we should do: Trey uncovers a bunch of things we didn’t know
  20. 20. Trey hands his work to deployment engineers
  21. 21. “Throw it over the wall” projects Execs Data Science Application Developers
  22. 22. Common reasons these types of projects stall - Unclear benefits - Skepticism about effectiveness - Too complex to operationalize - Too time-consuming - Unclear how to measure ROI
  23. 23. Data Science Value Chain
  24. 24. Making data valuable Collect and display individual records Structure, link, metadata, interact, share Understand, infer, learn Drive value, change Clean, aggregate, visualize Actions Predictions Reports Charts Records Extracting value from data is like any other value chain. Value
  25. 25. Like a raw material, data has no obvious utility to start out. Collect and display individual records Structure, link, metadata, interact, share Understand, infer, learn Drive value, change Clean, aggregate, visualize Actions Predictions Reports Charts Records Value Making data valuable
  26. 26. We make it valuable through sequential refinement. Collect and display individual records Structure, link, metadata, interact, share Understand, infer, learn Drive value, change Clean, aggregate, visualize Actions Predictions Reports Charts Records Value Making data valuable
  27. 27. Cost of Creating that Value Building data products requires lots of work
  28. 28. Cost of Creating that Value But most of the value is generated at the end
  29. 29. Cost of Creating that Value Data Teams Managers Customers Everyone has to see past a lot of challenges
  30. 30. Data Science Customers
  31. 31. - Consumers Several types of customers Carl wants to watch a good movie.
  32. 32. - Consumers - App Developers Cambria needs to call credit models from Salesforce. Several types of customers
  33. 33. Douglas needs 3 AM server outages to stop. Several types of customers - Consumers - App Developers - Infrastructure Admins
  34. 34. Gordon wants sales reps calling the hottest leads. Several types of customers - Consumers - App Developers - Infrastructure Admins - Sales & Marketing
  35. 35. Data Science 5 Attributes for Success
  36. 36. 1. Focus on the customer 5 Attributes of Successful Data Science Teams
  37. 37. 1. Focus on the customer 2. Identify practical constraints 5 Attributes of Successful Data Science Teams
  38. 38. 1. Focus on the customer 2. Identify practical constraints 3. Start small but ship quickly 5 Attributes of Successful Data Science Teams
  39. 39. 1. Focus on the customer 2. Identify practical constraints 3. Start small but ship quickly 4. Measure the impact 5 Attributes of Successful Data Science Teams
  40. 40. 1. Focus on the customer 2. Identify practical constraints 3. Start small but ship quickly 4. Measure the impact 5. Relentless iteration 5 Attributes of Successful Data Science Teams
  41. 41. 1. Focus on the customer 2. Identify practical constraints 3. Start small but ship quickly 4. Measure the impact 5. Relentless iteration 5 Attributes of Successful Data Science Teams
  42. 42. Demo
  43. 43. Hm...cool. Can you talk to the dev team? Here’s what we should do: Trey uncovers a bunch of things we didn’t know
  44. 44. Trey hands his work to deployment engineers
  45. 45. “Throw it over the wall” projects Data Science Application Developers
  46. 46. Deploy Models Faster Data Science Application Developers

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