Why Why Why
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Often talks about data science focus on tools and methods. Tools are important and it is really important to stay up to date with the latest tools and technologies (they are the “how”). But data science is also about finding good problems and solving them (the “why”). Good problems are ones that are both valuable (someone really wants an answer) and tractable (there is data you can find to help you answer it). Today, with modern technologies, many more problems are tractable than ever before; lots of data is freely available on the internet and sensors make it easy to collect ad hoc data sets. This talk will emphasize the importance of asking “why” by exploring a few examples from Datascope’s client work and our various side projects where, if we hadn’t asked “why”, the outcomes would have been far less useful.
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Why Why Why
- 1. berlin pydata | @gabegaster | 2015 february what is data science?
- 2. berlin pydata | @gabegaster | 2015 february what is data science?
- 3. berlin pydata | @gabegaster | 2015 february what is data science? who is a data scientist?
- 4. berlin pydata | @gabegaster | 2015 february what is data science? who is a data scientist? review of literature
- 5. berlin pydata | @gabegaster | 2015 february what is data science? who is a data scientist? review of literature
- 6. berlin pydata | @gabegaster | 2015 february what is data science? review of literature
- 7. berlin pydata | @gabegaster | 2015 february what is data science? review of literature
- 8. berlin pydata | @gabegaster | 2015 february what is data science? who is a data scientist?
- 9. berlin pydata | @gabegaster | 2015 february what is data science? who is a data scientist? “a scientist who can code”
- 10. berlin pydata | @gabegaster | 2015 february what is data science? who is a data scientist? “a scientist who can code” • lower barrier to attack new problems
- 11. berlin pydata | @gabegaster | 2015 february what is data science? who is a data scientist? “a scientist who can code” • lower barrier to attack new problems • repeatable analysis
- 12. berlin pydata | @gabegaster | 2015 february what is data science? who is a data scientist? “a scientist who can code” • lower barrier to attack new problems • repeatable analysis • freedom to think about problems new ways
- 13. berlin pydata | @gabegaster | 2015 february what is data science?
- 14. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically
- 15. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before
- 16. berlin pydata | @gabegaster | 2015 february which were difficult to answer before
- 17. berlin pydata | @gabegaster | 2015 february computing has progressed which were difficult to answer before
- 18. berlin pydata | @gabegaster | 2015 february 1950 computing has progressed
- 19. berlin pydata | @gabegaster | 2015 february 1950 cost of new analysis computing has progressed
- 20. berlin pydata | @gabegaster | 2015 february 1950 cost of new analysis years computing has progressed
- 21. berlin pydata | @gabegaster | 2015 february 1950 cost of new analysis years today computing has progressed
- 22. berlin pydata | @gabegaster | 2015 february 1950 cost of new analysis years today v computing has progressed
- 23. berlin pydata | @gabegaster | 2015 february 1950 cost of new analysis years today hoursv v computing has progressed
- 24. berlin pydata | @gabegaster | 2015 february 1950 cost of new analysis years today same person thinking about the problem can conduct experiments to answer it hoursv v computing has progressed
- 25. berlin pydata | @gabegaster | 2015 february computing has progressed
- 26. berlin pydata | @gabegaster | 2015 february open-source code computing has progressed
- 27. berlin pydata | @gabegaster | 2015 february open-source code standing on shoulders of giants computing has progressed
- 28. berlin pydata | @gabegaster | 2015 february open-source code standing on shoulders of giants computing has progressed
- 29. berlin pydata | @gabegaster | 2015 february open-source code standing on shoulders of giants computing has progressed
- 30. berlin pydata | @gabegaster | 2015 february open-source code standing on shoulders of giants reinventing the wheel computing has progressed
- 31. berlin pydata | @gabegaster | 2015 february open-source code standing on shoulders of giants reinventing the wheel computing has progressed
- 32. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before
- 33. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically knowing what is possible which were difficult to answer before
- 34. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful
- 35. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful HOW
- 36. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful HOW WHY
- 37. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful
- 38. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful using new good the right tools
- 39. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful using new good the right asking whytools
- 40. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful using new good the right asking why tools
- 41. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful using new good the right asking whytools
- 42. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful using new good the right asking whytools WHY
- 43. berlin pydata | @gabegaster | 2015 february what is data science? using emerging technologies to approach problems scientifically which were difficult to answer before knowing what is possible doing something useful using new good the right asking whytools WHY WHY
- 44. berlin pydata | @gabegaster | 2015 february why why why what is data science?
- 45. berlin pydata | @gabegaster | 2015 february why why why what is data science? science is about asking why
- 46. berlin pydata | @gabegaster | 2015 february why why why what is data science? science is about asking why start there
- 47. berlin pydata | @gabegaster | 2015 february an anecdote
- 48. berlin pydata | @gabegaster | 2015 february
- 49. berlin pydata | @gabegaster | 2015 february
- 50. berlin pydata | @gabegaster | 2015 february
- 51. berlin pydata | @gabegaster | 2015 february an example from the real world
- 52. berlin pydata | @gabegaster | 2015 february • e an example
- 53. berlin pydata | @gabegaster | 2015 february
- 54. berlin pydata | @gabegaster | 2015 february
- 55. berlin pydata | @gabegaster | 2015 february
- 56. berlin pydata | @gabegaster | 2015 february goal: save money
- 57. berlin pydata | @gabegaster | 2015 february goal: save money
- 58. berlin pydata | @gabegaster | 2015 february goal: save money
- 59. berlin pydata | @gabegaster | 2015 february goal: save money
- 60. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february goal: save money task: find needle in the haystack (without poking yourself)
- 61. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february goal: save money task: find needle in the haystack (without poking yourself)
- 62. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february goal: save money task: find needle in the haystack (without poking yourself)
- 63. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february aboutpatent not aboutpatent goal: save money task: find needle in the haystack (without poking yourself)
- 64. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february aboutpatent not aboutpatent turn over to plaintiff don’t turn over to plaintiff adverse inference goal: save money task: find needle in the haystack (without poking yourself)
- 65. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february aboutpatent not aboutpatent turn over to plaintiff don’t turn over to plaintiff adverse inference give away trade secrets goal: save money task: find needle in the haystack (without poking yourself)
- 66. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february aboutpatent not aboutpatent turn over to plaintiff don’t turn over to plaintiff adverse inference give away trade secrets goal: save money task: find needle in the haystack (without poking yourself)
- 67. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february turn over to plaintiff don’t turn over to plaintiff goal: save money task: find needle in the haystack (without poking yourself)
- 68. berlin pydata | @gabegaster | 2015 february
- 69. berlin pydata | @gabegaster | 2015 february
- 70. berlin pydata | @gabegaster | 2015 february
- 71. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february goal: save money prototype
- 72. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february goal: save money prototype
- 73. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february goal: save money prototype — design for lawyers
- 74. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february goal: save money prototype — design for lawyers
- 75. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february Sexier. Less nerdy. Tailored. design for transparency
- 76. berlin pydata | @gabegaster | 2015 februaryberlin pydata | @gabegaster | 2015 february http://www.daegis.com/judicial-acceptance-of-tar/
- 77. berlin pydata | @gabegaster | 2015 february another example contests
- 78. berlin pydata | @gabegaster | 2015 february another example
- 79. berlin pydata | @gabegaster | 2015 february
- 80. berlin pydata | @gabegaster | 2015 february task:
- 81. berlin pydata | @gabegaster | 2015 february classify schizophrenia w MRItask:
- 82. berlin pydata | @gabegaster | 2015 february why? classify schizophrenia w MRItask:
- 83. berlin pydata | @gabegaster | 2015 february why? classify schizophrenia w MRItask: improve understanding of disease
- 84. berlin pydata | @gabegaster | 2015 february why? classify schizophrenia w MRItask: improve understanding of disease how?
- 85. berlin pydata | @gabegaster | 2015 february why? classify schizophrenia w MRItask: improve understanding of disease how? … outside contest purview
- 86. berlin pydata | @gabegaster | 2015 february why? outside contest purview
- 87. berlin pydata | @gabegaster | 2015 february why? outside contest purview
- 88. berlin pydata | @gabegaster | 2015 february why? outside contest purview kaggle
- 89. berlin pydata | @gabegaster | 2015 february why? outside contest purview kaggle getting data & making usable
- 90. berlin pydata | @gabegaster | 2015 february why? outside contest purview kaggle getting data & making usable WHY
- 91. berlin pydata | @gabegaster | 2015 february timeline of contest Accuracy of Classification
- 92. berlin pydata | @gabegaster | 2015 february timeline of contest AUC Accuracy of Classification
- 93. berlin pydata | @gabegaster | 2015 february what is AUC? AUC
- 94. berlin pydata | @gabegaster | 2015 february AUC what is AUC? Area Under Curve
- 95. berlin pydata | @gabegaster | 2015 february AUC what is AUC? Area Under Curve what curve?
- 96. berlin pydata | @gabegaster | 2015 february AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
- 97. berlin pydata | @gabegaster | 2015 february AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
- 98. berlin pydata | @gabegaster | 2015 february AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
- 99. berlin pydata | @gabegaster | 2015 february balances: AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
- 100. berlin pydata | @gabegaster | 2015 february balances: True Positive Rate False Positive Rate AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
- 101. berlin pydata | @gabegaster | 2015 february balances: True Positive Rate False Positive Rate AUC what is AUC? Area Under Curve what curve? Receiver Operating Characteristic
- 102. berlin pydata | @gabegaster | 2015 february AUC what is AUC? balances: True Positive Rate False Positive Rate Area Under Curve what curve? Receiver Operating Characteristic
- 103. berlin pydata | @gabegaster | 2015 february why? AUC what is AUC? balances: True Positive Rate False Positive Rate Area Under Curve what curve? Receiver Operating Characteristic
- 104. berlin pydata | @gabegaster | 2015 february why? … AUC what is AUC? balances: True Positive Rate False Positive Rate Area Under Curve what curve? Receiver Operating Characteristic
- 105. berlin pydata | @gabegaster | 2015 february why? … upshot: AUC what is AUC? balances: True Positive Rate False Positive Rate Area Under Curve what curve? Receiver Operating Characteristic
- 106. berlin pydata | @gabegaster | 2015 february why? … choice of metric matters a LOT upshot: in practice AUC what is AUC? balances: True Positive Rate False Positive Rate Area Under Curve what curve? Receiver Operating Characteristic
- 107. berlin pydata | @gabegaster | 2015 february timeline of contest Accuracy of Classification AUC
- 108. berlin pydata | @gabegaster | 2015 february timeline of contest Accuracy of Classification AUC random guess
- 109. berlin pydata | @gabegaster | 2015 february timeline of contest Accuracy of Classification AUC random guess basic SVM
- 110. berlin pydata | @gabegaster | 2015 february timeline of contest goal? Accuracy of Classification AUC random guess basic SVM
- 111. berlin pydata | @gabegaster | 2015 february timeline of contest goal: depends on why Accuracy of Classification AUC random guess basic SVM
- 112. berlin pydata | @gabegaster | 2015 february random guess basic SVM timeline of contest Accuracy of Classification AUC
- 113. berlin pydata | @gabegaster | 2015 february me timeline of contest Accuracy of Classification AUC
- 114. berlin pydata | @gabegaster | 2015 february me timeline of contest Accuracy of Classification AUC turned out to place 9th — because overfitting
- 115. berlin pydata | @gabegaster | 2015 february me timeline of contest Accuracy of Classification AUC turned out to place 9th — because overfitting very common problem
- 116. berlin pydata | @gabegaster | 2015 february timeline of contest Accuracy of Classification worth it? AUC
- 117. berlin pydata | @gabegaster | 2015 february
- 118. berlin pydata | @gabegaster | 2015 february
- 119. berlin pydata | @gabegaster | 2015 february
- 120. berlin pydata | @gabegaster | 2015 february
- 121. berlin pydata | @gabegaster | 2015 february
- 122. berlin pydata | @gabegaster | 2015 february
- 123. berlin pydata | @gabegaster | 2015 february
- 124. berlin pydata | @gabegaster | 2015 february
- 125. berlin pydata | @gabegaster | 2015 february
- 126. berlin pydata | @gabegaster | 2015 february an example just for fun
- 127. berlin pydata | @gabegaster | 2015 february Chicago Bike Share System ! ! kind of like call-a-bike
- 128. berlin pydata | @gabegaster | 2015 february Show what I like about Bike share ! Chicago Bike Share System ! ! kind of like call-a-bike
- 129. berlin pydata | @gabegaster | 2015 february Show what I like about Bike share ! Think about how bike share has changed geography Chicago Bike Share System ! ! kind of like call-a-bike
- 130. berlin pydata | @gabegaster | 2015 february a typical trip for me
- 131. berlin pydata | @gabegaster | 2015 february Bus transit times = a LIE
- 132. berlin pydata | @gabegaster | 2015 february Chicago is a grid city
- 133. berlin pydata | @gabegaster | 2015 february Difficult Public Transit on the grid =+ Diagonals
- 134. berlin pydata | @gabegaster | 2015 february Difficult Public Transit on the grid =+ Diagonals 2+ buses = FAIL
- 135. berlin pydata | @gabegaster | 2015 february Adding bikes to public transit = win
- 136. berlin pydata | @gabegaster | 2015 february show how has divvy changed where people can go viz Goal:
- 137. berlin pydata | @gabegaster | 2015 february show how has divvy changed where people can go show where people actually go viz Goal:
- 138. berlin pydata | @gabegaster | 2015 february demo
- 139. berlin pydata | @gabegaster | 2015 february in conclusion
- 140. berlin pydata | @gabegaster | 2015 february
- 141. berlin pydata | @gabegaster | 2015 february
- 142. berlin pydata | @gabegaster | 2015 february
- 143. berlin pydata | @gabegaster | 2015 february
- 144. berlin pydata | @gabegaster | 2015 february
- 145. berlin pydata | @gabegaster | 2015 february thanks! @gabegaster