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Should Digital Analysts Become More Data Science-y?

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Should Digital Analysts Become More Data Science-y?

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Presentation by Tim Wilson at Superweek 2017 in Budapest, Hungary. The presentation explores why digital analysts should consider adding some data science skills to their toolset, what types of tools that entails, and what sort of additional value that will help them deliver to their organizations

Presentation by Tim Wilson at Superweek 2017 in Budapest, Hungary. The presentation explores why digital analysts should consider adding some data science skills to their toolset, what types of tools that entails, and what sort of additional value that will help them deliver to their organizations

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Should Digital Analysts Become More Data Science-y?

  1. 1. Should Digital Analysts Become More Data Science-y? Tim Wilson #SPWK / @tgwilson @tgwilson / #SPWK Source: Flickr / KamiPhuc
  2. 2. October 10, 1993 @tgwilson / #SPWK
  3. 3. Mt. Katahdin @tgwilson / #SPWK Source: Google Maps
  4. 4. The end of a 5-month journey @tgwilson / #SPWK Source: Google Maps
  5. 5. …which was a journey on foot. @tgwilson / #SPWK Source: Google Maps
  6. 6. @tgwilson / #SPWK
  7. 7. Which is like walking from Barcelona to Moscow. @tgwilson / #SPWK
  8. 8. Last year, I was on a different journey. @tgwilson / #SPWK Source: Google Maps Digital Analyst Fall 2015 Intermediate R User End of 2016
  9. 9. These journeys were similar in many ways. @tgwilson / #SPWK Flickr / Ines Hegedus-Garcia
  10. 10. They were both challenging! @tgwilson / #SPWK
  11. 11. They were both rewarding! @tgwilson / #SPWK
  12. 12. They both turned out to have destinations that weren’t as clear as I thought. @tgwilson / #SPWK
  13. 13. Last year’s journey @tgwilson / #SPWK Source: Google Maps Digital Analyst Fall 2015 Intermediate R User End of 2016
  14. 14. Last year’s journey @tgwilson / #SPWK Source: Google Maps Digital Analyst Fall 2015 Data Science-y Analyst End of 2016
  15. 15. @tgwilson / #SPWK But… WHY?!
  16. 16. Because the Role of the Digital Analyst Is Evolving @tgwilson / #SPWK
  17. 17. The Spectrum of “Analytics” Digital Analyst Marketer Basic Metrics Segmentation Data Science @tgwilson / #SPWK
  18. 18. Data Science?! Data Science @tgwilson / #SPWK
  19. 19. Data Science Wikipedia sayeth employs techniques and theories drawn from many fields within the broad areas of: @tgwilson / #SPWK
  20. 20. @tgwilson / #SPWK
  21. 21. @tgwilson / #SPWK
  22. 22. @tgwilson / #SPWK is like boiling water.
  23. 23. PEAK1 Flickr / Revolution_Ferg MSR Whisperlite vs. @tgwilson / #SPWK
  24. 24. @tgwilson / #SPWK vs.
  25. 25. vs. Programming Languages Open Source (Free*) Supported By Large Communities Connectable to Digital Analytics (and Other!) Data Sources * TANSTAAFL @tgwilson / #SPWK
  26. 26. Clingmans Dome The highest point on the Appalachian Trail… Flickr / David Fulmer …and where I was during my undergraduate commencement. @tgwilson / #SPWK
  27. 27. Just as education arms us with the power of knowledge… ...“programming” with data is powerful. Flickr / David Fulmer @tgwilson / #SPWK
  28. 28. Data Extraction Flickr / Scott Swigart @tgwilson / #SPWK
  29. 29. @tgwilson / #SPWK One GA Account Many Properties! Many Views!
  30. 30. @tgwilson / #SPWK 22 lines of code
  31. 31. @tgwilson / #SPWK 22 lines of terribly written code
  32. 32. @tgwilson / #SPWK 17 lines of terribly okay written code 38Google Analytics views queried IN 19 seconds
  33. 33. @tgwilson / #SPWK
  34. 34. @tgwilson / #SPWK Pull all of the views I can access Filter to the one account I care about Filter to only the “production” views Pull the data for each of those views Add that data to my (filtered) list of views Export a .csv Pretty up the column names
  35. 35. @tgwilson / #SPWK Yes! Is this just an alternative to Adobe’s Data Warehouse? …with more flexibility …without the mystery delivery time
  36. 36. Reusability and Extensibility Flickr / Scott Swigart @tgwilson / #SPWK
  37. 37. @tgwilson / #SPWK Pull all of the views I can access Filter to the one account I care about Filter to only the “production” views Pull the data for each of those views Add that data to my (filtered) list of views Export a .csv Pretty up the column names
  38. 38. @tgwilson / #SPWK
  39. 39. Comparisons: (Top) 18 Web Sites @tgwilson / #SPWK
  40. 40. Totals: Channel vs. Customer Segment @tgwilson / #SPWK
  41. 41. Anomalies: Channels vs. Segments @tgwilson / #SPWK
  42. 42. github.com/gilliganondata @tgwilson / #SPWK
  43. 43. Data Science @tgwilson / #SPWK
  44. 44. @tgwilson / #SPWK “The clearest way into the universe is through a forest wilderness.” - John Muir
  45. 45. “Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” - Stephen Few 0 20 40 60 80 100 120 140 160 180 200 1/3 1/10 1/17 1/24 1/31 2/7 2/14 2/21 2/28 3/6 3/13 3/20 3/27 4/3 4/10 4/17 4/24 5/1 5/8 5/15 5/22 5/29 6/5 6/12 @tgwilson / #SPWK
  46. 46. Where does R really stand out? Flickr / Marina del Castell @tgwilson / #SPWK VS.
  47. 47. Variety! r-graph-gallery.com @tgwilson / #SPWK
  48. 48. X X Variety! r-graph-gallery.com @tgwilson / #SPWK
  49. 49. Variety! r-graph-gallery.com @tgwilson / #SPWK
  50. 50. How else does R stand out? Flickr / Marina del Castell @tgwilson / #SPWK
  51. 51. My Perspective: Fall 2015 @tgwilson / #SPWK Do the Analysis Communicate Results @analyticshero DATA VISUALIZATION
  52. 52. My Data Science-y Perspective @tgwilson / #SPWK Do the Analysis Communicate Results @analyticshero DATA VISUALIZATION
  53. 53. To be clear… @tgwilson / #SPWK …many tools offer powerful data visualization.
  54. 54. Data scientists build visualizations that are interactive and reactive. @tgwilson / #SPWK bit.ly/ga-explore
  55. 55. bit.ly/ga-explore @tgwilson / #SPWK
  56. 56. bit.ly/ga-explore @tgwilson / #SPWK
  57. 57. bit.ly/ga-explore @tgwilson / #SPWK
  58. 58. bit.ly/ga-explore @tgwilson / #SPWK
  59. 59. Data Science @tgwilson / #SPWK
  60. 60. What is a p-value? @tgwilson / #SPWK
  61. 61. “Informally, a p-value is the probability under a specified statistical model that a statistical summary of the data (for example, the sample mean difference between two compared groups) would be equal to or more extreme than its observed value.” – American Statistical Association @tgwilson / #SPWK
  62. 62. @tgwilson / #SPWK p-value: Statistically significant pregnancy test.
  63. 63. This topic is… Flickr / Howard Lake @tgwilson / #SPWK
  64. 64. As web analysts, how often do we consider: Correlations? Regressions? Confidence Levels? Confidence Intervals? Outlier Detection/Removal? Type 1 vs. Type 2 Errors? Bayesian vs. Frequentist Approaches? Flickr / Neil Piddock @tgwilson / #SPWK
  65. 65. @tgwilson / #SPWK This is hard! Flickr / Casey Fleser
  66. 66. Which Channels Have a Traffic Correlation? @tgwilson / #SPWK
  67. 67. TODAY The metric went down. Why?!
  68. 68. TODAY Did the metric move enough for me to care?
  69. 69. Forecasts = The Future? Actual Forecast TODAY (NOT FOR THIS, AT LEAST)
  70. 70. Let’s pretend “today” is in the past! Actual Forecast TODAY PRETEND “TODAY”
  71. 71. Let’s pretend ”today” is in the past! Actual Forecast PRETEND “TODAY”
  72. 72. Actual Forecast And let’s build a forecast from that point PRETEND “TODAY”
  73. 73. We know a forecast won’t be perfect. Prediction Interval Actual Forecast PRETEND “TODAY”
  74. 74. Now we have meaningful context! Prediction Interval Actual Forecast TODAY
  75. 75. What this can look like in the real world. @tgwilson / #SPWK
  76. 76. And…drilling down and summarizing @tgwilson / #SPWK
  77. 77. Mark Edmondson’s Pre-/Post- Analysis Using a Bayesian Structural Time-Series Method @tgwilson / #SPWKbit.ly/ga-effect
  78. 78. So, are YOU ready to put data science(-y-ness) on your career roadmap? STATISTICS @tgwilson / #SPWK Source: Flickr / Xavi DATA VISUALIZATION COMPUTER PROGRAMMING
  79. 79. The Measure Slack Team @tgwilson / #SPWK http://join.measure.chat
  80. 80. dartistics.com @tgwilson / #SPWK
  81. 81. R and Statistics for the Digital Analyst Columbus, Ohio, US – June 13-15, 2017 bit.ly/r-stats-training Introduction to RDAY 1 The Basics Taught by Tim Wilson and Mark Edmondson Statistics for the Digital Analyst DAY 2 Putting R into Practice DAY 3 Real-World Digital Analytics Examples Taught by Dr. Michael Levin, Otterbein University Building Those Real-World Examples (and Advanced Topics) Taught by Mark Edmondson and Tim Wilson
  82. 82. Flickr / Jeffrey Stylos Slack/Twitter: @tgwilson linkedin.com/in/tgwilson tim@analyticsdemystified.com Podcast: analyticshour.io @tgwilson / #SPWK 2-D Tool: bit.ly/ga-explore Website: dartistics.com Slack: join.measure.chat Training: bit.ly/r-stats-training

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