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Explore Data: Data Science + Visualization

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Talk on Data Visualization for Data Scientist at Stockholm NLP Meetup June 2015: http://www.meetup.com/Stockholm-Natural-Language-Processing-Meetup/events/222609869/

Video recording at https://www.youtube.com/watch?v=3Li_xIQ1K84

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Explore Data: Data Science + Visualization

  1. 1. Explore Data: Data Science + Visualization Roelof Pieters PhD candidate at KTH & Data Science consultant.  @graphific
  2. 2. (much thanks & graph love to fellow presenter Jay Solomon* / Augify) *who unfortunately couldn't make it today
  3. 3. Visualizations 
 are everywhere
  4. 4. Data Science Visualization
  5. 5. High friction software Scientific Visualization Output from Algorithms Data Science Visualization
  6. 6. Data journalism Infographic design Visualisation tools Data Science Visualization
  7. 7. Data Visualization / Why?
  8. 8. Title Data journalism / Faces of Fracking http://www.facesoffracking.org/data-visualization/
  9. 9. Infographics design / Nobel Prizes and laureates 1901-2012 by Accurat http://www.accurat.it
  10. 10. Gephi gephi.github.io Tableau www.tableausoftware.com Bokeh bokeh.pydata.org/en/latest/ Processing processing.org R-ggplot2 ggplot2.org Visualisation tools d3.js d3js.org
  11. 11. What is a Visualization?
  12. 12. Visualization is data communicated visually “with clarity, precision and efficiency” (Tufte) Defined?
  13. 13. But it is more than that, The role of visualization is to communicate data meaning through stories
  14. 14. The goal of visualization is to aid our understanding of data by leveraging the human visual system's highly-tuned ability to see patterns, spot trends, and identify outliers. Well-designed visual representations can replace cognitive calculations with simple perceptual inferences and improve comprehension, memory, and decision making. By making data more accessible and appealing, visual representations may also help engage more diverse audiences in exploration and analysis. The challenge is to create effective and engaging visualizations that are appropriate to the data. A Tour Through the Visualization Zoo. Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky.
  15. 15. The challenge is to create effective and engaging visualizations that are appropriate to the data. A Tour Through the Visualization Zoo. Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky.
  16. 16. This is a.. ? and don’t you forget it :) This is a Graph This is a Chart
  17. 17. Some interesting books…
  18. 18. How do you move from data to visualization? How do you determine the appropriate visualization for your data? How do you use visualizations to create meaning and extract valuable insights? And many questions..
  19. 19. What’s in a Visualization?
  20. 20. Visual Storytelling Experience 1 2 3
  21. 21. Position Size Shape Color Metaphor Sequence Structure Content Language Interactivity Adaptability Discovery Reaction Familiarity Visual Storytelling Experience 1 2 3
  22. 22. 1 How do you make data visual? Visual
  23. 23. The goal of visualization is to aid our understanding of data by leveraging the human visual system's highly-tuned ability to see patterns, spot trends, and identify outliers. A Tour Through the Visualization Zoo. Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky.
  24. 24. How would you visualize proportion? Pie Donut Nightingale Voronoi Stacked area chart Stacked bar Treemap
  25. 25. 72% 68% 50% 47% “Sneaker pickup” videos “First Impressions” videos Product “Review” videos “Haul” videos Every decision counts; position, size and shape. Growing genres of product reviews on youtube https://www.thinkwithgoogle.com/articles/i-want-to-buy-moments.html
  26. 26. 72% 68% 50% 47% “Sneaker pickup” videos “First Impressions” videos Product “Review” videos “Haul” videos Every decision counts; position, size and shape. (Our version)
  27. 27. Find the right balance between Accuracy , Aesthetics & Meaning
  28. 28. http://www.nytimes.com/newsgraphics/2014/01/05/poverty-map/ Color as a natural quantifier (Tufte) http://www.nytimes.com/newsgraphics/2014/01/05/poverty-map/
  29. 29. http://www.nytimes.com/newsgraphics/2014/01/05/poverty-map/ Color as metaphor and meaning http://demographics.coopercenter.org/DotMap/index.html
  30. 30. http://www.nytimes.com/newsgraphics/2014/01/05/poverty-map/ Color as metaphor and meaning http://demographics.coopercenter.org/DotMap/index.html
  31. 31. http://www.nytimes.com/newsgraphics/2014/01/05/poverty-map/ From “Envisioning Information”, Tufte. General Bathymetric Chan of the Oceans, International Hydrographic Organization (Ottawa, Canada, 5th edition, 1984), 5.06. Color as metaphor and meaning
  32. 32. Title http://drones.pitchinteractive.com http://www.nytimes.com/newsgraphics/2014/01/05/poverty-map/ Shape as visual metaphor http://drones.pitchinteractive.com
  33. 33. http://www.nytimes.com/newsgraphics/2014/01/05/poverty-map/ Shape as visual metaphor http://chrisharrison.net/projects/bibleviz/BibleVizArc7.png
  34. 34. 2 How do you tell a story with data? Storytelling
  35. 35. Because stories are powerful & memorable
  36. 36. Stories are made of conceptually separable episodes 
 or sub-goals in a chain of actions that form the story’s plot. Stories contain microstructures via the particular details of an event and macro-structure via the relationship of those events to one another in the plot A Deeper Understanding of Sequence in Narrative Visualizsation. Hullman, Drucker, Riche…
  37. 37. Scroll through episodes / New York Times data driven stories http://www.nytimes.com/interactive/2014/06/12/world/middleeast/the-iraq-isis-conflict-in-maps-photos-and-video.html
  38. 38. Animated sequence / 2015 Measles outbreak http://www.bloomberg.com/graphics/2015-measles-outbreaks/
  39. 39. Animated sequence / 2015 Measles outbreak http://www.bloomberg.com/graphics/2015-measles-outbreaks/
  40. 40. Animated sequence / Bloomberg business http://www.bloomberg.com/news/articles/2015-03-04/u-s-companies-are-stashing-2-1-trillion-overseas-to-avoid-taxes
  41. 41. Layering the data / Poverty Tracker, CPRC http://povertytracker.robinhood.org
  42. 42. Interactive visualization / New York Times http://www.nytimes.com/interactive/2014/upshot/buy-rent-calculator.html
  43. 43. 3 How do you enable the user to interact with the visualization 
 & discover relevant insights? Experience
  44. 44. What is the story you want to tell? Unfocused query generates irrelevant story #1 Query
  45. 45. What is the story you want to tell? Identify appropriate data Determine what question to ask Guide through suggestions = INSIGHT
  46. 46. Consider the many sides to the same story #2 Lenses
  47. 47. Like a novel, a narrative can be read through the characters, the context, the events, the emotional landscape
  48. 48. Enable exploration and discovery Reveal the data at several levels of detail, from a broad overview to the fine structure #3 Filter
  49. 49. http://www.informationisbeautiful.net/visualizations/snake-oil-superfoods/ Filters
  50. 50. Silders to explore and see change http://www.nytimes.com/interactive/2014/07/08/upshot/how-the-year-you-were-born-influences-your-politics.html?abt=0002&abg=0
  51. 51. ‘What if’ scenario http://www.nytimes.com/interactive/2014/11/04/health/visuals-ebola-model.html
  52. 52. http://www.nytimes.com/interactive/2014/11/04/health/visuals-ebola-model.html ‘What if’ scenario
  53. 53. Sometimes less is more Think of layering and separation of the data visualization, reduce the noise and let the content shine #4 Options
  54. 54. Focused view / Connect the world cup http://www.nytimes.com/interactive/2014/06/20/sports/worldcup/how-world-cup-players-are-connected.html
  55. 55. Reduce the noise / What can you discover when you reduce the noise? Outliers, focused question and answer….
  56. 56. Reduce the noise / What can you discover when you reduce the noise? Outliers, focused question and answer….
  57. 57. Reduce the noise / What can you discover when you reduce the noise? Outliers, focused question and answer….
  58. 58. Divide the story to relevant portions. A complex graph may not be enough. Look at the micro vs. macro stories and how they help extract insights. Try different metrics #5 Insights
  59. 59. Small multiples / A visual guide to the crisis in Iraq and Syria. http://www.nytimes.com/interactive/2014/06/12/world/middleeast/the-iraq-isis-conflict-in-maps-photos-and-video.html
  60. 60. Interactive linked small multiples / Close the gap http://ri.id.au/closethegap
  61. 61. #7 Experiment & test your hypothesis… Until you find the best visual representation
  62. 62. Visualization that answers a question: Who is most affected? http://www.nytimes.com/interactive/2013/05/27/science/drunk-driving-2011.html?_r=0
  63. 63. What about 
 graphs?
  64. 64. Internet as a Graph
  65. 65. (S. Carmi,S. Havlin, S. Kirkpatrick, Y. Shavitt, E. Shir. 
 A model of Internet topology using k-shell decomposition. 
 PNAS 104 (27), pp. 11150- 11154, 2007) Internet as a Graph
  66. 66. Graphs / Notation nodes edges
  67. 67. Graphs / Notation
  68. 68. Graphs / Communities
  69. 69. Graphs / Community Attributes
  70. 70. (S. Papadopoulos, Y. Kompatsiaris, A. Vakali, P. Spyridonos. “Community detection in Social Media” 2011)
  71. 71. Connected Network
  72. 72. Ego Network
  73. 73. Demo Time
  74. 74. Chinese Conversation Networks on Weibo
  75. 75. Topic Networks
  76. 76. Topic Networks for Crisis Management and detection of interconnected topics
  77. 77. Conversation Networks
  78. 78. Design Choices
  79. 79. Design Choices
  80. 80. Design Choices
  81. 81. Design Choices
  82. 82. Design Choices
  83. 83. Design Choices
  84. 84. Design Choices
  85. 85. Thank you for your attention!

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