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Data 101: Introduction to Data Visualization

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Do you want to make pictures using data but don't know where to start? Would you like to learn how data visualization works, and how to tell stories with data?

This workshop by David Newbury explores the history of data visualization from the first maps to the latest interactive tools from the New York Times.The workshop will also discuss the hows and whys of storytelling with data. It finshes with a collaborative exploration of data visualization using Sharpies, Post-It notes, and things that begin with "S".
No computers will be used in this class, and there are no prerequisites. As a result of this workshop, you'll have a stronger foundation in understanding how to communicate information more-effectively.
We’re excited to partner with the Carnegie Library of Pittsburgh on a “Data 101” training series designed to build information literacy, mapping, and data visualization skills for people looking to get started in using data, or more-experienced users looking to brush-up on their skills. The training sessions will be offered monthly at one of the Library’s branches, and will be followed by ample time to practice what you’ve learned.

This first class on data visualization was offered on the morning of May 10, 2016 at the East Liberty Branch.

Publié dans : Données & analyses

Data 101: Introduction to Data Visualization

  1. 1. Data Visualization Data 101 May 10th, 2016 Data 101. David Newbury — @workergnome 1
  2. 2. David NewburyProfessional nerd artist @workergnome www.workergnome.com Data 101. David Newbury — @workergnome 2
  3. 3. What We're Doing Today: —(Brief) History of Data Visualization —(Tiny) Theory of Visualization —(Nerdy) Overview of Concepts —(Fake) Data Exploration —(Incomplete) Overview of Tools Data 101. David Newbury — @workergnome 3
  4. 4. What We're not Doing Today: —Writing Code —Thinking about Mapping —Worrying about Data Provenance Data 101. David Newbury — @workergnome 4
  5. 5. Which is biggest? 15012, 8271, 30193, 1189, 9913, 16000, 92481, 49801, 100407, 2910, 3809, 8018, 61528, 18083, 38691, 1800 Data 101. David Newbury — @workergnome 5
  6. 6. Which is biggest? Data 101. David Newbury — @workergnome 6
  7. 7. Which is biggest? Data 101. David Newbury — @workergnome 7
  8. 8. Why? Data 101. David Newbury — @workergnome 8
  9. 9. (Brief) History of Data Visualization Data 101. David Newbury — @workergnome 9
  10. 10. Tabula Peutingeriana, 5th century CE Data 101. David Newbury — @workergnome 10
  11. 11. Data 101. David Newbury — @workergnome 11
  12. 12. Rene Descartes, 1600s Data 101. David Newbury — @workergnome 12
  13. 13. Joseph Priestly, New Chart of History (1769) Data 101. David Newbury — @workergnome 13
  14. 14. William Playfair, (1786 & 1801) Data 101. David Newbury — @workergnome 14
  15. 15. Data 101. David Newbury — @workergnome 15
  16. 16. Data 101. David Newbury — @workergnome 16
  17. 17. John Snow, London Cholera Map (1854) Data 101. David Newbury — @workergnome 17
  18. 18. Cholera Map Data 101. David Newbury — @workergnome 18
  19. 19. Florence Nightingale, War Deaths (1855) Data 101. David Newbury — @workergnome 19
  20. 20. Charles Minard, March on Moscow (1862) Data 101. David Newbury — @workergnome 20
  21. 21. More recent history. Data 101. David Newbury — @workergnome 21
  22. 22. Data 101. David Newbury — @workergnome 22
  23. 23. Edward Tufte The Visual Display of Quantitative Information. Data 101. David Newbury — @workergnome 23
  24. 24. Data 101. David Newbury — @workergnome 24
  25. 25. New York Times Data 101. David Newbury — @workergnome 25
  26. 26. Data 101. David Newbury — @workergnome 26
  27. 27. (tiny) Theory of Visualization Data 101. David Newbury — @workergnome 27
  28. 28. Dataviz is constructed reality. You are telling a story, not (just) stating facts. Data 101. David Newbury — @workergnome 28
  29. 29. data art as opposed to data visualization as opposed to statistical graphics Data 101. David Newbury — @workergnome 29
  30. 30. Statistical Graphics How do I create Statistical Graphs in SAS 9.1.3 without Proc Gplot. UCLA: Statistical Consulting Group. http://www.ats.ucla.edu/stat/ sas/notes2/ Data 101. David Newbury — @workergnome 30
  31. 31. Data Art Dear Data Giorgia Lupi & Stefanie Posavec. http://www.dear-data.com Data 101. David Newbury — @workergnome 31
  32. 32. Two Uses1). help people grasp things outside their reach Data 101. David Newbury — @workergnome 32
  33. 33. Two Uses1). help people grasp things outside their reach 2.) tell stories Data 101. David Newbury — @workergnome 33
  34. 34. explanatory visualization work as opposed to exploratory visualizations Data 101. David Newbury — @workergnome 34
  35. 35. Dataviz is constructed reality. Do you care how true your story is? Do you care how accurate your story is? Are you trying to teach, entertain, or convince? Data 101. David Newbury — @workergnome 35
  36. 36. Data 101. David Newbury — @workergnome 36
  37. 37. Data 101. David Newbury — @workergnome 37
  38. 38. Data 101. David Newbury — @workergnome 38
  39. 39. Data 101. David Newbury — @workergnome 39
  40. 40. (Nerdy) Overview of Concepts Data 101. David Newbury — @workergnome 40
  41. 41. What can you visualise? Data 101. David Newbury — @workergnome 41
  42. 42. Potential Subjects. subways, sheep, the solar system, shoes, sleep, skyline, snow, supermarket, sausages, school,the sea, spiders, staircases, syrup, soap, sawmills, stereos... Data 101. David Newbury — @workergnome 42
  43. 43. Potential Subjects. subways, sheep, the solar system, shoes, sleep, skyline, snow, supermarket, sausages, school,the sea, spiders, staircases, syrup, soap, sawmills, stereos... ...and other things that begin with S. Data 101. David Newbury — @workergnome 43
  44. 44. Dimension and Scope are about choosing what to focus on. Data 101. David Newbury — @workergnome 44
  45. 45. Scope Out of the infinite stories about any subject, which parts are you going to choose? Data 101. David Newbury — @workergnome 45
  46. 46. Possible Scopes All trains in a day All the rides that I've been on this year My train this morning All of the stops in the city Each line Every train stop in the past 50 years Data 101. David Newbury — @workergnome 46
  47. 47. Dimension Which bits of information about a subject are you going to focus on? Data 101. David Newbury — @workergnome 47
  48. 48. Possible Dimensions number of cars duration of ride date of a ride different lines number of stops cost per ride number of stops per day time between stops Data 101. David Newbury — @workergnome 48
  49. 49. What does your data look like? Data 101. David Newbury — @workergnome 49
  50. 50. Types of Data Dates Numbers Geo Coordinate Strings Categories Data 101. David Newbury — @workergnome 50
  51. 51. Types of Data number of cars - Numeric duration of ride - Numeric date of a ride - Date different lines - Category number of stops - Numeric cost per ride - Category number of stops per day - Numeric time between stops - Numeric Data 101. David Newbury — @workergnome 51
  52. 52. Two (related ides): Categories & measures Data 101. David Newbury — @workergnome 52
  53. 53. Categories are Discrete Things Measures are for Counting Data 101. David Newbury — @workergnome 53
  54. 54. number of cars - Measure duration of ride - Measure date of a ride - Measure different lines - Categories number of stops - Measure cost per ride - Categories number of stops per day - Measure time between stops - Measure cleanliness - Categories Data 101. David Newbury — @workergnome 54
  55. 55. A hidden dimension: David, Daniel, Dawn, Danique Data 101. David Newbury — @workergnome 55
  56. 56. A hidden dimension: David (1), Daniel (2), Dawn (3), Danique (4) Position of the item in the group. Data 101. David Newbury — @workergnome 56
  57. 57. (Fake) Data Exploration Data 101. David Newbury — @workergnome 57
  58. 58. TRY IT. Data 101. David Newbury — @workergnome 58
  59. 59. Choose one. subways, sheep, the solar system, shoes, sleep, skyline, snow, supermarket, sausages, school,the sea, spiders, staircases, syrup, soap, sawmills, stereos... ...and other things that begin with S. Data 101. David Newbury — @workergnome 59
  60. 60. Now What?Data 101. David Newbury — @workergnome 60
  61. 61. We need to map our data from a domain to a range. Data 101. David Newbury — @workergnome 61
  62. 62. Domain number of cars - 1...8 duration of ride - 30 sec...2 hours date of a ride - - 24ft...200ft different lines - Red line, Blue line, Green line, Silver Line, Yellow Line number of stops - **2..20 cost per ride - "$2.50, $1.75, $3.00, $0.00" number of stops per day - ??...??? Data 101. David Newbury — @workergnome 62
  63. 63. Range Domain is the possible input values Range is the possible output values Data 101. David Newbury — @workergnome 63
  64. 64. Data 3, 7, 10, 6, 2 Position of the item in the group. Domain [0-10] [1-5] Range X: 400px Y: 800px Mapping X: item position Y: numeric value Data 101. David Newbury — @workergnome 64
  65. 65. Data 3, 7, 10, 6, 2 Position of the item in the group. Area Data 101. David Newbury — @workergnome 65
  66. 66. Data 3, 7, 10, 6, 2 Position of the item in the group. Color Data 101. David Newbury — @workergnome 66
  67. 67. Data 3, 7, 10, 6, 2 Position of the item in the group. Multiples Dimensions Data 101. David Newbury — @workergnome 67
  68. 68. Data val1: 3, 7, 10, 6, 2 val2: 5, 8, 1, 8, 3 val3: Cat, Dog, Cat, Cat, Dog Position of the item in the group. Mapping X: item position Y: val1 Size: val2 Color: val3 Data 101. David Newbury — @workergnome 68
  69. 69. Dimensions beyond X and Y. Color Size Shape Labels Patterns Icons Anything Else You Can Imagine Data 101. David Newbury — @workergnome 69
  70. 70. TRY IT. Data 101. David Newbury — @workergnome 70
  71. 71. Finishing Touches Data 101. David Newbury — @workergnome 71
  72. 72. Measures get Axis Categories get Headers Data 101. David Newbury — @workergnome 72
  73. 73. Labels Data 101. David Newbury — @workergnome 73
  74. 74. Axis Category Axis Number Axis Date Axis Log axis Data 101. David Newbury — @workergnome 74
  75. 75. Legends Data 101. David Newbury — @workergnome 75
  76. 76. TRY IT. Data 101. David Newbury — @workergnome 76
  77. 77. Review Dimensions Scope Domain Range Categories Measures Data 101. David Newbury — @workergnome 77
  78. 78. (Incomplete) Overview of Tools Data 101. David Newbury — @workergnome 78
  79. 79. Data 101. David Newbury — @workergnome 79
  80. 80. Data 101. David Newbury — @workergnome 80
  81. 81. Data 101. David Newbury — @workergnome 81
  82. 82. Data 101. David Newbury — @workergnome 82
  83. 83. Data 101. David Newbury — @workergnome 83
  84. 84. Data 101. David Newbury — @workergnome 84
  85. 85. Thank You. Data 101. David Newbury — @workergnome 85

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