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Data visualization as an engineering task a methodological approach towards creating effective data visualization
The document discusses data visualization as an engineering task, emphasizing a methodological approach for creating effective visuals while avoiding common mistakes. It highlights the importance of understanding the audience, optimizing the data-ink ratio, and choosing appropriate graph types to convey information accurately. The overarching conclusion emphasizes the need to ensure that graphs communicate their intended message clearly and concisely.
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Data visualization as an engineering task a methodological approach towards creating effective data visualization
- 1. Data visualization asan engineering task a methodological approach towards creating effective data visualization Boris Gorelik http://gorelik.net
- 2. Three most commondata visualization mistakes 1 2 3
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- 4. Three most commondata visualization mistakes 1
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- 14. 0 10,000 1 2 34 5 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 Salaries (U.S. $) Female vs. Male Salary Distributions Pay Grades 110,000 0 10,000 1 2 3 4 5 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 Salaries (U.S. $) Male vs. Female Salary Distributions Pay Grades 110,000 From "Show me the numbers” by Stephen C. Few
- 16. Attitude Three most commondata visualization mistakes
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- 19. From "Show methe numbers” by Stephen C. Few
- 20. From "Show methe numbers” by Stephen C. Few “Pre-attentive attributes”
- 21. From "Show methe numbers” by Stephen C. Few
- 24. Three most commondata visualization mistakes Attitude Explanatory vs. Exploratory. Evidence-based. Someone will lie to you
- 25. Three most commondata visualization mistakes 2
- 29. Stimulus Sensory Organ EyeEyeEye Brain PerceptualOrgan Iconic Memory Working Memory Long-term Memory Sensation (Physical Process) Perception (Cognitive Process) From "Show me the numbers” by Stephen C. Few
- 30. Stimulus Sensory Organ EyeEyeEye Brain PerceptualOrgan Iconic Memory Working Memory Long-term Memory Sensation (Physical Process) Perception (Cognitive Process) From "Show me the numbers” by Stephen C. Few
- 31. Stimulus Sensory Organ EyeEyeEye Brain PerceptualOrgan Iconic Memory Working Memory Long-term Memory Sensation (Physical Process) Perception (Cognitive Process) From "Show me the numbers” by Stephen C. Few
- 32. Stimulus Sensory Organ EyeEyeEye Brain PerceptualOrgan Iconic Memory Working Memory Long-term Memory Sensation (Physical Process) Perception (Cognitive Process) From "Show me the numbers” by Stephen C. Few
- 33. Stimulus Sensory Organ EyeEyeEye Brain PerceptualOrgan Iconic Memory Working Memory Long-term Memory Sensation (Physical Process) Perception (Cognitive Process) From "Show me the numbers” by Stephen C. Few “Pre-attentive attributes”
- 34. From "Show methe numbers” by Stephen C. Few color enclosure
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- 39. Above all, showdata
- 40. No data —no ink
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- 45. Signal Noise Data Noise Usefulinformation Useless information
- 48. From "Show methe numbers” by Stephen C. Few
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- 55. Data-ink ratio Information layers Nouseless information
- 60. Three most commondata visualization mistakes Graph contents and composition Optimize the data-ink ratio. Use appropriate graph types.
- 66. Three most commondata visualization mistakes Attitude Evidence-based.Explanatory vs. Exploratory Graph contents and composition Optimize the data-ink ratio. Use appropriate graph types.
- 67. Three most commondata visualization mistakes 3
- 68. RED GREEN BLUERED BLUE GREEN BLUE YELLOW RED RED BLUE GREEN GREEN YELLOW RED GREEN BLUE
- 69. RED GREEN BLUERED BLUE GREEN BLUE YELLOW RED RED BLUE GREEN GREEN YELLOW RED GREEN BLUE
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- 73. Course Title “So what?”vs. “What?”
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- 77. Three most commondata visualization mistakes
- 78. Three most commondata visualization mistakes Attitude Evidence-based Explanatory vs. Exploratory
- 79. Three most commondata visualization mistakes Graph contents and composition Remove, remove, remove. Use appropriate graph types.
- 80. Three most commondata visualization mistakes Conclusions matter ”So what?” is more important than “what?”
- 81. Data visualization asan engineering task a methodological approach towards creating effective data visualization Boris Gorelik http://gorelik.net
- 83. Data visualization asan engineering task Audience who is your target audience?
- 84. Data visualization asan engineering task Audience who is your target audience Build the graph using the right data and the right graph type
- 85. Data visualization asan engineering task Audience who is your target audience Build the graph using the right data and the right graph type Conclusion does the graph say what it says that it says?
- 86. Data visualization asan engineering task Audience who is your target audience Build the graph using the right data and the right graph type Conclusion does the graph say what it says that it says? Delete remove everything unneeded
- 87. Data visualization asan engineering task Audience who is your target audience Build the graph using the right data and the right graph type Conclusion does the graph say what it says that it says? Delete remove everything unneeded
- 88. Data visualization asan engineering task Boris Gorelik, Ph.D. boris@gorelik.net http://gorelik.net
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