The document defines data visualization and describes the basic workflow for creating data visualizations using Tableau. It discusses acquiring, parsing, filtering, analyzing, and representing data. It also covers developing views and dashboards in Tableau, sharing results, and Schneiderman's mantra of providing an overview, zooming and filtering, and details on demand for effective data visualization.

1. Data Visualization
Wil O’Shea, Guest
MEDA 319, Information Design
2015/03/16

2. Goals
• Define data visualization and variations
• Identify basic data types and how to present them
• Describe a basic work flow for data visualization
• Use Tableau to open and explore a novel data set
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3. Data Visualization
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4. What is data visualization?
• A general term
• Information visualization
• “the use of computer supported, interactive, visual
representations of abstract data to amplify cognition”
• Scientific visualization
• Visual representation of generally physical data (e.g., MRI, x-
ray)
• Infographic
• Manually drawn
• Present a beautiful and engaging story about complex
information quickly and clearly using words and graphics to
reveal information, patterns or trends more easily than with
words alone
Card, Mackinlay, & Schneiderman (1999) from Few (2009)
http://visual.ly/what-is-an-infographic
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5. Data visualization versus
infographic
https://ebiinterfaces.files.wordpress.com/2012/07/iliinsky-datavis-ebi-lecture.pdf
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6. Data/Information Visualization
http://www.tableau.com/learn/gallery
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7. Scientific
Visualization
http://www.webgl.com/2012/03/webgl-demo-the-x-toolkit-for-scientific-visualization/
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8. Infographic
http://visual.ly/what-are-odds
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9. Why visualize data?
• “Statistics can reduce large, complex data sets to a
few numbers, but the reductive approach can also
sheer away much of the richness and subtlety in
data.”
Few (2009)
http://visual.ly/what-is-an-infographic
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10. Anscombe’s quartet
http://en.wikipedia.org/wiki/Anscombe%27s_quartet
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11. Anscombe’s quartet
http://en.wikipedia.org/wiki/Anscombe%27s_quartet
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12. How to visualization data
• Acquire
• Parse
• Filter
• Mine
• Represent
• Refine
• Interact
Fry (2008)
http://guides.library.duke.edu/datavis/
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13. Visualization work flow: Acquire
• Obtain the data
• Spreadsheets
• Databases
• Websites
• Scraping tools
• Data entry
• Data exhaust
Fry (2008)
https://import.io/
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14. Visualization work flow: Parse
• Structured and formatted for use
• Parsing data formats
• String – characters such as a word or sentence
• Float – number with decimal
• Character – single letter or symbol
• Integer – number with no decimal
• Index – a type of id that can relate data across tables
Fry (2008)
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15. Visualization work flow: Filter
• Narrow data to elements of interest
• Reduce unneeded variables
• Reduce unneeded observations
• Be aware not to introduce bias or loose context
Fry (2008)
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16. Visualization work flow: Mine
• Analyze data for statistical properties
• Central tendency
• Mean
• Median
• Mode
• Variation
• Range
• Frequency
Fry (2008)
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17. Visualization work flow: Represent
• Determining the form of the data (key)
• Map
• Bar graph
• Scatterplot
• Histogram
Fry (2008)
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18. Visualization work flow: Refine
• Iterate to clarify the representation
• Use graphic design methods to further clarify the
representation
• Call out attributes
• Increase readability
Fry (2008)
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19. Visualization work flow: Interact
• Enable users to “control or explore” the data
• User controlled filters
• Drill down
• Mouse over
• Painting
Fry (2008)
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20. Data
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21. Types of data
• Categorical
• Nominal
• Can be string or number
• Quantitative or numeric
• Interval/ratio
• Continuous or discrete
• Ordinal
• Rank
• Order
• Time
• Date / time
• Location
• Geographic data
• Encoded as other type
• Relationship
• Grouping
• Hierarchy
http://bit.ly/successfulvis
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22. How to visualize the data
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23. Tableau
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24. Tableau Workspace
Data
Window
View
Cards
View
Sheet Tabs
Workspace Controls
Column and Row Shelves
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25. Connecting to data: Source Types
• Desktop Personal & Professional
• Excel
• CSV
• Access
• Windows Azure/OData
• Tableau Extract
• Desktop Professional
• Databases (e.g., SQL Server, Oracle)
• Cloud (e.g., Google Analytics)
• Online *
• Tableau Extract
• Google Big Query
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26. Connecting to data: Methods
• Excel Connection *
• Menu
• Drag and drop
• Cut and paste
• CSV Connection *
• Select table
• Single Table
• Multiple Table
• Custom SQL
• Extracts
• Faster
• Enables unique counts
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27. Working with data
• Data Connections
• SQL
• Calculations
• Merge tables
• Transpose
• Data Manipulations
• Groups
• Calculations
• Hierarchies
• Edit aliases
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28. Developing views
• Drag and drop
• Show Me
• Annotation
• Tooltips
• Formatting
• Maps

29. Developing dashboards
• Layouts
• Annotation
• Filters
• Actions

30. Ways to share results
• Copy image
• Print to PDF
• Formatting
• Tableau Reader
• Careful of data
• Server
• Online
• Public
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31. Interactive dashboard
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32. Schneiderman Mantra
There are many visual design guidelines but the basic principle
might be: summarized as the Visual Information Seeking Mantra:
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Each line represents one project in which I found myself
rediscovering this principle and therefore wrote it down it as a
reminder.
Schneiderman (1996)

33. Schneiderman Mantra
• Overview: Gain an overview of the entire collection.
• Zoom: Zoom in on items of interest
• Filter: filter out uninteresting items.
• Details-on-demand: Select an item or group and get
details when needed.
• Relate: View relationships among items.
• History: Keep a history of actions to support undo,
replay, and progressive refinement.
• Extract: Allow extraction of sub-collections and of the
query parameters.
Schneiderman (1996)