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N328 Visualizing Information Week 2 | Data Abstractions & Intro - PowerPoint PPT Presentation

N328 Visualizing Information Week 2 | Data Abstractions & Intro to Tableau Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI Last week Why use vision to analyze information? Seasonally adjusted unemployment rate in the


  1. N328 Visualizing Information Week 2 | Data Abstractions & Intro to Tableau Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI

  2. Last week

  3. Why use vision to analyze information? Seasonally adjusted unemployment rate in the US Bureau of Labor Statistics

  4. Why use vision to analyze information?

  5. Visualization can also do harm, though, if not done correctly

  6. Space Shuttle Challenger Disaster

  7. By E. Tufte

  8. Based on reporting by the Guardian (https://www.theguardian.com/technology/blog/2008/jan/21/liesdamnliesandstevejobs) Via Miriah Meyer

  9. US smart phone marketshare Slide by Miriah Meyer

  10. US smart phone marketshare Slide by Miriah Meyer

  11. This week • Intro to Tableau • Data type and seman;cs • Variable types: Categorical, Ordinal, and Quan5ta5ve variables

  12. Assignment 1 Due Monday 11:59pm in Canvas Submit a PDF file: Visualiza5on (picture) + Writeup

  13. M Apple 1 Amy 8 S Pear 2 Basil 7 M Peach 3 Clara 6 S Lychee 4 Desmond 8 L Orange 5 Fanny 7 S Pear 6 George 6 M Apple 7 Hector 10

  14. Shirt Favorite ID Name Age Size Fruit M Apple 1 Amy 8 S Pear 2 Basil 7 M Peach 3 Clara 6 S Lychee 4 Desmond 8 L Orange 5 Fanny 7 S Pear 6 George 6 M Apple 7 Hector 10

  15. Terminology • Items: are individual units of informa5on. For example: a rows in a table represen5ng one order out of many. • A?ribute (variables, dimension): a property rela5ng to items. E.g., the shipping cost of a par5cular order

  16. Flat Tables Via Miriah Meyer

  17. Attribute/Variable Types ✦ Categorical (Nominal, Qualita;ve) A finite set of categories No implicit ordering between categories ✦ Ordered •Ordinal Implicit ordering between categories/levels, but no clear magnitude difference. Can compare and determine greater/less than •Quan;ta;ve Meaningful magnitude Can do arithme5c

  18. Quan;ta;ve Data Interval vs. Ra5o ✦ Interval •Zero does not indicate an absence of detectable measurement •We can determine distance between measurement, but not propor5ons •Example: temperature, dates ✦ Ra;o •The posi5on of zero indicates there is nothing of the measured en5ty •Can determine ra5o and propor5ons •Example: weight, age

  19. Quiz What a]ribute/variable type (Categorical, Ordinal, Interval, or Ra5o) best fit the following measurements? •Speed •Facebook reac5ons (Like, Angry, Sad, etc…) •Car configura5ons (Compact, Mid-Sedan, SUV) •Product Name •IQ scores •College Majors •50-meter race 5me Based on a slide by Alex Lex

  20. Introduction to Tableau

  21. Download IMDB Movies dataset: http://research.vis.ninja

  22. Design critique http://tinyurl.com/6mu8h63 • What is the visualiza5on about? • What data is represented in the visualiza5on? And how? • What are the interac5ons used? • What ques5ons can we answer with the visualiza5on? • Do you like the visualiza5on? • Are there any improvements that can be made to the design?

  23. Next week Visual Percep5on

  24. Required reading in Canvas: files/Visual thinking for design

  25. Critiques Cri;que thread posted (each week by Tuesday 11:59pm) • URL to the visualiza5on • What the visualiza5on is about • Visual encoding: describe how the data is depict in the visualiza5on • What insights did you discover from looking at the visualiza5on? • Do you like the visualiza5on? Why? What would you do to improve it? Comments due Sunday 11:59pm • Good comments: add addi5onal insights or cri5que points, answer ques5ons, provide a relevant example, etc… • Saying “I agree” is not enough; you should explain why

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