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==Core Readings== | ==Core Readings== | ||
| + | * Claus O. Wilke [https://clauswilke.com/dataviz/ Fundamentals of Data Visualisation]. Chapter 1-5, 17-26. | ||
* [http://dataremixed.com/2012/05/data-visualization-clarity-or-aesthetics/ Data Visualization: Clarity or Aesthetics?] | * [http://dataremixed.com/2012/05/data-visualization-clarity-or-aesthetics/ Data Visualization: Clarity or Aesthetics?] | ||
* [http://dataremixed.com/2012/05/clarity-or-aesthetics-part-2-a-tale-of-four-quadrants/ Clarity or Aesthetics? Part 2 – A Tale of Four Quadrants] | * [http://dataremixed.com/2012/05/clarity-or-aesthetics-part-2-a-tale-of-four-quadrants/ Clarity or Aesthetics? Part 2 – A Tale of Four Quadrants] | ||
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=Learning from Examples= | =Learning from Examples= | ||
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* [https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368 Mistakes, we’ve drawn a few] | * [https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368 Mistakes, we’ve drawn a few] | ||
| + | * [https://www.ft.com/graphics Graphics-Explore FT stories through charts and graphics] | ||
* [https://www.tableau.com/sites/default/files/whitepapers/three_steps_to_make_your_data_clearer_the_gi_tableau_guide.pdf Three Steps to Make Your Data Clearer] | * [https://www.tableau.com/sites/default/files/whitepapers/three_steps_to_make_your_data_clearer_the_gi_tableau_guide.pdf Three Steps to Make Your Data Clearer] | ||
| − | + | * [http://www.thefunctionalart.com/2019/03/when-your-own-data-contradicts-your.html When your own data contradicts your headline] | |
=All About Tableau= | =All About Tableau= | ||
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==Tutorial== | ==Tutorial== | ||
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* [https://www.tableau.com/learn/tutorials/on-demand/getting-started-dashboards-and-stories Getting Started with Dashboards and Stories] | * [https://www.tableau.com/learn/tutorials/on-demand/getting-started-dashboards-and-stories Getting Started with Dashboards and Stories] | ||
Latest revision as of 16:45, 3 January 2021
ISSS608 Visual Analytics and Applications
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Designing Graphs to Enlighten
Contents
Readings
Core Readings
- Claus O. Wilke Fundamentals of Data Visualisation. Chapter 1-5, 17-26.
- Data Visualization: Clarity or Aesthetics?
- Clarity or Aesthetics? Part 2 – A Tale of Four Quadrants
- Clarity or Aesthetics? Part 3 – Tips for Achieving Both
- Junk Charts Trifecta Checkup: The Definitive Guide
Optional Readings
- 7 Basic Rules for Making Charts and Graphs
- Quantitative Literacy Across the Curriculum
- Tapping the Power of Visual Perception
- Sometimes We Must Raise Our Voices
- Best Practices for Understanding Quantitative Data
- Data Visualization: Rules for Encoding Values in Graph
- Choosing Colors for Data Visualization
- Line Graphs and Irregular Intervals: An Incompatible Partnership
Learning from Examples
- Mistakes, we’ve drawn a few
- Graphics-Explore FT stories through charts and graphics
- Three Steps to Make Your Data Clearer
- When your own data contradicts your headline
All About Tableau
White Paper
- Which chart or graphis right for you?
- Good Enough to Great: A Quick Guide for Better Data Visualizations
Tutorial
Visual Analytics Method(s) of the Week
Age-sex Pyramid
- Population pyramid
- Age-Sex and Population Pyramids: The Most Useful Graphs in Population Geography
- Population Pyramid Singapore
Pareto Chart
Diverging Stacked Bar Chart
- Design of Diverging Stacked Bar Charts for Likert Scales and Other Applications
- When Are 100% Stacked Bar Graphs Useful?
- The case against diverging stacked bars
- Are diverging 100% stacked bars useless?
- Rethinking the divergent stacked bar chart — placing the stronger views in the center
