CS 725/825 - Information Visualization
Spring 2017: Wednesdays, 9:30am-12:15pm, E&CS 2120

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Assignment Guidelines

CS725 @ GitLab

Paper Presentations

Project - updated


Tableau's data visualization software is provided through the Tableau for Teaching program.

Topic Objectives

Week 1 - Introduction - Ch 1

  • Define visualization.
  • Explain the importance of humans in the visualization process.
  • Explain why human vision is particularly well-suited for information transfer.
  • Give an example of a visualization idiom.
  • Explain why it is best to consider multiple alternatives for vis before selecting a solution.
  • Explain at a high-level the "why-what-how" framework for analyzing visualization use.
  • Describe at least one historical visualization and explain its impact.
  • Differentiate between R, D3, and Tableau and describe the type of tasks for which each tool might be most appropriate.

Week 2 - Data - Ch 2

  • Distinguish among the four basic dataset types.
  • Distinguish among the five core data types.
  • Distinguish between categorical and ordered attributes.
  • Distinguish between ordinal and quantitative attributes.
  • Explain why understanding the dataset and data types and semantics matter for designing effective visualizations.
  • Distinguish between scientific vis and information vis in terms of how spatial data is used.
  • Explain the difference between a flat table and a multidimensional table.
  • Explain some of the complexities of dealing with temporal data.
  • Identify two tools for cleaning data.

Week 3 - Arrange Tables - Ch 7

  • Explain why the arrange design choice is the most crucial visual encoding choice.
  • Explain how the concepts of express, separate, order, and align all relate to arranging tabular data.
  • For each idiom example in the text (from scatterplot to normalized stacked bar chart), identify the "what: data" properties of the idiom.
  • For each idiom example in the text, identify the "how: encode" properties of the idiom.
  • For each idiom example in the text, identify the "why: task" properties of the idiom.
  • For each idiom example in the text, identify the "scale" properties of the idiom.
  • Differentiate between line charts and bar charts and explain when each is appropriate
  • Explain some of the disadvantages of pie charts.
  • Explain how a radial layout maps to a rectilinear layout.
  • Given a particular dataset and task, suggest an idiom and explain why it might be appropriate
  • Identify a visualization where an inappropriate arrange design choice was made and explain why the choice was inappropriate.

Hands-on with Tableau, R, D3

  • Use Tableau to create different views of a dataset for exploration
  • Use R to create a scatterplot matrix of a dataset suitable for examining the relationships between multiple variables
  • Use D3 to create an interactive view of a dataset
  • Explore the different types of graphs that can be created with R and D3

Week 4 - Marks and Channels - Ch 5

  • Explain how marks and channels are related.
  • Distinguish between the identity channel type and the magnitude channel type and indicate which channels belong to each type.
  • Distinguish between the principles of expressiveness and effectiveness in visual encoding.
  • List the channels for ordered attributes in order from most effective to least effective.
  • List the channels for categorical attributes in order from most effective to least effective.
  • Describe the effects of accuracy, discriminability, separability, popout (preattentive processing), and grouping and give one example that illustrates each.
  • Explain the implication of Stevens' Law for visualizations.
  • Explain the implication of Weber's Law for visualizations.

Week 5 - Tasks - Ch 3

  • Discuss the strengths and limitations of vis tools that are for a specific purpose and those that are general.
  • Distinguish among the three levels of actions in the task abstraction framework.
  • Given a visualization, identify the actions the vis allows and the targets of those actions.
  • Transform domain-specific tasks into the task abstraction framework.

Week 6 - Analysis - Ch 4

  • Explain the importance of validation in vis design.
  • Explain how the four levels of vis design fits into the what-why-how model.
  • Explain each of the four levels of vis design.
  • Distinguish between the top-down and bottom-up approaches to vis design.
  • Describe how the analysis framework applies to both top-down and bottom-up approaches.
  • Describe the four classes of threats to validity.
  • Describe validation approaches at each of the four levels.
  • Given an academic paper, identify the presence or absence of the four levels of vis design and validation.

Week 7 - Maps, Arrange Networks and Trees - Ch 8.1-8.3, Ch 9

  • Describe how the arrange design choice is different with spatial data as opposed to tabular data.
  • Describe a choropleth map.
  • Identify the two main families of visual encoding idioms for arranging network data in space.
  • Describe a spline radial layout and how it differs from a node-link layout.
  • Identify the tasks for which node-link diagrams are most appropriate.
  • Describe the adjacency matrix view of a network and contrast it with a node-link view.
  • Describe a treemap.

Week 8 - Rules of Thumb - Ch 6

  • Explain potential difficulties with the use of 3D visualization.
  • Identify situations in which the use of 3D visualization would be appropriate.
  • Explain why "eyes beat memory".
  • Explain what happens when people experience cognitive load.
  • Define change blindness.
  • Explain the tradeoff between resolution and immersion.
  • Explain the Shneiderman mantra "overview first, zoom and filter, details on demand".
  • Explain the alternate concept of "search, show context, expand on demand" and identify in what situations it may be more appropriate than the Shneiderman mantra.
  • Explain the importance of the design slogan "get it right in black and white".

Week 9 - Map Color and Other Channels - Ch 10

  • Describe the components of color.
  • Describe the three main types of colormaps.
  • Explain the importance choosing an appropriate colormap.
  • Given a set of data and a task, determine an appropriate colormap.
  • Identify an inappropriate use of a colormap and suggest a more appropriate one.
  • Explain why rainbow colormaps should only be used with great care.
  • For the visual channels other than color, identify which are magnitude and which are identity channels.

Week 10 - Manipulate View - Ch 11

  • Describe why changing a view might aid in understanding a dataset.
  • Explain why order can make such an impact in understanding.
  • Describe some of the design choices that can be made with selection.
  • Explain the difference between selection and highlighting.
  • Describe the three components of navigation.
  • Explain the idea behind semantic zooming.
  • Given an interactive visualization, identify the interaction idioms used.

Week 11 - Multiple Views - Ch 12

  • Explain the importance and usefulness of faceting data across multiple views.
  • Contrast the two major approaches to faceting information.
  • Describe the four major design choices for juxtaposed views.
  • Explain the concept of linked highlighting.
  • Describe the three alternatives for sharing data between two juxtaposed views.
  • Contrast the use of small multiples with a grouped bar chart.
  • Given a multiform visualization, identify the ways in which the data was split into multiple views and the design choices that were made.

Week 12 - Reduce Items and Attributes - Ch 13

  • Explain the need to reduce data, both in terms of number of items and number of attributes.
  • Explain the difference between filtering and aggregation and the purposes of each.
  • Identify instances of scented widgets, as opposed to standard filtering widgets.
  • Contrast histograms with bar charts.
  • Explain the benefits of a continuous scatterplot for high-density data over a scatterplot that uses size to encode density.
  • Describe the components of a boxplot.
  • Explain the idea behind dimensionality reduction.
  • Explain why dimensionality reduction is especially useful for analyzing text document collections.
  • Describe some cautions when visualizing the results of dimensional reduction with a scatterplot.

Week 13 - Embed: Focus + Context - Ch 14

  • Describe the idea behind focus+context views.
  • Describe the three main ways that focus+context can be employed.
  • Contrast the magic lens idiom with the fisheye lens idiom.
  • Describe the costs and benefits of distortion.
  • Given a focus+context visualization, identify whether it uses elision, superimposition, or distortion (or some combination).

Week 14 - Case Studies - Ch 15

  • Use the analysis framework to decompose a vis approach into pieces that can be compared with other approaches.