CS 625 - Data Visualization
Fall 2019: Tuesdays, 9:30am-12:15pm, ECSB 2120

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Topic Objectives

Chapter 1 - Introduction

  • 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.
  • Differentiate between R, D3, and Tableau and describe the type of tasks for which each tool might be most appropriate.

Chapter 2 - Data

  • 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.

Chapter 5 - Marks and Channels

  • 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.

Chapter 7 - Arrange Tables

  • 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.

Chapter 10 - Map Color and Other Channels

  • 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.


Chapter 13 (through 13.4.1) - Reduce Items

  • 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.
  • Describe the components of a boxplot.

Exploratory Data Analysis (EDA)

  • List the three steps in the iterative cycle of EDA.
  • Explain the importance of binwidth in a histogram.
  • Explain the concept of covariation.
  • Name some questions to ask about patterns found in data.
  • Given a dataset, generate questions aimed at examining correlation and understanding underlying patterns in the data.


  • List the seven genres of narrative visualization.
  • Describe the Martini glass structure of narrative visualization.
  • Describe a stepper in an interactive visualization.
  • Describe how narrative visualization and presentation visualization differ from exploratory/analysis visualization, especially in terms of tools and approaches.

Chapter 6 - Rules of Thumb

  • 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".

Chapter 8 - Maps (8.1-8.3)

  • Describe how the arrange design choice is different with spatial data as opposed to tabular data.
  • Describe a choropleth map.

Chapter 12 - Multiple Views

  • 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.

Chapter 11 (through 11.4) - Manipulate View

  • 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.
  • Given an interactive visualization, identify the interaction idioms used.