CS 725/825 - Information Visualization Fall 2013: Tues/Thurs 1:30-2:45pm, E&CS 2120

Staff

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

# Objectives

Note: The numbers in parenthesis refer to the section in the textbook that the objective aligns with.

## Week 1 - Introduction - Ch 1

• (1.1) - Explain the importance of visualization in aiding human understanding.
• (1.2) - Describe at least one historical visualization and explain its impact.
• (1.4) - Explain each of the stages in the visualization pipeline.
• (1.4) - Explain why it is important to consider human perception when designing visualizations.
• (1.6) - Describe a scatterplot and what type of data it can represent.
• (1.7) - Distinguish between presentation visualization and exploratory visualization.
• Differentiate between R, D3, and Tableau and describe the type of tasks for which each tool might be most appropriate.
• Use R, D3, and Tableau to create simple visualizations.

## Week 2 - Data - Ch 2

• (2.1) - Distinguish between ordinal and nominal values and list subcategories of each.
• (2.2) - Give an example of structured data and describe its properties.
• (2.3) - Explain the importance of data preprocessing before producing a visualization
• (2.3) - Contrast interactive subsetting with query-based subsetting
• (2.3) - Explain the purpose of dimensional reduction.

## Week 3 - Hands-on with Tableau, R, d3

• Use Tableau to create many different views of a dataset for exploration
• Use R to create a scatterplot matrix of the dataset suitable for examining the relationships between multiple variables
• Use d3 to create an interactive view of the dataset
• Explore the different types of graphs that can be created with R and d3

## Week 4 - Human Perception - Ch 3

• (3.1) - Define perception
• (3.2) - Distinguish between rods and cones in the human visual system.
• (3.3.1) - Define preattentive processing
• (3.3.1) - Describe four preattentive visual tasks.
• (3.3.1) - Distinguish between a preattentive feature and a feature that cannot be detected preattentively.
• (3.3.3) - Explain why knowledge of the feature hierarchy and visual interference is important for visualization designers.
• (3.3.4) - Explain change blindness and the implication for visualization designers.
• (3.4.1) - Explain why perceptual balance and distinguishability are important characteristics in choosing colors.
• (3.4.4) - Describe the three main types of memory
• (3.5.4) - Explain the implication of Weber's Law for visualizations.
• (3.5.4) - Explain the implication of Stevens' Law for visualizations.
• (3.5.9) - Describe methods that could be used to expand humans' ability to perceive information accurately.

## Week 5 - Visualization Foundations - Ch 4

• (4.1) - Distinguish between the visualization measures expressiveness (Mexp) and effectiveness (Meff).
• (4.2) - Define semiology.
• (4.2.1) - Describe the process by which a graphic is analyzed.
• (4.3) - List the 8 visual variables and describe the potential purposes of each
• (4.3.5) - Define colormap.
• (4.5.2) - List the data types in the Shneiderman taxonomy.
• (4.5.2) - Describe the tasks in the Shneiderman taxonomy.

## Week 6 - 1D and 2D Graph Types

• Differentiate between tables and graphs and explain when each is appropriate
• Differentiate between line graphs and bar graphs and explain when each is appropriate
• Give examples of graph types that can be used to show relationships between multiple variables
• List graph types that can be used to show part-to-whole relationships
• Explain some of the common problems with the use of pie charts
• List graph types that can be used to show distributions
• Given a particular task, suggest a graph type and explain why it is appropriate

## Week 7/8 - Designing Effective Visualizations - Ch 12

• (12.1) - List the basic steps in creating a visualization
• (12.1.1) - Explain the importance of choosing an intuitive data to visualization mapping.
• (12.1.2) - Describe at least 3 types of view modifications that a user might want to make.
• (12.1.3) - Explain the importance of choosing the appropriate amount of information to display and the pitfalls of displaying either too little or too much information.
• (12.1.4) - Explain the importance of non-data marks (keys, labels, legends, gridlines) in a visualization and the pitfalls of including either too few or too many such marks.
• (12.1.5) - Explain the importance of the appropriate use of color in a visualization.
• (12.2) - Describe at least 3 ways in which misleading visualizations can be created.
• Critique visualizations, pointing out both good design and poor design.

## Week 9 - Multivariate Data - Ch 7

• (7.1, 7.2) - Describe the types of relationships that can be highlighted through the use of a line-based technique vs. a point-based technique.
• (7.2.2) - Describe the construction of a parallel coordinates plot and the types of data that it best suited for.
• (7.2.2) - Given a parallel coordinates plot, draw conclusions about a set of data.
• (7.3.2) - Describe a situation in which a heatmap might be a useful visualization.
• (7.4.1) - Describe the construction of a Chernoff face.
• (7.4.2) - Describe the idea behind dense pixel displays.
• (7.4.2) - Describe the construction of a pixel bar chart.

## Week 10 - Geospatial Data (Maps) - Ch 6

• (6.1) - Describe the four types of spatial phenomena as they relate to maps.
• (6.1.1) - Explain the importance of map projections and why there are so many different types of map projections.
• (6.1.2) - Describe each of the visual variables for spatial data.
• (6.3) - Explain the purpose of edge bundling.
• (6.4) - Distinguish between a choropleth map and a cartogram.
• (6.4) - List four different types of cartograms and their characteristics.

## Week 11 - Trees, Graphs, and Networks - Ch 8

• (8.1) - Differentiate between space-filling and non-space-filling (node-link) hierarchical techniques.
• (8.1.1) - Describe the basic algorithm used to construct a treemap.
• Recognize different approaches from the literature for hierarchical visualization.
• (8.2) - Explain how a tree is a special case of a graph.
• State Shneiderman's NetViz Nirvana requirements.
• Describe the four additional (on top of Amar et al.'s low-level tasks) user tasks for graph visualization.
• (8.2.1) - Describe how force-directed graph drawing methods work.
• Recognize different approaches from the literature for network visualization.
• (8.2) - Distinguish between node-link diagrams and matrix displays for visualizing networks.

## Week 12 - Text and Document Visualization - Ch 9

• (9.1) - Define corpus and metadata.
• (9.3) - Describe the vector space model.
• (9.3.1) - Compute the tf-idf for a particular word in a document.
• (9.3.2) - Explain the meaning of Zipf's law for the distribution of words in a corpora.
• Name some of the common tasks a user might want to perform on text data.
• Name advantages and disadvantages of using word clouds for document visualization.
• (9.4) - Name three different visualization methods for single document visualization.
• (9.5) - Name two different visualization methods for document collection visualization.
• (9.6) - Name at least one visualization method each for software, search results, temporal documents, and relationships between documents

## Week 13 - Storytelling

• Define the terms story and narrative visualization.
• List the six genres of narrative visualization.
• Describe the Martini glass structure of narrative visualization.
• Given a narrative visualization, describe the design strategies it uses.
• Describe how narrative visualization and presentation visualization differ from exploratory/analysis visualization, especially in terms of tools and approaches.

## Week 14 - Interaction

• List the seven categories of interaction tasks described in Yi et al.
• List the three high-level categories of interaction task types described in Heer and Shneiderman.
• Describe each of the 12 interaction task types described in Heer and Shneiderman, including an example.
• Explain why interaction is such an important part of information visualization and visual analytics.