Modules
Module 0: Welcome: What is Vis?
Description
A short introduction (2 days over two week) to the topic and the class. We introduce a broad definition of visualizations as solutions to user data tasks and consider how they can be effective at this. We’ll use the basic notion of “what does a visualization make easy to see” to develop intuitions about what makes visualizations effective.
Learning Outcomes
- To understand our broad definition of visualization as the solution to “data problems” (tasks) and the notion of effectiveness (which requires tasks)
- To get some intuitions about “what is easy to see” (as a way to appreciate effective visualizations)
- To get some exposure to design process
- Be set with class mechanics
Module 1: Building Blocks
Description
We will look at how we can consider visualizations in terms of building blocks including data and task abstractions and visual encodings. We will see how this allows designing and analyzing visualizations. We will practice design process (including critique). Students will begin to design and analyze visualizations (pen-and-paper).
Learning Outcomes
- have practice with critique to enable learning from examples going forward.
- have a vocabulary for describing data (data abstraction) to help us connect it to visual representations.
- understand the many different ways that we might talk about task, in ways that help us design visualizations that address tasks.
- be able to describe a range of different visual encodings and describe visualizations in terms of these building blocks.
Module 2: Methodology: Making and Using Visualizations
Description
We will look at strategies for implementing (creating) visualizations and how visualization can be used for exploring data (in addition to communicating stories with it). We will also consider design strategies for two common issues: comparison and scalability.
Learning Outcomes
- Select appropriate implementation strategies based on an awareness of a wide range of available approaches and tools.
- Understand the range of strategies for visualization implementation strategies.
- Understand the main tradeoffs in implementation strategies.
- Exposure to examples of the different types of tools.
- Understanding of the different levels of abstraction of visualization tools/toolkits
- Practice making exploratory visualizations (quick visualization designed to expose interesting aspects of the data)
- Connect visualization to statistical/data science abstractions
- Apply concepts from interaction to address visualization challenges
- Describe scalability problems in a broad/abstract way and apply the basic strategies to achieve them.
Module 3: Principles and Formalism
Description
In this module, we will try to be more “scientific” about how we approach visualizations: bringing more formality into how we evaluate visualization, examining studies to build our understanding, and exploring the cognitive and perceptual foundations. We start with evaluation: why assessing visualizations is hard, and the nested model as a way to locate where a design succeeds or fails. We look at empirical studies: how experiments produce knowledge about visualization, and how to read them critically. Then we turn to the viewer: the perceptual and cognitive foundations of visualization, color (a surprisingly complicated topic), and the cognitive biases that affect how visualizations are read and designed. Throughout, we connect the science to design practice, and students continue to create and critique visualizations.
Learning Outcomes
- Understand the challenges of evaluating visualizations, be aware of the strategies, and apply the nested model to locate where a design succeeds or fails.
- Read empirical research critically and apply its findings to visualization design and analysis.
- Describe the human visual system and identify its implications for visualization design.
- Explain the basics of the physical and perceptual aspects of color and apply them to visualization design.
- Identify cognitive mechanisms that influence visualization effectiveness.
- Be aware of cognitive biases that affect how visualizations are read and designed.
Module 4: Standard (Hard) Problems
Description
We will look at some standard “hard problems” that come up including hard data properties (e.g., uncertainty), data types (e.g., networks, high-dimensional records), and applications. We will look at some of the standard approaches to address these problems. The design exercises will give more practice at creating and critiquing visualizations.
Learning Outcomes
- Identify network (graph) data and describe standard visualization approaches for it
- Identify high-dimensional (multivariate) data and describe standard visualization approaches for it
- Describe standard approaches for visualizing uncertainty in data
- Describe standard approaches for visualizing set data
- Identify standard challenges and approaches to standard scientific data types
Module 5: Wrap Up
Description
The last week of class.
Learning Outcomes
- Assemble a portfolio of visualizations from the semester
Leftovers
Description
Holding pen for outcomes that have not yet been placed
Learning Outcomes
- Apply principles to create and critique effective presentations
- Identify ethical and societal implications of visualization
- Apply basic principles of visual design
- Practice creating and critiquing visualizations
- Ethics