Schedule

The class is organized by week - each week follows the Rhythm. If you’re curious about what is going on this week, you can look at the “this week in vis” sidebar (top right of the page), or the Canvas Calendar to see things in a calendar format, or the Canvas Agenda to see a list of assignment deadlines.

This page has a more thematic outline of the course weeks. I have also written them out in terms of Learning Goals.

You can also see All Readings which puts the semester’s worth of readings on one page, which should give you can idea of the topics as well.

A warning: the actual schedule details may change - anything more than a week or two ahead is uncertain.

Week 1: What is Visualization?

To start with, we ask the question “What is Visualization” in terms of what the topic is, the academic discipline, and also the class.

Week 2: Why Visualize? How to Visualize?

Once you know what visualization is, the obvious next question is to understand why we might want to do it. What are the alternatives? What are the pros and cons?

The other question is to begin to understand how to do visualization. In the sense of how do we think about design process and specifically critique, as its a key tool for learning about visualization and making good visualizations.

Week 3: Abstractions: Data and Task Abstraction

Two key elements to visualization are task (what are we trying to do with a visualization) and data (what information are we trying to show). We need abstraction to help us examine these two things, otherwise we would get too mired in the details of each scenario we need to consider.

Week 4: Encodings

Encodings - the mappings between data and things we can see - are the building blocks of visualization. By learning to think about visualizations in terms of these building blocks, rather than total designs, we can more easily create designs (by putting encodings together to create visualizations) as well as to understand them (breaking visualizations into parts we can understand).

Week 5: Implementation

Once we’ve designed a visualization, we need to make it - that’s where implementation comes in. Our goal is to understand the range of available tools, and how we might choose among them. We won’t focus too much on any specific tool.

Week 6: Scale

A fundamental challenge in visualization is dealing with scale - when we have “too much stuff”. We might have too many data items, data items that are too big to show, too much complexity in the relationships between data items, etc. We will look at general strategies for how we can address challenges of scale.

Week 7: High-Dimensional Data

A specific type of scalability problem is when we need to consider multiple dimensions of data together. We will look at basic strategies for handling these kinds of cases.

Week 8: Why does Visualization Work?

This week, we’ll survey some of the cognitive and perceptual foundations of Vis. And we’ll look at the problem of comparison.

Week 9: Interaction

Interaction is a powerful tool to use in designing visualizations. We’ll look at how to think about interaction in visualization design, what it can be used for, and the “costs” of using it.

Week 10: Perception

Understanding how people see can help us understand visualization. Of course, Visual Perception is an entire field unto itself, so we’ll just try to get some of the most relevant basics that relate to visualization. Our focus will be to see how we can use an understanding of how we see to better design visualizations.

Week 11: Color

While we have been using color all along, in this week, we’ll look at it carefully, starting with the perceptual issues and then moving on to the more practical and design oriented issues.

Week 12: Graphs (Networks)

Graphs (as in “networks” not charts) are a common and important data type (or kind of data type). They have unique challenges and a rich literature. We can spend a whole semester on graphs - but we won’t. One of the key points is that while graphs are node and link data, there are more ways to show a graph than a node-link diagram. We’ll also touch on the basics on how to draw node link diagrams.

Week 13: Evaluation: How do we know it’s any good?

Hopefully, this question of assessing a visualization has been core throughout the class. But in this week, we’ll focus on strategies for evaluating visualizations. This is important as part of our design process.

Week 14: Leftover topics (Uncertainty and 3D)

We’ll try to cram in one or two “big” topics that we didn’t to discuss over the course of the semester. It might be 3D, it might be “scientific visualization”, or it might be uncertainty. The topics are interconnected.

3D refers to showing data that really exists in the 3 spatial dimensions of the world (4 if you count time). Scientific visualization refers to the challenges faced with “traditional” scientific data sets (such as volumes and medical images), which are often 3D. Uncertainty refers data where we don’t know things precisely. All of these are big topics in visualization, and I hope to give the class at least some exposure to them.

Week 15: Research and Presentations

I always like to take some time in a graduate level class to discuss presentations. It’s a skill we all need, so it is worth taking time to learn about. It does directly relate to visualization: they are both about communication, and often we use visual elements (such as slides) in our presentations.

We will also take a look at what current research in visualization is like.