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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).

The following are some historic examples of assignments, intended to give you some context for the grading scheme. Note that none of these are very long, but they all show different degrees of connection to the readings (and in the case of the Seek and Find examples, to the visualizations). We do not take formatting or (minor) spelling/grammar errors into account when grading.

Since grades for assignments are assigned based on overall quality across multiple answers, these examples are only provided to show expectations.

Each module will have required readings in the first two weeks. The first week will have backbone and core readings. The second week will have advanced and experientialmodern and historic readings. Modules will also suggest optional readings.

This class has many components (assignment types). There are Lectures, In Class Activities, Reading Surveys, Content Surveys, Anonymous Surveys, Seek and Finds, Design Warmups, and Design Exercises.

Summary: Use AI tools (Claude, ChatGPT, Co-Pilot, etc.) to help you in your learning, not to do the assignments for you. Treat them like another student: you can get them to help you, but you are responsible for your own work. Give them proper credit / attribution. Don’t claim their work as your own.

Course learning outcomes explain what we are trying to achieve in the course. For the student, it gives you a sense of what I (as instructor) am trying to do. As instructor, it guides the design of the class.

This class is a principles-and-design-oriented exploration of Data Visualization. This posting will explain the content, style, and rationale for the class. The class is different from most other CS classes.

Here’s what you should do to get started…

1. Understand what this class is about - please read the What and Why page (and maybe the Course Learning Outcomes) which will give you a sense of what this class is about. Reading the first tutorials can give you the philosophy of the course in a nutshell. This isn’t your standard CS class. If this class isn’t for you, we won’t be offended if you drop early.
2. Understand how the class will work - look at Policy Overview page to get an idea of the mechanics. The Syllabus has details about policies, but also a bit of the description of the content.
3. Be prepared for class - class attendance is required. Please bring drawing supplies (pens or pencils, in multiple colors - I recommend black + four other colors)

   Attendance is part of class. This class involves in-class experiences. This requires students to be in class - if you are not in class for the in-class experience, you won’t have the in-class experience. If you know that you will miss a lot of classes, or do not intend to come to class “nearly all of the time”, please do not take the class.

   Read more…

Summarizing the policy related contents of the Syllabus…

This is a Syllabus in the form of the official University Template. It details the course policies. A summary of course policies is available at Policy Overview

More information is on the course web page: https://pages.graphics.cs.wisc.edu/765-26/.