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




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Course Overview

Catalog Description: This course covers the theory and application of data visualization. This includes issues in data cleaning to prepare data for visualization, theory behind mapping data to appropriate visual representations, introduction to visual analytics, and tools used for data analysis and visualization. Modern visualization software and tools will be used to analyze and visualize real-world datasets to reinforce the concepts covered in the course.

Comparison to CS 725/825: If you have already taken CS 725/825, then CS 625 is not appropriate for you. Future offerings of CS 725/825 may require (or strongly recommend) CS 625 as a prerequisite.

Course Organization

This course will be organized based on the "flipped classroom" model. Students will be assigned readings and homework that will be due before class time. There will be few, if any, lectures by the instructor. Class time will be spent on discussions of the readings and assignments, demonstrations, student presentations, and in-class work. It is essential that each student be fully prepared to participate in class discussions each week.

This course is offered as a "hybrid" course, supporting both in-class and online students. The audio of class meetings and all materials projected in class are recorded for later viewing by our online students (as well as for review by in-class students). Online students may also connect via WebEx during the class meeting time, but this is not required. In-class exercises are optional, but will be provided for all students. Online students may solicit feedback on their in-class exercises from other students or the instructor. Unless otherwise specified, online students must meet the same deadlines as in-class students.

Announcements, submission of assignments, and grading will be done via the class Blackboard site.

In-class work and hosting of code will be done via GitHub.

The required textbook for this course is Visualization Analysis and Design by Tamara Munzner.

Course Objectives

After completing this course, you should be able to do the following:

  • Use OpenRefine to explore and clean real-world data.
  • Explain the difference between exploratory (discover task) and explanatory (present task) visualizations.
  • Describe the channels of visual encoding and order them from most effective to least effective.
  • Identify a visualization where an inappropriate arrange design choice was made and explain why the choice was inappropriate.
  • Explain how different data types map most effectively to various visualization idioms (i.e., charts).
  • Explain the importance choosing an appropriate colormap.
  • Given a dataset, develop questions about the data that can effectively be answered with a visualization.
  • Critique and redesign an existing visualization.
  • Use Tableau, R, Vega-Lite, or other API or software to create effective standard charts, such as line charts, scatterplots, bar charts.
  • Use Tableau, R, Vega-Lite, or other API or software to create an effective visualization of real-world data.
  • Explain and defend the design choices that you made in creating your visualization.

Summary Schedule

Note: This is a tentative schedule and may be updated during the semester.

Week Date Topic Textbook Reading Homework Assigned Homework Due
1 Aug 27 Introduction, What's Vis and Why Do It?
Ch 1 HW1 HW0
2 Sep 3 Data and Data Cleaning
Ch 2 HW2 HW1
3 Sep 10

Marks and Channels

Ch 5 HW2
4 Sep 17 Arrange Tables
Ch 7 HW3
5 Sep 24 Arrange Tables (continued) Ch 7
6 Oct 1 Map Color and Other Channels
Ch 10 HW3
Oct 15 No Class - Fall Break
8 Oct 22 Reduce Items
Ch 13 (through 13.4.1) HW5 HW4
9 Oct 29 Exploratory Data Analysis (EDA) HW6 HW5
10 Nov 5 Storytelling Vis HW7 HW6
11 Nov 12 Rules of Thumb, Maps
Ch 6 Objectives, Ch 8 Objectives
Ch 6, Ch 8.1-8.3
12 Nov 19 Multiple Views
Ch 12 (skip 12.4.4) HW8 HW7
13 Nov 26 Manipulate View
Ch 11 (through 11.4) HW8