CS 625 - Data Visualization
Fall 2020: TR 9:30-10:45am / online, 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.

We will be using several websites for class this semester. This site (https://www.cs.odu.edu/~mweigle/CS625-F20/) should be your first stop -- it contains links to all of the other sites that are needed.

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, 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 Zoom during the class meeting time, but this is not required. For more details on course delivery, see the syllabus.

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.

ODU Fall 2020 academic schedule

Week Date Topic Textbook Reading
1 Aug 18, 20 Introduction, What's Vis and Why Do It?
Objectives
Ch 1
2 Aug 25, 27 Data and Data Cleaning
Objectives
Ch 2
3 Sep 1, 3

Marks and Channels
Objectives

Ch 5
4 Sep 8, 10 Arrange Tables
Objectives
Ch 7
5 Sep 15, 17 Arrange Tables (continued) Ch 7
6 Sep 22, 24 Map Color and Other Channels
Objectives
Ch 10
7 Tues, Sep 29 Review
Thurs, Oct 1 MID-TERM EXAM
8 Oct 6, 8 Reduce Items
Objectives
Ch 13 (through 13.4.1)
9 Oct 13, 15 Exploratory Data Analysis (EDA)
10 Oct 20, 22 Storytelling Vis
11 Oct 27, 29 Rules of Thumb, Maps
Ch 6 Objectives, Ch 8 Objectives
Ch 6, Ch 8.1-8.3
12 Nov 3, 5 Multiple Views
Objectives
Ch 12 (skip 12.4.4)
13 Nov 10, 12 Manipulate View
Objectives
Ch 11 (through 11.4)
14 Nov 17 Class Wrap-Up