Data Visualization

This is the course website for CS444, Data Visualization.

• Instructor: Carlos Scheidegger
• TA: Youhao Wei
• class email: fall16cs444@cs.arizona.edu Use this email for urgent class-related questions and comments. It will reach both Youhao and Carlos. For content discussions, please use Piazza.
• Lectures: Tuesdays and Thursdays, 9:30AM-10:45AM, GS906
• Office Hours
  • Carlos: Tuesdays, 1:00PM-3:30PM, GS734. Otherwise by appointment only.
  • Youhao: Tuesdays, 12:30AM-1:30PM, Fridays 11AM-12PM.

Welcome to CS444, Data Visualization. In this course, you will learn how, and why, to create data visualizations. Please read the syllabus carefully, and contact the instructor should you have any questions about its content.

A “visualization” is simply a visual representation of an object of our interest. It’s visual: we consume them with our eyes, and so it is essential that we know how our eyes work — and, more importantly, the parts of our brains connected to our eyes. It’s also a representation; we get to choose what this representation will be, and different choices lead to different pictures, some good and some bad. We will learn how to tell those apart, and how to make pictures that are more good than bad.

Good data visualization involves perceptual psychology, mathematics, and computer science. This makes our subject uniquely challenging: sometimes the way our eyes work stands in way of applying some beautiful result from computer science. Sometimes it’s the other way around: something deep about the math in the data will help guide the design process and let us make a picture that is beautiful, informative, and truthful.

The content of the course is split roughly in three distinct aspects: mechanics, principles, and techniques.

Course syllabus

The syllabus for the course is available here.

Class materials

Assignments

• Assignment 1 (past due, grades reported)
• Assignment 2 (past due, grades reported)
• Assignment 3 (past due, grades reported)
• Assignment 4 (past due, grades reported)
• Assignment 5 (past due, grades reported)
• Assignment 6 (past due, grades reported)
• Assignment 7 (past due, grades reported)
• Assignment 8 (past due, grades reported)
• Assignment 9 (past due, grades reported)
• Assignment 10 (past due, grades reported)
• Assignment 11 (posted)

Lectures

Intro	Aug 23	Introduction	slides
Mechanics	Aug 25	HTML/CSS/SVG Basics	no slides
	Aug 30	Javascript Basics	no slides
	Sep 1	Javascript + DOM, SVG	no slides
	Sep 6	d3 intro	no slides
	Sep 8	d3 joins and scales	no slides
Principles	Sep 13	Color vision	slides
	Sep 15	Color vision	slides
	Sep 20	Other perceptual channels	slides
		Assignment 3 discussion
	Sep 22	Other perceptual channels	slides
	Sep 27	Assignment 4 discussion
		Interaction	slides
	Sep 29	Design Criticism, Algebraic Design	slides
Techniques	Oct 4	Basic Spatial Arrangements	slides
	Oct 6	cont’d.
	Oct 11	High-Dimensional Data	slides
	Oct 13	High-Dimensional Data
Review	Oct 20	Review	slides
	Oct 25	MIDTERM
	Oct 27	Class canceled (VIS 2016)
	Nov 1st	Hierarchies	slides
	Nov 3rd	Graphs	slides
	Nov 8th	Graphs+Spatial Data	slides
	Nov 10th	Spatial Data	slides, slides 2
	Nov 15th	Spatial Data	slides 2
Topics	Nov 17th	Cartography	slides
	Nov 22nd	Large Data	slides
	Nov 24th	Thanksgiving, no class
	Nov 29th	Putting it all together
	Dec 1st	The Human Side of Data
	Dec 6th	Retrospective, Review	slides

Planned schedule

Mechanics

• the web browser as a dev environment; HTML; CSS
• DOM, SVG
• Javascript basics
• Higher-order Javascript
• d3 intro
• d3 selections, joins
• d3 scales, transitions

Principles

• Color Vision 1
• Color Vision 2
• Bivariate colormaps, other perceptual channels, Cleveland/McGill, Separable-Integral channels, Mackinlay’s APT.
• Pre-attentiveness, Gestalt, higher-level channels
• Interaction, Filtering, Aggregation
• Algebraic Design Process
• Design criticism

Techniques

• Line Plots, Dot Plots, Scatterplots, Small multiples
• Linear Algebra review, PCA
• MDS, Other dimensionality reduction 2
• Trees, Graphs, Hierarchies 1/2
• Trees, Graphs, Hierarchies 2/2
• Spatial Data 1/2: heatmaps, contour plots, vector fields
• Spatial Data 2/2: heatmaps, contour plots, vector fields
• Methods for large data; binning, sampling
• Uncertainty/Probabilistic Data

Planned material

Mechanics

• Week 2, HTML/CSS/SVG Basics (spr 2015 material).
• Week 3, JavaScript Basics (spr 2015 material).
• Week 3b, JavaScript for simple visualizations (spr 2015 material).
• Week 4, Odds and Ends (spr 2015 material): JavaScript Events, AJAX, HTTP.
• Week 4b, Introduction to d3 (spr 2015 material)
• Week 5, d3 selections and joins (spr 2015 material)

Principles

• Principles: Color (spr 2015 material).
• Principles: Other Channels (spr 2015 material).
• Principles: Preattentiveness, Gestalt (spr 2015 material).
• Principles: Interaction, Filtering, Aggregation (spr 2015 material).

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