CS 485/584 - Information Visualization

Synchronous Lectures T/Th 11:30a - 12:45p EST

Data is everywhere, and it increasingly informs important decisions. For instance, analysis of cellphone data tracks the spread of a global pandemic, sensor data is collected and used to track and predict weather patterns, people sift through ratings and reviews to inform consumer purchases, researchers analyze patient data from medical trials to inform the success of new drugs, etc. Statistical summaries of data alone are often insufficient. People's perceptual skills allow us to see differences quickly and efficiently when data is visualized.

Furthermore, when visualization is combined with machine learning (in the form of visual analytic systems), there are dual benefits of human domain expertise and perceptual capabilities alongside machine accuracy and efficiency. In this course, students (1) will become acquainted with the fundamental theories in cognitive and perceptual psychology that drive visualization design, (2) will be introduced to the state-of-the-art in visualization research, and (3) will begin to develop the skills and proficiencies necessary to construct interactive visualizations using D3 and Tableau.

Course Logistics

When: T/Th 11:30a - 12:45p EST, synchronous
Where: Math & Science Center N304
Lecture Slides: Canvas
Assignments: GitHub Classroom
Discussion / Questions: Slack
Office Hours: here

Your attendance is crucial, because you will be working on your group projects in class. Your teammates will depend on your presence and engagement. Regular in-class quizzes will also count as part of your grade and cannot be made up outside of class unless your absence was university-approved.

People

Instructor: Dr. Emily Wall is an Assistant Professor at Emory University CS. Prior to joining Emory, she was a Postdoctoral Researcher at Northwestern University after completing her Ph.D. in Computer Science at Georgia Tech.

Mengyu (Bonnie) Chen (TA) is a first year CSI Ph.D. student. She joined Emory after completing her Masters at Chongqing University.

Kevin Wu (TA) is a senior majoring in Computer Science.

anscombes quartet
Anscombe's Quartet demonstrates four datasets that share the same statistical summaries yet represent very different datasets.


minards map
Charles Minard's map of Napoleon's march from Paris to Moscow during the French invasion of Russia.


crimean war deaths
Florence Nightingale's visualization of causes of death in the Crimean War.

Learning Objectives

Theory

  • Students should be able to critique a visualization design.
  • Students should be able to articulate and justify their designs using concepts related to visual perception and cognition.

Design

  • Students should be able to articulate a suite of visual representations and encodings and for which data types each is suitable.
  • Students should be able to design effective static and interactive visualizations.

Implementation

  • Students should develop working proficiency in creating custom visualization or visual analytic interfaces using tools such as D3 and Tableau.

Research

  • Students should understand a variety of topics in visualization research.
  • (Graduate) students should be able to critically engage with research by synthesizing ideas from the literature on a given topic.

Engagement

  • Students should engage in discussions in-class and online.
  • Students should complete in-class design activities.

Assignments & Grading

Complete assignment descriptions are available on GitHub Classroom. All assignments are due at 11:59pm EST on the due date.


Objective Weight (Grad) Weight (Undergrad) Description
15% 15% Reading Quizzes.
12% 15% Participation.
25% 30% Homework Assignments.
36% 40% Group Project.
12% (extra credit) Debate.
Alternatively, you may forego all of these assignments if you accept and successfully pursue the Straight-A Shortcut.

Schedule

= Optional Reading = No Preparation = No Lecture = Homework = Group Project Milestone

Week Date Topic Reading Assignments Due
1 8-25 Introduction to Information Visualization The Value of Information Visualization, Fekete et al
Using Vision to Think, Card et al.
Complete Background Survey
2 8-30 HTML and Javascript Basics
Tutorial by: Mengyu
HTML in Data Visualization with D3 by Kelleher
Javascript Basics by NYU Visualization
0: Web Development
9-01 Data
Virtual @ Dagstuhl
The Eyes Have It, Shneiderman
3 9-06 Perception Perception in Visualization, Healey 1: Design Static Visualizations
9-08 D3 Data Transformation, Selection, and Joins
Tutorial by: Kevin
Data Transformation by NYU Visualization
Selections and Joins by NYU Visualization
Bar Chart Walkthrough by NYU Visualization
4 9-13 Charts & Visual Encodings A Tour Through the Visualization Zoo, Heer et al.
Effectively Communicating Numbers, Few
Rippin' the Rainbow a New One
2: Implement a Bar Chart
Check out your assigned date for the Debate
9-15 Group Project Pitch & Group Formation 1: Project Pitch
5 9-20 D3 Fundamental Graphs and Interaction
Tutorial by: Mengyu
Fundamental Graphs with D3 by NYU Visualization
Interaction by NYU Visualization
Networks by NYU Visualization
Last day to inform instructor and TAs if you will pursue the Straight-A Shortcut
9-22 Interaction In Defense of Interactive Graphics, Aisch
The death of interactive infographics, Baur
6 9-27 Communication & Storytelling TED Talk, David McCandless 3: Implement an Interactive Network -- Now due Friday, Sep. 30
9-29 Visual Analytics Why Visual Analytics 2: Short Project Statement
7 10-04 Data & Visualization Ethics
Part I
Dear Data
Lies, Distortions, and Misrepresentations in Data, Correll
Data Feminism, Klein
10-06 Data & Visualization Ethics
Part II
ProPublica Article, Larson et al.
Hidden Brain w/ Daniel Kahneman
4: Design Ethical Visualizations
8 10-11 Fall Break
10-13 Exploratory Data Analysis Skim Resources on Tableau
9 10-18 Design Principles
Virtual @ IEEE VIS
What Makes a Visualization Memorable, Borkin et al. 5: Conduct Exploratory Data Analysis with Tableau
10-20 Uncertainty & Animation
Virtual @ IEEE VIS
The Visual Uncertainty Experience, Hullman
When(ish) Is My Bus, Kay et al.
Hypothetical Outcome Plots, Hullman et al.
10 10-25 Evaluation
Part I
Multi-Dimensional In-Depth Long-term Case Studies, Shneiderman and Plaisant 6: IEEE VIS Travel Report
10-27 Evaluation
Part II
A Heuristic Approach to Value-Driven Evaluation of Visualizations, Wall et al.
11 11-01 Group Project Poster Session 3: Project Design Document
11-03 Networks & Trees A Visual Bibliography of Tree Visualization, Schulz
Graphiti, Srinivasan et al.
12 11-08 Design Lab
Maps & Networks
11-10 Tables & Text Lineup: Visual analysis of multi-attribute rankings, Gratzl et al.
Jigsaw, Stasko et al.
13 11-15 Geospatial Visualization
Guest Lecture: Dr. Clio Andris, Georgia Tech
Skim Tableau GeoVis Primer 7: Critique a Visualization
11-17 VIS Research Flash Talks
Guest Speakers: Yanan Da, Shrey Gupta, & Mengyu Chen
14 11-22 Tasks & Visualization Models
Enjoy the holiday
Knowledge Generation Model for Visual Analytics, Sacha et al. 4: Status Update
11-24 Thanksgiving
15 11-29 Group Debate, Topic 3
+ Time Series
Streamgraphs, Schwabish and Pazos
EventFlow, Monroe et al.
12-01 Group Project Presentations
16 12-06 Group Project Presentations Final Deliverables
12-13 (Final Exam period: 11:30a - 2:00p)
No exam

Resources

Technology Requisites

D3

Tableau (free version available via Tableau for Higher Education)

Github

Optional Resources


Programming

D3: Examples; Tutorials; Documentation

Front-end Web Stack:
Tutorials: From Tamara Munzner's VIS Course
HTML: Mozilla Dev Network
CSS: Mozilla Dev Network; CSS Zen Garden
SVG: Tutorial
Javascript: Mozilla Dev Network; Javascript Garden; Eloquent Javascript; Design Patterns
Javascript Frameworks: React; Vue
Syntax Alternatives: Typescript

Other Vis Toolkits: Vega; Vega-Lite; P5.js and Processing; Matplotlib and Seaborn (for Python); GGPlot2 (for R)

Github: Tutorials

Web Scraping: Beautiful Soup (Python)

Systems, Data, Colors, etc.

Systems: Tableau; Spotfire; Qlik

Data: Census.gov; Data.gov; Tableau Data; /r/opendata; Quandl; Metro Boston Data Common; CDC; Real Climate; UK Office for National Statistics; World Bank Data Catalog; Basketball; UN Data; WHO Data; OECD Stats

Color: Color Brewer 2.0; 0 to 255

Example Visualizations: Data Visualization and the Modern Imagination; Baby Name Wizard

Document Scanning: CamScanner (useful for scanning in paper sketches to submit assignments online)

Policies

Grading Philosophy

For many of you, most CS assignments that you’ve had up until this point had clear, crisply defined goals that mapped cleanly to point values. This is impossible in a course that partially relies on design. Simply checking off each of the TODOs on an assignment does not necessarily mean that you’ve created a visualization that is easy and compelling to use. Rather than simply ask “Did you do this?”, there will be the question of “Did you do this well?”. This often translates to “Did you successfully apply the concepts we learned in class to this assignment?”. For example, if you build an app that is functional but breaks many visualization design guidelines or heuristics… that is not a successful application in this class.

Peer Evaluation: Some of the assignments that you complete will include some kind of peer evaluation. We will be critiquing each other’s work throughout the semester using the framing of I Like, I Wish, What If from Stanford’s design school.

Group Work: Group work can be challenging. As a result, in your group project milestones, you will submit a brief assessment of you and your classmates’ work. At the end of the semester, I may use these assessments to reweight the group portion of your grade (either positively or negatively).

Academic Honesty

Emory aims to cultivate a community based on trust, academic integrity, and honor. Students are expected to act according to the highest ethical standards. For information on Emory’s Honor Code, please visit here.

Any student suspected of cheating or plagiarizing on a quiz, exam, or assignment will be reported to the Office of Student Conduct, who will investigate the incident and identify the appropriate penalty for violations.

Unless explicitly stated otherwise, you are expected to complete assignments on your own. It is appropriate to discuss your ideas with others to gain feedback and help with sticky problems. It is not appropriate to find an existing solution online or from your friends, modify them, and submit as your own work. If in doubt, confer with your instructor. It is much easier to ask about these things than handle the consequences of a poor decision.

Timeliness

All assignments are due at the start of class on the day listed in the schedule. You will have a total of 3 “free” late days to use for any homework assignments as needed throughout the course (e.g., you can use 1 late day for HW1 and 1 for HW3, …). These "free" days can apply only to homework assignments and cannot be used for research presentations or group projects. These are for any cases where Institute-approved absences do not apply, and no reason must be given to use them. After the 3 “free” late days are used up, any late assignments will receive a 10% per day penalty. Assignments turned in one week or later past the due date will not be graded and given a 0. Note that you have to clearly note on your assignment if you want to apply your late days. This has to be done at the time of submission, not later in the course. Once you use them, you cannot switch them later in the course, so plan wisely.

Office of Accessibility Services

Your success in this class is important to me. We all need accommodations because we all learn differently. If there are aspects of this course that prevent you from learning or exclude you, let me know as soon as possible. Together we'll develop strategies to meet both your needs and the requirements of the course.

I encourage you to visit the Office of Accessibility Services to determine how you could improve your learning as well. If you need official accommodations, you have a right to have these met. Students must renew their accommodation letter every semester they attend classes. Contact the Office of Accessibility Services for more information at (404) 727-9877 or email at accessibility@emory.edu. Additional information is available at the OAS website.

Regrade Policy

You can request a re-grade of an assignment within seven days of releasing the grade by sending an email to the course staff. The request should contain a written explanation of why you think that the grade is incorrect. We will look over your work again upon request. If we spot errors in grading, we will fix the error. This may end up assigning a lower score than the original if we find additional errors.

Student Support Services

In your time at Emory, you may find yourself in need of support. Here you will find some resources to support you both as a student and as a person.

Contact

Instructor: Dr. Emily Wall

emily.wall@emory.edu

Office Hours: T 12:45pm-1:45pm (right after class) in Math & Science Center W302E or virtually by appointment via Zoom

emilywall.github.io

Teaching Assistant: Mengyu Chen

mengyu.chen@emory.edu

Office Hours: Th 2:30p-3:30p in Math & Science Center E308 or virtually by appointment

Teaching Assistant: Kevin Wu

kevin.wu2@emory.edu

Office Hours: W 5p-6p in Math & Science Center E308 or virtually by appointment