CS4460 - Introduction to Information Visualization
Instructor: Alex Endert
Spring 2023
Lecture: Monday, Wednesday 2:00 - 3:15pm, Howey L2
Recitation: Friday, 5:00-6:15pm, D.M. Smith 105


Information visualization is an area of research that helps people analyze and understand data using visualization techniques. The multi-disciplinary area draws from other areas of science, including human-computer interaction, data science, psychology, and art to develop new visualization methods and understand how (and why) they are effective.

Information visualization methods are applied to data from many different application domains, including:

  • Political reporting and forecasting – as seen on TV and in the papers in election season. News reporting – look at the interactive visualizations used by the New York Times, Wall Street Journal, Slate, etc.
  • Social science and economics data, such as census and other surveys, and micro and macro economic trends.
  • Social networking and web traffic, to understand patterns of communication
  • Business intelligence and business dashboards – to forecast sales trends, understand competitive marketplace positions, allocate resources, manage production and logistics.
  • Text analysis – to determine trends and relationships for literary analysis and for information retrieval.
  • Criminal investigations – to portray the relationships between event, people, places and things.
  • Performance analysis of computer networks and systems.
  • Software engineering – developing, debugging and maintaining software.
  • Bioinformatics, to understand DNA, gene expressions, systems biology.

Course objectives

  • Learn the principles involved in designing effective information visualizations.
  • Understand the wide variety of information visualizations and know what visualizations are appropriate for various types of data and for different goals.
  • Understand how to design and implement information visualizations.
  • Know how information visualizations use dynamic interaction methods to help users understand data.
  • Learn to apply an understanding of human perceptual and cognitive capabilities to the design of information visualizations.
  • Develop skills in critiquing different visualization techniques in the context of user goals and objectives. Learn how to implement compelling information visualizations.


There are no required textbook for this course. However, a free textbook that may help with learning principles of web-based visualization development is: Interactive Data Visualization for the Web, Scott Murray, O’Reilly Media, ISBN 9781449339739. While on the GT VPN, you can access this for free at this link.

Optional readings that may be helpful for those who want to learn more about the material include:

  • For those interested in design: Any of Edward Tufte’s three books: The Visual Display of Quantitative Information; Envisioning Information; and Visual Explanations.
  • For those interested in business intelligence and business dashboards: Wayne Eckerson, Performance Dashboards: Measuring, Monitoring, and Managing Your Business, Wiley, 2005, ISBN 978-0471724179
  • For those interested in Network Visualization, particularly Social Networks: Hansen, Shneiderman and Smith, Analyzing Social Media Networks with NodeXL, Morgan Kaufman, 2011, ISBN 978-0-12-382229-1.
  • For those interested in the psychological/perceptual factors affecting information visualization: Colin Ware, Information Visualization: Perception for Design, 2nd Edition, Morgan Kaufman Elsevier 2004, ISBN 978-1558608191.
  • For a deeper treatment of many aspects of InfoVIz: Visualization Analysis and Design, Tamara Munzer, CRC Press 2014, ISBN 9781466508910.

Course Format

The course will follow a lecture/seminar style with discussions, demonstrations of InfoVis software, viewing of videos, hands-on experience with information visualization software, and in-class activities. An important aspect of this learning is being present in class. While slides give key points and high-level topics discussed, much of the content of the course comes through the discussion of visualizations, and other in-class activities. If you want to do well, attending class is important.


There are scheduled recitation sessions that are part of this course. Attendance to these are not required, and no graded materials will be part of these sessions. However, they will include helpful topics and skills that go along with the materials for the course. Recitation will not meet each week. Check the schedule on the Canvas page for topics and days.


All assignments are due at 11:59pm on Friday. Late work will receive a 10% per day penalty. After 5 days, a 0% will be given and no submission will be accepted. Too much other work, gone for the weekend, ran out of paper etc. are not emergencies. Advance notification to the instructor and TAs is expected in all but the most severe emergency situations.

However, it is understandable that life events and other reasons come up that may require you to miss a deadline. As such, each student is given 2 “late days” that you can use throughout the semester. If you want to use any of your late days, add a note to the canvas submission at the time you submit. You cannot apply late days to assignments at the end of the semester or days after you submit your assignment. These are intended to be used in a situation where life events happen, not to retractively apply them at the end of the semester.

Canvas, Ed Discussion, Github

Canvas will be used for electronic submissions used for most of the assignments, and for recording grades. Ed Discussion will be used for asking questions. For contacting the instructor or the TAs, please use email or private Canvas messages. The labs will be released on GitHub.


Final course grades may be curved (but not always). Grades of individual assignments will not be curved. If a curve is given, it will only be curved up (not down). Any regrade requests should be emailed directly to Prof. Endert and need to describe the detailed concern for the regrade. Grading distributions for this course are:

Component Weight
HW Assignments 30%
- HW1: Find and Critique a Vis 5%
- HW2: Data Exploration and Analysis 7%
- HW3: Ethical Visualization Design 8%
- HW4: Tableau 10%
Labs 40%
- Lab1 1%
- Lab2 2%
- Lab3 3%
- Lab4 4%
- Lab5 7%
- Lab6 8%
- Lab7 15%
Test 1 15%
Test 2 15%

Expectations and Academic Integrity

Mutual expectations. At Georgia Tech we believe that it is important to continually strive for an atmosphere of mutual respect, acknowledgement, and responsibility between faculty members and the student body. See http://www.catalog.gatech.edu/rules/22/ for an articulation of some basic expectations – that you can have of me, and that I have of you. In the end, simple respect for knowledge, hard work, and cordial interactions will help build the environment we seek. I encourage you to remain committed to the ideals of Georgia Tech while in this class, and always.

Attendance is expected. Institute approved absences will be accommodated, as will absences for interviews, conferences, etc. Notify us, by email or direct Canvas messages, if you will miss class for one of these two reasons (if you feel some other reason for absence is reasonable, email us, but again, in advance).

Contacting your instructor and TA. For communication with TAs and Instructors, please use Canvas messages. Email will work ok, but it will likely take longer to get a response due to flooded inboxes. Ed Discussion works well for questions that you think others in class might have, or other students might be able to answer. If you use email, please include [CS4460] in the subject line.

Collaboration and academic honesty. Georgia Tech 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 Georgia Tech’s Academic Honor Code, please visit http://www.catalog.gatech.edu/policies/honor-code/ or http://www.catalog.gatech.edu/rules/18/.

Any student suspected of cheating or plagiarizing on a test, assignment, or project will be reported to the Office of Student Integrity, who will investigate the incident and if needed identify the appropriate penalty for violations.

Unless explicitly stated otherwise, you are expected to do coursework on your own.

In-class use of computers, cell phones and tablets. Please use your technology appropriately while in class. Using computers. tablets, smartphones, watches, VR headsets, etc. in a way that reinforces the educational context, such as taking notes or visiting a web site being discussed, is appropriate. Reading email, playing games, browsing social media, watching Netflix, doing your HW assignments, purchasing football tickets, web browsing, etc. are not appropriate. Not only does this detract from your learning, it unavoidably distracts those sitting near you. Also, incoming emails and alerts are distracting. Even note-taking on your computer may not be such a great idea: studies have shown that note-taking by hand has been shown to be more efficient for learning (also see this news story), as opposed to by computer, but that’s your call. In short, it’s really in your best interest to take the 75 minutes out of your day, disconnect from the internet, and engage in the course.

Also, understand that this course is about data visualization. We will spend significant class time showing slides of visualizations and discussing them. The content of the discussion is not captured in the slides, yet you are expected to take notes, learn, and be tested on it.

Accommodations for students with disabilities. If you are a student with learning needs that require special accommodation, contact the Office of Disability Services (often referred to as ADAPTS) at http://disabilityservices.gatech.edu/, as soon as possible, to make an appointment to discuss your special needs and to obtain an accommodations letter. Please also e-mail your instructor as soon as possible in order to set up a time to discuss your learning needs.

Student Support Services. In your time at Georgia Tech, 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.

Software. One of the assignments is to analyze data using Tableau. Tableau’s data visualization software is provided through the Tableau for Teaching program.