CS 4460 Introduction to Information Visualization
Pre-Reading: The Value of Information Visualization
Data is everywhere. It helps us to make informed decisions. However, it is always overwhelming to interpret the “raw” data. Visualizations are tools that translate the raw data into **meaningful graphics**. They take advantage of the **powerful human visual system** to summarize data in a cognitively efficient way, making them popular in **science, analysis, and the media**.
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.
Time: Tuesday and Thursday 11:00am -12:15PM
Location: Scheller College of Business | Room 300
Instructor: Cindy Xiong
Office Hours: Tuesdays 12:15-1:00 pm or by appointment
Email: cxiong[at]gatech[dot]edu
Office Location: TSRB 332 or via ZOOM (link provided upon request)
TAs: (Office hours starting from Week 2)
Sean Ru
Email: sru3[at]gatech[dot]edu
Office Hours: Monday 3:00 PM – 4:00 PM
Office Location: TSRB 334
Abhinav Vinod
Email:avinod34[at]gatech[dot]edu
Office Hours: Tuesday 3:30 PM – 4:30 PM
Office Location: TSRB 334
Sichen Jin
Email:sjin86[at]gatech[dot]edu
Office Hours: Wednesdays: 4:00 PM – 5:00 PM
Office Location: SEE ZOOM LINK ON CANVAS
Ananya Kumar
Email:akumar757[at]gatech[dot]edu
Office Hours: Thursdays 9:30 AM – 10:30 AM
Office Location: SEE ZOOM LINK ON CANVAS
Textbooks
There are no required textbooks for this course. However, a free textbook that may help with learning the 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. Or visit http://go.oreilly.com/GATech and then search the name of the book.
Course Objectives and Learning Outcome
- 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.
| Monday | Tuesday | Wednesday | Thursday | Friday | |
|---|---|---|---|---|---|
| Week 1 | Jan 6 | Jan 7 Introduction | Jan 8 | Jan 9 InfoVis Overview (Pre-reading: Here) | Jan 10 |
| Week 2 | Jan 13 | Jan 14 Visual Encoding and Basic Charts | Jan 15 | Jan 16 Perception (Pre-Reading: Here) | Jan 17 Lab 1 Due (11:59PM) |
| Week 3 | Jan 20 (Holiday) | Jan 21 User Tasks and Sensemaking (Pre-Reading: Here) | Jan 22 | Jan 23 Communication and Storytelling (Pre-Reading: Here) | Jan 24 Lab 2 Due (11:59PM) |
| Week 4 | Jan 27 | Jan 28 Multivariate Data Presentations Coordinates, Line, Axes, Colors | Jan 29 | Jan 30 Unit Charts and Infographic (Pre-Reading: Here) | Jan 31 Homework 1 Due (11:59PM) Find it on CANVAS |
| Week 5 | Feb 3 | Feb 4 Design Principles | Feb 5 | Feb 6 User Interaction | Feb 7 Lab 3 Due (11:59PM) |
| Week 6 | Feb 10 | Feb 11 Uncertainty | Feb 12 | Feb 13 Animation | Feb 14 Homework 2 Due (11:59PM) Find it on CANVAS |
| Week 7 | Feb 17 | Feb 18 ★ Test 1 ★ | Feb 19 | Feb 20 Text Visualization (Pre-Reading: Here) | Feb 21 Lab 4 Due (11:59PM) |
| Week 8 | Feb 24 | Feb 25 Graphs, Networks, Hierarchies, and Trees | Feb 26 | Feb 27 Visual Analytics, Time Series, and Temporal Data (Pre-Reading: Here) | Feb 28 Homework 3 Due (11:59PM) Find it on CANVAS |
| Week 9 | Mar 3 | Mar 4 Geospatial Visualizations (Guest Lecture!) |
Mar 5 | Mar 6 Cognitive Bias in Data Decision-Making | Mar 7 Homework 4 Due (11:59PM) Find it on CANVAS |
| Week 10 | Mar 10 | Mar 11 Data Humanism, Bias, and Ethics | Mar 12 | Mar 13 (No Class) Catch-Up Day | Mar 14 |
| Week 11 Spring Break | Mar 17 (Spring Break) | Mar 18 (Spring Break) | Mar 19 (Spring Break) | Mar 20 (Spring Break) | Mar 21 (Spring Break) |
| Week 12 | Mar 24 | Mar 25 Evaluation I | Mar 26 | Mar 27 Evaluation II | Mar 28 Lab 5 Due (11:59PM) |
| Week 13 | Mar 31 | Apr 1 (No Class) Catch-Up Day | Apr 2 | Apr 3 Natural Language Interfaces for Visualization | Apr 4 Lab 6 Due (11:59PM) |
| Week 14 | Apr 7 | Apr 8 Explainability | Apr 9 | Apr 10 Immersive Technologies (AR/VR) | Apr 11 |
| Week 15 | Apr 14 | Apr 15 Trust and Visualizations | Apr 16 | Apr 17 Last Class ★ Test 2 ★ | Apr 18 |
| Week 16 Last Week | Apr 21 | Apr 22 (No Class) Work on Lab 7! |
Apr 23 | Apr 24 (No Class) | Apr 25 Lab 7 Due (11:59PM) |
Assignments
All assignments (homework + labs) are due at 11:59 pm on Fridays.
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 retroactively apply them at the end of the semester.
*** There are two primary types of assignments for this course: Homework Assignments and Programming Assignments.
Homework Assignments (HW) Details on HW assignments are on the Canvas site for this course. Due dates for each assignment can be found on Canvas and on the course Schedule. Grading distributions can be found on the course Syllabus. Submit all HW assignments on Canvas. Unless indicated by the HW instructions, all HWs are to be completed individually.
Programming Assignments (Labs) These individual assignments will teach you the basic skills for developing web-based visualizations. You are expected to complete these assignments using d3.js. It is good practice to develop your assignments using some sort of version control. GaTech gives you access to GitHub, which is a good one to use if you haven’t done so already. D3.js is the Javascript InfoVis toolkit we will use for the programming assignments. Go through the following short tutorial on the fundamentals and set up of D3.
When grading, we will use Google Chrome in Incognito Mode to run your visualizations. Further, when a server is required, we will use a Python server on localhost. When submitting on Canvas, make sure you submit a .zip containing all your files, and name it Lastname_Firstname.zip (e.g., Davis_Andy.zip), unless otherwise mentioned in the assignment.
Warning: There are many existing examples and source code widely available online. While these are great resources for you to learn, note that copying these is considered a breach of the rules from the Office of Student Integrity and will be handled accordingly. Be careful and thoughtful. Many of the assignments will ask you to start from existing source code or examples. In these cases, it is expected that parts of your assignments will resemble the original. You are expected to start with these templates and build your submission to meet the needs of the assignments from there.
The labs start relatively simple and increase in complexity throughout the semester. The due dates for the labs are listed on the Schedule and on Canvas. The labs can be accessed on the GitHub repo here: https://github.gatech.edu/CS4460/Xiong-Spring25-Labs-PUBLIC/ Notice that in order to access this repo, you must use your Georgia Tech Github account. Carefully read through the Wiki for each Lab for instructions, submission requirements, etc. Remember, when you clone the labs, please make sure that you do not publicly share your code to avoid inadvertent plagiarism.
Grading
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). 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 | 2% |
| – Lab2 | 3% |
| – Lab3 | 3% |
| – Lab4 | 5% |
| – Lab5 | 7% |
| – Lab6 | 8% |
| – Lab7 | 12% |
| Test 1 | 15% |
| Test 2 | 15% |
Attendance/Participation: In some classes, the instructor will randomly call on students to answer questions, and -1% will be applied if the student is not in class (for each class).
Expectations and Academic Integrity
Mutual expectations. At Georgia Tech, we believe that it is important to continually strive for an atmosphere of mutual respect, acknowledgment, 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. 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 website 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.