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: Monday and Wednesday 2-3:15pm
Location: Howey Physics | Room L2
Instructor: Cindy Xiong
Office Hours: Monday 3:15-4:00 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)
Abhinav Vinod
Email:avinod34[at]gatech[dot]edu
Office Hours: Tuesday 10-11 and Wednesday 10-11
Office Location: TSRB 334
Grading: Lab 5, Homework 1, Test 1 + 2
Sichen Jin
Email:sjin86[at]gatech[dot]edu
Office Hours: Monday 4-5 and Tuesday 2-3
Office Location: SEE ZOOM LINK ON CANVAS
Grading: Lab 2, Homework 4, Lab 7, Test 1
Songwen Hu
Email: shu343[at]gatech[dot]edu
Office Hours: Friday 1-3
Office Location: TSRB 334
Grading: Lab 4, Homework 2, Test 1 + 2
Jennifer Chandran
Email: shu343[at]gatech[dot]edu
Office Hours: Tuesday 3-4 and Thursday 9-10
Office Location: TSRB 334
Grading: Lab 3, Homework 3, Test 1+2
Olivia Hu
Email:oliviahu[at]gatech.edu
Office Hours: Monday 10-12
Office Location: TSRB 334
Grading: Lab 1, Lab 6, Test 1+2
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 | Aug 18 Introduction | Aug 19 | Aug 20 InfoVis Overview (Pre-reading: Here) | Aug 21 | Aug 22 |
| Week 2 | Aug 25 Visual Encoding and Basic Charts | Aug 26 | Aug 27 Perception (Pre-Reading: Here) | Aug 28 | Aug 29 Lab 1 Due (11:59PM) |
| Week 3 | Sept 1 (No Class) Labor Day | Sept 2 | Sept 3 User Tasks and Sensemaking (Pre-Reading: Here) | Sept 4 | Sept 5 Lab 2 Due (11:59PM) |
| Week 4 | Sept 8 Communication and Storytelling (Pre-Reading: Here) | Sept 9 | Sept 10 Multivariate Data Presentations Coordinates, Line, Axes, Colors | Sept 11 | Sept 12 Homework 1 Due (11:59PM) Find it on CANVAS |
| Week 5 | Sept 15 (No Class) | Sept 16 | Sept 17 Unit Charts and Infographic (Pre-Reading: Here) | Sept 18 | Sept 19 Lab 3 Due (11:59PM) |
| Week 6 | Sept 22 Design Principles |
Sept 23 |
Sept 24 Uncertainty | Sept 25 | Sept 26 Homework 2 Due (11:59PM) Find it on CANVAS |
| Week 7 | Sept 29 User Interaction/Animation (Guest Lecture: Songwen Hu) | Sept 30 | Oct 1 ★ Test 1 ★ | Oct 2 | Oct 3 |
| Week 8 | Oct 6 (No Class) Fall Break | Oct 7 (No Class) Fall Break | Oct 8 Text Visualization (Pre-Reading: Here) | Oct 9 | Oct 10 Lab 4 Due (11:59PM) |
| Week 9 | Oct 13 Graphs, Networks, Hierarchies, and Trees | Oct 14 | Oct 15 (No Class) | Oct 16 | Oct 17 Homework 3 Due (11:59PM) Find it on CANVAS |
| Week 10 | Oct 20 Cognitive Bias in Data Decision-Making |
Oct 21 | Oct 22 Visual Complexity and Trust (Guest Lecture: Kylie Lin) | Oct 23 | Oct 24 Homework 4 Due (11:59PM) Find it on CANVAS |
| Week 11 | Oct 27 Visual Analytics and Accessibility Visualizations (Pre-Reading: Here) | Oct 28 | Oct 29 Explainability | Oct 30 | Oct 31 Lab 5 Due (11:59PM) |
| Week 12 | Nov 3 (No Class) | Nov 4 | Nov 5 (No Class) | Nov 6 | Nov 7 Lab 6 Due (11:59PM) |
| Week 13 | Nov 10 Evaluation I | Nov 11 | Nov 12 Evaluation II | Nov 13 | Nov 14 Lab 7 Due (11:59PM) |
| Week 14 | Nov 17 Geospatial Visualizations | Nov 18 | Nov 19 Natural Language Interfaces for Visualization | Nov 20 | Nov 21 |
| Week 15 | Nov 24 ★ Test 2 ★ | Nov 25 | Nov 26 (No Class) Student Recess | Nov 27 (No Class) Thanksgiving |
Nov 28 (No Class) Thanksgiving |
| Week 16 Last Week | Dec 1 (No Class) You are done 🙂 Congratulations! |
Dec 2 |
Dec 3 | Dec 4 | Dec 5 |
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% |
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.