Data Visualization

Fall 2021
T/H 11:50am-1:10pm in POS 152
Office hours: T 10:30am-11:40am in NSH 4607
Picture of Adam Perer
Adam Perer
(Instructor)
Picture of Dominik Moritz
Dominik Moritz
(Instructor)
Picture of Ángel Alexander Cabrera
Ángel Alexander Cabrera
(Teaching Assistant)

Submit assignments and discussions on Canvas.

This course is an introduction to key design principles and techniques for interactively visualizing data. The major goals of this course are to understand how visual representations can help in the analysis and understanding of complex data, how to design effective visualizations, and how to create your own interactive visualizations using modern web-based frameworks.

After you successfully complete the course you will be able to:

  • Understand the value of visualization
  • Critically evaluate visualizations and suggest improvements and refinements
  • Use best practices from design to create effective visualizations
  • Use principles of human perception and cognition in visualization design
  • Learn how to use visualization as a communication tool
  • Conduct exploratory data analysis and transform data
  • Create web-based interactive visualizations using frameworks such as Vega-Lite and D3
  • Be prepared to do research in visualization

Jump to

  • TOC

Schedule and Readings

Tue, Aug 31

Introduction and the Value of Visualization Slides

Thu, Sep 02

Sketching Slides

Tue, Sep 07

Data and Image Models Slides

Thu, Sep 09

Effective Visual Encodings Slides

Tue, Sep 14

Exploratory Data Analysis Slides

Thu, Sep 16

Transform Data with SQL Slides

Tue, Sep 21

Interactivity Slides

Thu, Sep 23

Visualization Tools Slides

Tue, Sep 28
Thu, Sep 30

Final Project Introduction Slides

Tue, Oct 05

Narrative Visualization Slides

Thu, Oct 07

Designing for Perception Slides

Tue, Oct 12

Color + Interactive Visualization Fast Forwards Slides

Tue, Oct 19

Ethical Data Visualization I Slides

Thu, Oct 21

Ethical Data Visualization II Slides

Tue, Oct 26

Virtual VIS Week I Slides

Thu, Oct 28

Virtual VIS Week II Slides

Tue, Nov 02

Geographic Visualization Slides

Thu, Nov 04

Network Visualization Slides

Tue, Nov 09

Scalable Visualization Slides

Thu, Nov 11

Accessibility Slides

Tue, Nov 16

Animation Slides

Thu, Nov 18

Uncertainty Slides

Tue, Nov 23

Visualization and Machine Learning Slides

Tue, Nov 30

Final Project Presentations Slides

Thu, Dec 02

Final Project Presentations Slides

Details

Prerequisites

Expected skills you should have before entering the course:

  • Programming
  • Git (collaborative software development)

Skills that can be learned during the course:

  • JavaScript
  • HTML, CSS, DOM
  • Vega, Vega-Lite, D3
  • SQL

Projects

The course is project oriented. It includes a large final group-defined project along with 2 homework assignments designed to provide the stepping stones needed to complete the final project. Tentative due dates for these projects can be found at the bottom of this syllabus under the ‘Course Summary’ heading. Your work will be evaluated relative to your background and level of effort. This is a graduate class, and the assumption is that you are a mature and motivated student, and that you will define your work so that you learn and grow, given your background. Students who are taking this course as a part of a technical requirement (such as the computer science course requirement in the HCI PhD) will need to do more advanced or ambitious projects, and should consult with the instructor to make sure they are meeting this bar.

All homework assignments are to be done as individual work. It is expected that students may assist each other with conceptual issues, but not provide code. If you use example code, you must explicitly acknowledge this in your assignment submission. If you are unsure about these boundaries, ask.

Work Required

This will not be an exam-heavy course. Instead, much of the work will focus on projects. The course will focus on understanding the techniques of data science and visualization through developing creative analyses and visualizations using tools to solve defined problems.

There is no final exam in this course. Students who do well will be invited to continue on an independent project on topics related to the course, working with Prof. Perer or Prof. Moritz during a future semester.

Readings

You will be expected to read assigned readings before the lecture they pertain to. These may include chapters drawn from textbooks about data, or readings about the research literature. To incentivize this, each student will be required to make at least one relevant postings to the discussion group before the class on which each reading is due. This participation will count toward the Participation and Attendance portion of their grade.

All students are required to submit at least 1 substantive discussion post per lecture related to the course readings. Each student has 1 pass for skipping comments.

Good comments typically exhibit one or more of the following:

  • Critiques of arguments made in the papers
  • Analysis of implications or future directions for work discussed in lecture or readings
  • Clarification of some point or detail presented in the class
  • Insightful questions about the readings or answers to other people’s questions
  • Links to web resources or examples that pertain to a lecture or reading

Grades

The tentative breakdown for grading is below. As a reminder, here is the university policy on academic integrity.

  • 30% Homework Assignments
  • 60% Final Project
  • 10% Participation and Attendance

Resources

The MDN web docs are the go-to guide for web technolgies such as HTML, CSS, and JavaScript.

For D3 questions, check out D3’s documentation.

For Vega-Lite questions, check out the Vega-Lite documentation and the examples in the online editor.

Policies

Respect for Diversity

It is our intent that students from all diverse backgrounds and perspectives be well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength and benefit. It is our intent to present materials and activities that are respectful of diversity: gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups. In addition, if any of our class meetings conflict with your religious events, please let us know so that we can make arrangements for you.

Accommodations for Students with Disabilities

If you have a disability and are registered with the Office of Disability Resources, we encourage you to use their online system to notify us of your accommodations and discuss your needs with us as early in the semester as possible. We will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to contact them at access@andrew.cmu.edu.

Health and Well-being

This year is unlike any other. We are all under a lot of stress and uncertainty at this time. Make sure to move regularly, eat well, and reach out to your support system or us if you need to. We can all benefit from support in times of stress, and this semester is no exception.

Due to the ongoing pandemic, do not under any circumstances physically come to class sick to keep your fellow students and instructors safe. Reach out to the instructors to determine a way to participate remotely and/or catch-up.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help; call 412-268-2922 and visit their website at www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help. If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:

If the situation is life threatening, call the police. On campus call CMU Police: 412-268-2323. Off campus: 911.

Food Insecurity

If you are worried about affording food or feeling insecure about food, there are resources on campus who can help. Email the CMU Food Pantry Coordinator to schedule an appointment: Pantry Coordinator: cmu-pantry@andrew.cmu.edu SLICE office: 412-268-8704

If you have questions about this or your coursework, please let the instructors know. Thank you, and have a great semester.

Acknowledgements

Material for this class has been adapted from classes taught by Jeffrey Heer at the University of Washington, and Arvind Satyanarayan at MIT, which in turn build on materials from many others.

The class draws heavily on materials and examples found online, and we try our best to give credit by linking to the original source. Please contact us if you find materials where credit is missing or that you would rather have removed.