About This Course
Instructor
- Michael Friendly datavis.ca home page
- Email: friendly AT yorku DOT ca
- Twitter: [@datavisFriendly](http://twitter.com/datavisFriendly); #psy6135
- Office:
226 BSB - Phone:
x66249 - Office hour: Wednesday: 11:30-12:30, but by appointment
- Course web site: http://euclid.psych.yorku.ca/www/psy6135/
- TAs:
- Nataly Beribisky
- Justeena Zaki-Azat
- Nataly Beribisky
- Photo mosaic of me (composed of images from the history of data vis); click on the image for a larger version
Class meetings
- Tuesday / Thursday, 2:30 pm - 5:30 pm, via zoom. Link and instructions to be announced.
Class discussion forum
This term we will be using eClass for class discussion. The system is designed to make it easier to communicate with your classmates, the TA, and myself. Rather than emailing questions to a listserv or to us individually, I encourage you to post your questions on eClass.
Students in the course are automatically subscribed. The link is: https://eclass.yorku.ca/eclass/user/index.php?id=37950
Summary
This course is designed as a lecture/seminar dealing with data visualization from a largely psychological and historical perspective.
Course Description
Information visualization is the pictorial representation of data.
- Successful visualizations capitalize on our capacity to recognize and understand patterns presented in information displays.
- Conversely, they require that writers of scientific papers, software designers and other providers of visual displays understand what works and what does not work to convey their message.
This course will examine a variety of issues related to data visualization from a largely psychological perspective, but will also touch upon other related communities of research and practice related to this topic:
- history of data visualization,
- computer science and statistical software,
- visual design,
- human factors.
We will consider visualization methods for a wide range of types of data from the points of view of both the viewer and designer/producer of graphic displays.
Evaluation
Grades will be based on the following components:
Discussion leader (20%) Each session, 1-2 of you will lead a brief discussion on one of the readings, sub-topics or an application related to the topic. (~ 5 min.).
- It may be helpful to prepare a one-page handout or other visual materials.
- You can send post these on eClass
Class presentation (40%) In the last week, each person will give a ~ 20 min presentation on a topic of research, application, or software related to data visualization. The topic is not restricted to things covered in the lectures. You’ll find a few random suggested topics on the resources page You are permitted to do this work with one other person.
- You should prepare a brief topic proposal and post it to eClass or discuss it with one of us by email by Jun 1. Ungraded, but we want to help you with your topic.
- Your presentation should be accompanied by a slide show or other visual materials. We encourage students to prepare and record an audio/video of your presentation in advance and upload to eClass or share a link. We would prefer to do this offline, in advance — meaning you can record an audio/video of your presentation in advance and upload to eClass or share a link. In class discussion can then be abbreviated. Those who do post their presentation in advance will be given extra credit.
- Evaluation of the presentation will be done by peer review as well as by the instructors. A rating form will provided on eClass.
Research proposal (40%) Prepare a brief research proposal (~ 5-6 pages) on a data visualization topic. This can an empirical study of some aspect of data visualization, an historical review, or a proposal for development or implementation of a graphical method. You are permitted to do this work with one other person. This will be due on Friday, June 25, the week following the last class.
Copyright © 2018 Michael Friendly. All rights reserved.
friendly AT yorku DOT ca
orcid.org/0000-0002-3237-0941