Introduction to Visual Analytics (IAT355) - Fall 2024

Welcome to Visual Analytics! We are so excited that you are here. This page serves as a place for you to learn about the course structure and you will have access to the resources you need to be successful and happy!

Teaching Team

Instructor

Dr. Alireza Karduni

Email: akarduni@sfu.ca

TA

koosha Kabiri

Email: koosha_kabiri@sfu.ca

Course Policies

Contacting us

There will be a Discord channel through which we can all stay connected, ask questions and help each other.

In case you'd like to email us, please allow up to 2 business days for responses though we will typically reply much sooner. We may be able to answer questions about software or code via email. Please arrange a meeting or attend office hours for complex software or code questions. Happy to respond to other questions, but some questions are better asked in person.

To make our responses faster, please include the following in your email:

  • Your full name.
  • The course number (IAT-355).
  • A clear question

Conduct

Please treat our online interactions the same way you would in-person interactions. As a teaching team we are dedicated to providing a harassment-free experience for everyone in this class, regardless of gender, sexual orientation, disability, physical appearance, body size, race, or religion. Harassment of any form is not tolerated. Sexual language and imagery is not appropriate in this class.

If you have concerns with anyone's conduct either in-person or online, Email your instructor. If you do not feel comfortable reaching out to your instructor, please contact SIAT's advisors

SFU's complete student conduct policy is available online.

Illness

If you are feeling ill, you should stay home and get better. Let your instructor or TA know that this is the case, and make sure to catch up with course materials to stay up-to-date.

Grading and evaluation

Assignments, quizes, and final exam

The course is part lecture, and part lab. You will need access to a computer, and install some coding tools. We will work with you to figure out coding assignments, and teach you the basics in the labs.

There are several assignments that you will submit through canvas. Some assignments require coding, some are design based, and most (hopefully) are fun. You will receive information about each assignments as the course moves on.

There will be one midterm exam (quiz) that will be held in class, and will involve critically analyzing a set of visualizations (more details will be discussed in class).

There will be a final project where you will work in pairs to create a visualization, present it to the class, and write something interesting about it. (more details will be shared)

Your grades are based on your participation in all of these activities:

  • Assignment: 40%
  • Midterm Quiz: 15%
  • Final Project Submission: 30%
  • Final Presentation: 15%

Late assignments

Late penalties (10% a day for 2 days, 20% after). If you have issues and can't submit on time, please let us know in advance, we are happy to work figure out a way to get you up to speed.

Use of AI for coding

For coding assignments, it is fine if you use AI (chatgpt or others) to work through your course. But the aim is for you to learn. If you use AI, we ask that you submit your chat as part of your assignment. We will evaluate the prompt and how you dealt with AI's responses to ensure that take the most out of this class.

For presentations and writing assignments. Do not use AI for writing content. Do not copy and paste from AI directly. Feel free to use AI for ideation, and help with writing, but the end result, should be your output, not AI's.

Ackowledgements

This course and the content was heavily inspired by slides from Professor Lyn Bartram (SIAT), and Professor Emily Wall (Emory University). There were also content used from work by Steven Franconeri and Jessica Hullman from (Northwestern University).

Course Syllabus