Course Overview

Visualizing data is a key step in understanding many problems. This course is designed to introduce students to methods of visualizing many different types of data, such as images, numerical/statistical data, 3D surfaces, flow fields, and medical data. We will both use existing visualization software and program custom visualizations using JavaScript. Course activities include discussion of recent and classic research papers, weekly homework assignments, in-class critiques of visualization artifacts, and a final project to explore creative uses of these techniques.

Prerequisites

CSCI 1200 Data Structures and CSCI 2300 Intro to Algorithms or CSCI 2600 Principles of Software or permission of instructor. C++ and sufficient prior programming experience is required.

Learning Outcomes

Students who have successfully completed this course will be able to:

• Analyze, interpret, and evaluate a specific visualization example and discuss how the visualization might be improved for more accurate interpretation or communication of patterns in the data.
• Select or design an effective visualization strategy for a variety of different types of data.
• Create a visualization of a new dataset using available open-source visualization resources.
• Use visualization to communicate results of experiments and research in their field of study.
• Incorporate visualization for debugging and improved program development or experimental data analysis in their field of study.

Course Grades

Your grade in this course will be determined as follows:

• 10% Assigned reading summaries & Submitty forum posts & discussion
• 10% In class discussion & participation
• 5% In class Worksheets
• 40% Homework Assignments
• 10% Exams
• 25% Final Project Report/Presentation

Assigned Readings

For each lecture we will have a selection of relevant papers and articles on visualization. All students should download and read one of the assigned papers and post a detailed question or comment on the assigned reading before 8pm on the day before the paper will be discussed in class. Remember, getting your reading done on time is courteous to the presenters the next day. The post should be well-written and approximately 100-200 words in length.

It's "ok" if you don't understand all of the details (we will often be reading recent technical research results), but you should be able to put that paper in the wider context of visualization research and learn more about the technical background related to that paper (using other reference material as needed). Ideally, the paper will serve as a jumping off point that leads to other reading, possibly specialized to your area of interest.

Your post should demonstrate that you did a careful and thoughtful reading of the paper. Avoid superficial statements regarding when the work in the paper was done (e.g., it was slow because of now-outdated machines), the difficulty of the work done (e.g., the math looked difficult), and empty comments regarding the quality of the work (e.g., the visualization was pretty).

Your post can respond to another student's comment/question. Multiple posts, following up on the discussion are encouraged. Including links to other sources of related background material and a summary of how that material is related are encouraged as well.

In-Class Discussion and Participation

Participation is a very important component of the course. You are expected to regularly attend lecture, ask questions, and join in the in-class and online discussions.

Each student will lead the lecture discussion of one assigned reading during lecture. The student should give a quick (~5 minute) summary of the paper (remember, the other students have already read the paper), focusing on:

• the important contributions of the paper to the community,
• what was interesting about the paper or topic, and
• any questions or confusions that arose in the online discussion. Then we will open up the discussion to the whole class, moderated by the student discussant (~10 minutes).

Note: It is important that everyone have posted their comment/question before 8pm on the day prior to the discussion, so the student leading the presentation has time to review the posts in preparation for the in-class discussion.

In Class Worksheets

Over the term, we will have multiple in class worksheets. Many of these will be team based, with teams chosen at random via Webex breakout rooms. Worksheets will be provided via Submitty course materials, and will be submitted as a Submitty gradeable.

Homework Assignments

There will be a number (~8) of homework assignments throughout the term. Typically, these will be assigned during a Friday lecture, and will be due on the next Thursday at midnight. Not all assignments will follow this pattern.

Late Days

You will begin the term with three Submitty late days which may be used to turn in an assignment after its initial due date. Please use them as a method of aiding in your course/life balance. These late days may only be used on homework assignments; they cannot be used on the in class readings, nor can they be used on the final project/its presentation).

To use a late day, simply submit the assignment as normal on the Submitty system. You do not need to notify your TA or instructor. For example, an assignment submitted 22 hours late (Friday evening at 10:00pm) counts as 1 late day used. As another example, an assignment submitted 26 hours late (Saturday morning at 2:00am) counts as 2 late days used. No more than two late days may be used for any one assignment. Once your late days have been exhausted, late assignments will not be accepted without an excused absence note from the Student Experience office.

Exams

We will have 2 in-class exams covering the lecture material and readings.

Final Project

This course will have a comprehensive, team based final project in which you will gather data, clean it, and then craft an interesting, robust, and suitably impressive visualization of your choosing. You will present this visualization to the class, and will write up a report on the process that you followed and the visualization that you produced.