Course Details

Semester: Spring 2024

Section: 001

Meeting Time: MW 5:30pm – 6:45pm

Meeting Location: 300 Main Street, Room B201

Credit Hours: 3

Bulletin Description: Scientific visualization tools as applied to sampled and generated data; methods for

data representation and manipulation; investigation of visualization techniques Prerequisite(s): CSCE 145 or CSCE 206 or CSCE 207.

Learning Outcomes

After taking this course, you should be able to:

• Describe potential uses of visualization tools in data analysis and presentation

• Use visualization tools for data analysis

Textbook

There is no required textbook. However, here is a list of potentially useful books:

Kieran Healy. Data Visualization: A Practical Introduction. (2019) ISBN: 9780691181622

Tamara Munzner. Visualization Analysis and Design. (2014) ISBN: 9781466508910

Alexander Loth. Visual Analytics with Tableau. (2019) ISBN: 9781119560203

Claus O. Wilke. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. (2019) ISBN-10: 1492031089

All assigned readings and course materials are compliant with copyright and fairuse policies.

Course Overview

This is an in-person synchronous course. Attendance expectations are described in the Course Delivery section below.

Technology and Software

This course will extensively make use of blackboard.sc.edu. Students should check Blackboard regularly for class materials and assignments.

All software required in the course will be free for students of the class to download or use online.

Tentative List of Topics Covered

•    Data visualization tools: Tableau, R, JavaScript

•    Basics: scatterplots, line plots, bar charts, small multiples

•    Channels: colors,position, shape, tilt, size

•    Distributions: histograms, kernel density estimation

•    2D Scalar fields: choropleths, heatmaps, isocontours

•    3D Scalar fields: isocontours, direct volume rendering

•    Hierarchies and networks: force-directed layouts, metric embeddings

•    Interaction: linked views, visual querying

•    Time series

•    Scalability: sampling and preaggregation

Evaluation and Grading

Homework - 20%

Homework assignments using the visualization tools and techniques covered in class will be submitted in Blackboard.

Written Responses - 25%

On five occasions during the semester (tentatively 1/24, 2/7, 2/21, 4/1, and 4/15), the instructor will    provide articles for you to read, videos for you to watch, or visualizations for you to view. Each time at least two sources will be provided.

Undergraduate Students

Undergraduate students will select one of the sources each time and submit a two paragraph response.

Graduate Students

Graduate students will select two of the sources each time and submit a two paragraph response on each of them.

Visualization Project

The primary product of this course will be a visualization project. You will find a dataset and come up with interesting and useful visualizations of the data. The visualizations can be built with the

visualization tools of your choice. Examples and potential data sources will be provided throughout the course.

Midterm Proposal - 15%

March 13th  – You will submit a PDF with an outline of your proposed work for the final project. You need to have a description of the data you chose and what you intend to visualize. There do not have to be any preliminary visualizations at this point.

Peer Review - 5%

March 20th – You will be randomly assigned two proposals to peer review. I will provide you with the two PDF proposals, and you will provide feedback for the students.

Final Implementation - 35%

Due April 26th – The final deliverable will be the implementation of your proposed work.

Undergraduate Students

Undergraduate students will provide the visualizations and/or a link to the visualizations.

Undergraduate students will also submit a PDF describing the work completed, the challenges faced, and additional work that one could do with more time.

Graduate Students

Graduate students will provide the visualizations and/or a link to the visualizations. Graduate students will also submit a PDF describing the work completed, the challenges faced, and

additional work that one could do with more time. Graduate students will present and

demonstrate their visualizations to the class, in person, during the scheduled final exam period on Friday, April 26 at 4:00 p.m.

Late Assignment Submission Policy

Late work is strongly discouraged. You will be provided ample time to prepare and submit your

assignments prior to each due date. The penalty for late work is 10% of the assignment grade per day, beginning exactly at the due date and time, with no assignment submission accepted after three days.

Grade Computation

Homework: 20%

Written Responses: 25%

Midterm Project Proposal: 15%

Midterm Project Peer Reviews: 5%

Final Project Implementation: 35%

Grading Scale

A          90%-100%

Student Disability Resource Center

The Student Disability Resource Center (SDRC) empowers students to manage challenges and limitations

imposed by disabilities. Students with disabilities are encouraged to contact me to discuss the logistics of

any accommodations needed to fulfill course requirements (within the first week of the semester). In order to receive reasonable accommodations from me, you must be registered with the Student Disability

Resource Center (contact information is listed below). Any student with a documented disability should contact the SDRC to make arrangements for appropriate accommodations.

Office Location: 1705 College Street, Close-Hipp Suite 102, Columbia, SC 29208

Phone: 803.777.6142

Fax: 803.777.6741

Email: [email protected]

Web: https://sc.edu/about/offices and divisions/student disability resource center/index.php