CS 181 Data Visualization • Fall 2025

Welcome to the syllabus for CS 181 Data Visualization with Professor Sophie Engle for Fall 2025.

This syllabus provides specifics about this course, as well its expectations and policies. Since a syllabus is similar to a contract, it can be quite long.

A few key points from this syllabus are highlighted here:

This class will use Observable and JavaScript to create interactive web-based data visualizations, but students do not need to already be familiar with either before starting.
This class uses alternative grading, with the focus on which learning outcomes students have passed or exceeded.
Students must earn a 70% or higher Participation average to pass the class, which requires attendance at most (but not all) classes.
Late submissions are accepted for most assignments up to 72 hours late, but may impact whether students can complete required Feedback assignments on-time.
There will be mandatory in-class presentations on weeks 13 (Tue 11/18 and Thu 11/20) and week 15 (Tue 12/09 and Thu 12/11).
Collaboration and AI use is allowed up to a point, but it is discouraged and attribution is required.

Students are still required to read through the entire syllabus. It is broken up into the following sections:

The entire syllabus can also be found below.

Information

Find basic details on the course, lectures, prerequisites, instructor, and more below.

Details

CSCI 181AP HM Data Visualization 3 Credits • Fall 2025 https://hmc-datavis-fall2025.notion.site/

This course introduces the fundamentals of data visualization. Students will learn principles of perception, design, and evaluation, as well as a variety of visualization techniques for high-dimensional, temporal, hierarchical, network, and/or geospatial data. Students will create, revise, and evaluate visualizations using a variety of programming languages and tools.

Lectures

Tuesday, Thursday • 1:15pm to 2:30pm *Shanahan Center • Room 2461*

Lectures will usually be recorded by the instructor via Zoom and recordings will be made available to enrolled students on the Canvas website.

These recordings are not intended to replace in-person attendance or support long-term remote participation.

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Prerequisites

Students must pass one of the following before taking this course:

CS 70 HM Data Structures and Program Development
CS 62 PO Data Structures and Advanced Programming

Students should already know the fundamentals of programming and data structures, but do not need to know JavaScript prior to taking this course.

Materials

Students must have access to a computer capable of installing and running software or tools such as the latest versions of Visual Studio Code, Tableau, and a modern web browser capable of running the latest versions of JavaScript.

There are no required textbooks for this course.

This course will utilize the following websites in addition to this one:

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Instructor

The instructor and grutors for this course are below:

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The instructor primarily uses she/her pronouns, but welcomes and encourages gender-neutral they/them pronouns as well.

Students are encouraged to refer to the instructor by their first name, Sophie (pronounced soh-fee), in person or by email. Students that prefer to be formal may also use the Professor or Dr. titles with the instructor's first or last name. For example: Sophie, Professor Sophie, or Dr. Engle.

Visit the Getting Help section for drop-in hours and contact information.

Learning Objectives

At the end of this course, students should be able to meet the following :

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Visit the Assessment section for details on how these objectives will be assessed.

Workload

The Academic Policies section of the **HMC Catalog** states there must be a minimum of 3 hours of work per week per 1 credit in a full semester course. For this 3 credit course, that works out to approximately 3 hours of in-class lecture and between 6 to 9 hours of out-of-class work each week.

Tentative Schedule

Below is the tentative schedule for this class. Visit the for the latest.

Week 01: Introduction and Terminology
Week 02: Perception and Illusions
Week 03: Basic Charts
Week 04: Design and Evaluation
Week 05: Temporal Data
Week 06: High-Dimensional Data
Week 07: D3.js
Week 08: D3.js
Week 09: Interactivity
Week 10: Geospatial Data
Week 11: Hierarchical Data
Week 12: Network Data
Week 13: Project Beta Demos ⭐
Week 14: Uncertainty Data
Week 15: Project Presentations ⭐

Important Dates

There will be mandatory in-class presentations on weeks 13 (Tue 11/18 and Thu 11/20) and week 15 (Tue 12/09 and Thu 12/11).

The final exam slot for this class is Tuesday, December 9th from 2:00 pm to 5:00 pm. As such, the final course deliverables will be due at 5:00pm on Tuesday, December 9th. Visit the official Academic Calendar for other important dates.