CS 329E: Elements of Data Visualization

CS 329E: Elements of Data Visualization - Fall 2026

Class Meetings

This course is offered in two sections. Both sections cover the same material, follow the same week-by-week schedule, and share this course page.

Monday / Wednesday Section 3:30pm - 5:00pm
GDC 1.304

Tuesday / Thursday Section 2:00pm - 3:30pm
SZB 2.802

Professor

Shirley Cohen
scohen at cs dot utexas dot edu

Teaching Assistants

Indira Dutta · Monday / Wednesday section
idutta at cs dot utexas dot edu

Talia Chen · Tuesday / Thursday section
taliac at utexas dot edu

Office Hours

Who	When	Where
Shirley Cohen (Instructor)	Monday, 5:00pm - 6:00pm	GDC 6.510
Shirley Cohen (Instructor)	Tuesday, 7:00pm - 8:00pm	Zoom
Indira Dutta (MW TA)	Wednesday, 12:00pm - 1:00pm	GDC 3.416
Indira Dutta (MW TA)	Friday, 12:00pm - 1:00pm	GDC 3.416
Talia Chen (TTh TA)	Thursday, 4:00pm - 5:00pm	GDC 6.816
Talia Chen (TTh TA)	Friday, 10:00am - 11:00am	Zoom

Course Description

Data visualization is more than just creating graphs; it is a critical tool for exploration, discovery, and persuasion. This course provides a hands-on introduction to the principles and techniques of data visualization using the Python ecosystem. Students will learn to transform raw data into actionable insights, moving from exploratory data analysis (EDA) to high-stakes storytelling.

Topics to include:

Exploratory data analysis (EDA) using Pandas, Altair, and Streamlit.
Selecting and implementing visualization techniques appropriate for varied data formats, including time-series, network, and geospatial.
Using visualization as a primary tool for data-driven hypothesis generation and testing.
Navigating the lifecycle of a data visualization project, from data collection to interactive dashboards and visualization agents.
Communicating technical findings concisely and convincingly to executive audiences.

Learning Outcomes

By the end of this course, students will be able to:

Clean, transform, and analyze raw datasets efficiently using libraries like Pandas and Polars to prepare them for robust exploratory data analysis.
Select and construct appropriate static and interactive charts (utilizing Matplotlib, Altair, Plotly, and Folium) specifically suited for complex data formats, including time-series, network, and geospatial data.
Leverage exploratory data analysis (EDA) as a primary mechanism to formulate, test, and objectively validate data-driven hypotheses.
Build and securely deploy full-stack data applications using Streamlit, Google Cloud Run, and Identity-Aware Proxy (IAP).
Augment traditional human-consumed dashboards by integrating autonomous agentic workflows using modern AI development kits.
Translate exploratory analysis into a compelling business case presentation that adapts technical findings to a business context and drives a clear recommendation for an executive audience.

Prerequisites

CS 313E or equivalent software design course.

Required Textbook

Better Data Visualizations by Jonathan Schwabish (Columbia University Press, 2021).

Recommended Textbooks

Python for Data Analysis by Wes McKinney (O'Reilly, 2022).
Storytelling with Data by Cole Nussbaumer Knaflic (Wiley, 2015).

Supplementary Materials

Online documentation, tutorials, and videos (Coursera, LinkedIn Learning, etc.)

Course Communication Tools
We will be using the following tools throughout the term:

Canvas for project submissions and grade reporting. Each section has its own Canvas site: Monday / Wednesday · Tuesday / Thursday.
Ed Discussion for announcements, questions, and discussions. Each section has its own Ed site: Monday / Wednesday · Tuesday / Thursday.
Zoom for online office hours. Join links are listed in the Office Hours table above.

Tech Stack
Our primary Python tech stack consists of the following libraries:

Pandas: For data wrangling and cleaning.
Numpy: For numerical computation.
Matplotlib & Seaborn: For foundational and statistical visualization.
Altair, Plotly, and Folium: For interactive and web-based charts.
Streamlit: For building interactive applications.
Polars: For high-performance data processing.

Our primary development and deployment infrastructure consists of the following tools:

GitHub for version control and code repository.
Colab and VS Code: For development and testing.
Cloud Run: For deploying Streamlit.
Identity Aware Proxy (IAP): For securing Streamlit.

Term Project
The coursework will center around one major Exploratory Data Analysis project, which students will adapt into a business case presentation at the end, moving from analysis for themselves to communication for a decision-making audience. More specifically:

You will work in groups of two, formed during the first week of class.
Your group will act as data investigators. You will conduct an end-to-end analysis to uncover hidden patterns in a primary dataset and its related counterparts.
You will perform data wrangling, cleaning, and visualization to test hypotheses.
You will produce Colab notebooks, Data cards, Streamlit app, AI agent, and a business case presentation to the class.

The project is divided into 7 milestones (M0–M6), each producing a concrete deliverable:

Milestone 0: Project proposal
Milestone 1: Loaded and cleaned dataset
Milestone 2: Static visualization portfolio
Milestone 3: Interactive Streamlit dashboard
Milestone 4: AI-augmented dashboard
Milestone 5: Robust agents and responsive app
Milestone 6: Business case presentation

Milestones are due at 11:59pm on the Sunday following the week in which they appear on the schedule. This deadline is the same for both sections.

Quizzes

There will be quizzes on most weeks, based on the assigned readings. The quizzes will be done in class, and students are expected to take them by themselves.

Exam

A comprehensive exam will be held at the end of the term. It will consist of three components: questions on the assigned readings, questions covering lecture concepts, and an oral assessment related to your term project. The written component will be given during the last week of class. Oral assessments will be scheduled by sign-up during the last week of class and the exam period.

Code Reviews

Students will participate in regular, live code reviews during class. During these sessions, you should be prepared to answer impromptu questions and clearly explain the logic, design, and progress of your project milestones.

Class Participation

Students are expected to actively participate in discussions and complete hands-on exercises, which will usually take the form of interactive code labs.

Grading Breakdown

Term Project: 40%
Exam: 20%
Quizzes: 20%
Code Reviews: 10%
Participation: 10%

The final mapping to letter grades will be determined at the end of the term once all coursework has been graded, and will be based on the overall distribution of scores. Grades are curved across both sections together, using the combined distribution.

Academic Integrity

This course will abide by UTCS' code of academic integrity.

Generative AI Policy

Your primary goal as a student is to master the course content and develop as a capable engineer. This means engaging deeply with the material, working through assignments, and building your projects with genuine understanding. Generative AI tools can support this learning process, but over-reliance on them often backfires, leaving you with a shallow grasp of the concepts and skills that the course is designed to build.

You may use generative AI to:

Brainstorm visualization strategies for complex datasets.
Look up syntax for Pandas, Polars, or specific charting libraries.
Troubleshoot and debug errors in your Streamlit applications.
Generate boilerplate code to set up your project structure.

You may not use generative AI to:

Engage in blind generation where you generate your application without understanding what the code does. You must be able to explain every line of code you submit. During code reviews, oral assessments, or class discussions, "the AI wrote it" is not an acceptable answer.
Generate an entire project from a prompt and submit it as your own work without verifying the underlying data logic, analytical accuracy, and design integrity. Doing so is a direct violation of academic integrity.

Late Submissions, Extensions, and Make-up Quizzes

You will receive a total of 5 slip days for the semester, which can be used for project milestones, quizzes, and participation assignments.

Each slip day used extends a project milestone deadline by 24 hours with no penalty, up to a maximum of 3 days late.
Because milestones are group deliverables, a late milestone submission will deduct slip days from both group members. For example, a milestone submitted one day late costs each group member one slip day.
For quizzes, using a slip day allows you to make up a missed quiz during the instructor's or TA's office hours, either the same week or the following week. One slip day covers one make-up quiz, regardless of when it is taken.
For participation assignments, one slip day extends an in-class exercise deadline by 24 hours after the class session.
Slip days are applied automatically. You do not need to notify the teaching staff to use one; they are calculated based on your submission timestamp in Canvas.

Once you have used all 5 slip days, you will need a doctor's note (or equivalent documentation) to make up a missed quiz or receive an extension on a project milestone or participation assignment. Without proper documentation:

Late project milestones will incur a 10% grade penalty per day, up to a maximum of 3 days late, after which the milestone receives a zero.
Missed quizzes and participation assignments will receive a zero.

Students with Disabilities

If you are a student with a disability, or think you may have a disability, and need accommodations, please contact Disability and Access (D&A). Contact and more details are available on D&A's website.
If you are already registered with D&A, please share your accommodation letter with me as early as possible in the semester so we can discuss how your approved accommodations will be implemented in this course.

Week-by-week Schedule

This schedule is tentative and is subject-to-change based on the needs of the class. Both sections follow the same week-by-week plan; find your section's meeting date in the corresponding column.

Week	Mon / Wed	Tue / Thu	Topic	Milestone	Reading	Direct Links
1	Aug 24	Aug 25	Course overview	M0	Ch 1 & 2
1	Aug 26	Aug 27	Group work	M0	Ch 1 & 2	Milestone 0
2	Aug 31	Sep 1	Pandas and Altair tutorials (Colab)	M0	Ch 1 & 2
2	Sep 2	Sep 3	Group work and quiz	M0	Ch 1 & 2, Q1	Milestone 0
3	Sep 7 · Labor Day	Sep 8	First data exploration (Colab)	M1	Ch 3 & 4
3	Sep 9	Sep 10	Data load and preparation (Colab)	M1	Ch 3 & 4	Milestone 1
4	Sep 14	Sep 15	Data cleaning and transformation (Colab)	M1	Ch 3 & 4
4	Sep 16	Sep 17	Group work and quiz	M1	Ch 3 & 4, Q2	Milestone 1
5	Sep 21	Sep 22	Static visualizations (Colab)	M2	Ch 5
5	Sep 23	Sep 24	Group work and quiz	M2	Ch 5, Q3	Milestone 2
6	Sep 28	Sep 29	Interactive visualizations (Colab)	M2	Ch 6
6	Sep 30	Oct 1	Group work and quiz	M2	Ch 6, Q4	Milestone 2
7	Oct 5	Oct 6	Interactive web application (Streamlit)	M3	Ch 7
7	Oct 7	Oct 8	Group work and quiz	M3	Ch 7, Q5	Milestone 3
8	Oct 12	Oct 13	Deployment to cloud	M3	Ch 8
8	Oct 14	Oct 15	Group work and quiz	M3	Ch 8, Q6	Milestone 3
9	Oct 19	Oct 20	Visualization agent (Claude Agent SDK)	M4	Ch 9
9	Oct 21	Oct 22	Group work and quiz	M4	Ch 9, Q7	Milestone 4
10	Oct 26	Oct 27	Evaluating and improving agent	M4	Ch 10
10	Oct 28	Oct 29	Group work and quiz	M4	Ch 10, Q8	Milestone 4
11	Nov 2	Nov 3	Polish and performance	M5	Ch 11
11	Nov 4	Nov 5	Group work and quiz	M5	Ch 11, Q9	Milestone 5
12	Nov 9	Nov 10	Business case brief	M5	Ch 11
12	Nov 11	Nov 12	Group work and quiz	M6	Ch 11, Q10	Milestone 6
13	Nov 16	Nov 17	Business case presentations	M6
13	Nov 18	Nov 19	Business case presentations	M6		Milestone 6
14	Nov 23 – 28 · Fall Break / Thanksgiving Holiday
15	Nov 30	Dec 1	Exam prep
15	Dec 2	Dec 3	Exam: written
16	Dec 7	—	Exam: oral

Acknowledgments

The design of this course draws from conversations with Professor Mitra as well as feedback from former TAs and students who were part of its first and second editions. Cloud computing resources are provided through the generous support of Google.