CS 329E: Elements of Data Visualization

CS 329E: Elements of Data Visualization - Spring 2026

Class Meetings:

Section 1 (53129): Monday and Wednesday 2:00pm - 3:30pm in UTC 1.146
Section 2 (53128): Tuesday and Thursday 3:30pm - 5:00pm in JGB 2.218

Instructor: Shirley Cohen

Email: scohen at cs dot utexas dot edu

Office Hours:

In Person: Monday 4:00pm - 5:00pm in GDC 6.510
Online: Tuesday 7:00pm - 8:00pm on Zoom

Teaching Assistants:

Section 1 (53129):
- Sangjun Park
- Email: sangjun at cs dot utexas dot edu
- Office Hours: Monday 11:00am – 12:00pm at GDC 1.302, TA Station Desk 5 (GDC basement)
  Wednesday 11:00am – 12:00pm at GDC 1.302, TA Station Desk 5 (GDC basement)
Section 2 (53128):
- Kate Zhang
- Email: katezhang at utexas dot edu
- Office Hours: Tuesday 11:00am - 12:00pm at GDC 1.302, TA Station Desk 4 (GDC basement)
  Friday 10:00am -11:00am at GDC 1.302, TA Station Desk TBD

Course Description

Data visualization is more than just creating charts; 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, Matplotlib, Altair, Plotly, Seaborn, etc.
Selecting and implementing visualization techniques appropriate for varied data formats, including time-series, network, image, geospatial, textual, and video data.
Using visualization as a primary tool for data-driven hypothesis generation and testing.
Constructing a compelling narrative that uses data to persuade and drive organizational decision-making.
Navigating the lifecycle of a data visualization project, from analyzing a one-page RFP to delivering a high-impact pitch.
Communicating technical findings concisely to executive audiences with limited time and attention.

Prerequisites

CS 313E or equivalent programming experience in Python.

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.)

Tech Stack
Here are some of the Python libraries we'll be using:

Pandas: For data wrangling and cleaning
Numpy: For numerical computation
Matplotlib & Seaborn: For foundational and statistical visualization
Altair & Plotly: For interactive and web-based charts
NetworkX: For network and graph data analysis
GeoPandas: For geospatial data and mapping
NLTK / spaCy: For textual data and NLP
OpenCV / Pillow: For image and video data processing
Streamlit: For building interactive dashboards

Course Projects

The coursework will center on two major group projects, designed to simulate real-world workflows:

Project I: The Data Discovery Lab (EDA)

Overview: In this first project, your group will act as data investigators. You will conduct an end-to-end exploratory analysis to uncover hidden patterns in a primary dataset and its related counterparts.
Challenge: Students will perform data wrangling, cleaning, and visualization using the Python stack (specifically pandas, matplotlib, and seaborn) to test hypotheses.
Deliverables: A technical report (Colab notebook and Data Card), detailing your methodology, findings, and the "why" behind your findings.

Project II: The Executive Pitch (Consultancy Simulation)

Overview: This project shifts from "discovery" to "persuasion." Your group will operate as a data consultancy firm, hired to solve a specific business problem.
Challenge: Based on a formal Request For Proposal (RFP), you will create high-impact visual narratives that allow an executive audience to make data-driven decisions at a glance.
Deliverables: A pitch deck, presentation, and interactive dashboard (Streamlit).

Both projects are collaborative efforts performed in groups. To ensure that your team stays on track, I will meet with each group several times throughout the semester to provide feedback and guidance, and our TA will be available for additional support.

Quizzes

There will be regular quizzes based on the readings from our text as well as the supplementary materials (documentation, tutorials, and videos). The quizzes will be done in class, and you are expected to take them entirely by yourself. If you have questions, please ask the TA or me for help, rather than consulting others.

Class Participation

All students are expected to participate actively through the completion of classroom exercises and presenting project highlights. The class participation component will be based on these activities.

Academic Integrity

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

Late Submissions, Extensions, and Make-up Quizzes

You will lose 10% of your score for each late day of your project milestone submission unless you have obtained an extension from me prior to the assignment deadline.

For deadline extension requests, alternate quiz requests, SSD accommodations, or special accommodations (for emergencies or personal issues), please make a private post on Ed and email the teaching staff directly if you don't receive a response within 24 hours. Please include your reason for requesting an extension or make-up quiz and any relevant documentation if applicable.

Students with Disabilities

Students with disabilities may request appropriate academic accommodations.

Grading

Course Project I: 30%
Course Project II: 30%
Quizzes: 20%
Course Participation: 20%

Tools

We will be using the following tools throughout the term:

Canvas 1 or Canvas 2 for project submissions and grade reporting.
Ed Discussion 1 or Ed Discussion 2 for announcements, questions, and discussions.
Zoom for office hours.
GitHub for code repository.
Google Cloud Platform for project work.
Lucidchart for diagramming, free for academic use.

Week-by-week Schedule

This schedule is tentative and is subject-to-change based on the needs of the class.

Week	Date	Topic	Project Milestones	Reading & Quizzes	Slides
1	Jan 12	Course overview and setup	P1 M0 / P2 M0	Ch 1	Week 1
2	Jan 19	Data vis elements	P1 M1 / P2 M1	Ch 2, Q1
3	Jan 26	Time-series & trends data	P1 M2 / P2 M2	Ch 3, Q2
4	Feb 2	Time-series & trends data	P1 M3 / P2 M2	Ch 4, Q3
5	Feb 9	Network & graph data	P1 M3 / P2 M3	Ch 5, Q4
6	Feb 16	Network & graph data	P1 M4 / P2 M3	Ch 6, Q5
7	Feb 23	Geospatial data	P1 M4 / P2 M4	Ch 7, Q6
8	Mar 2	Geospatial data	P1 M5 / P1 M1	Ch 8, Q7
9	Mar 9	Textual data & NLP	P2 M1 / P1 M2	Ch 9, Q8
10	Mar 16	Spring Break!	Spring Break!	Spring Break!	Spring Break!
11	Mar 23	Textual data & NLP	P2 M2 / P1 M3	Ch 10, Q9
12	Mar 30	Image data	P2 M2 / P1 M3	Ch 11, Q10
13	Apr 6	Image data	P2 M3 / P1 M4	Ch 12, Q11
14	Apr 13	Video data	P2 M3 / P1 M4	Ch 13, Q12
15	Apr 20	Video data	P2 M4 / P1 M5	Streamlit tutorial, Q13
16	Apr 27	Wrap-up	P2 M5 / P2 M5	Streamlit tutorial, Q14

Acknowledgments

The course design was inspired by discussions with Professor Mitra. Cloud computing resources are provided through the generous support of Google.