ECS 273 — Visual Analytics

Visual analytics is the science of analytical reasoning supported by interactive visual interfaces. This course introduces students to the core principles and methods that integrate data visualization, statistical analysis, machine learning, and scalable computing. Emphasis is placed on how these components work together to support the exploration and interpretation of complex datasets. Students will learn how to work with a variety of data types—including tabular, temporal, spatio-temporal, graph, text, and multimedia data—using techniques that combine automated analysis with human-centered design. The course includes lectures, hands-on assignments, and a final project, all designed to build practical skills for developing and evaluating visual analytics systems used in scientific research, business applications, and other real-world domains

Course Learning Objectives

By the end of this course, you are expected to:

  1. Understand key concepts, techniques, and system architectures that form the foundation of visual analytics.
  2. Learn to design and implement interactive systems that integrate visualization, statistical reasoning, and machine learning.
  3. Gain hands-on experience building full-stack data analytics solutions that support exploratory analysis and decision-making.
  4. Apply visual analytics methods to explore, analyze, and explain complex datasets across different types from real-world applications.
  5. Critically read and discuss research papers in visual analytics and related areas, and understand how these methods are used in practical applications and deployed systems.

Lecture Information

Lecture Time: 12:10 PM - 1:30 PM, Tuesdays and Thursdays

Location: OLSON 205

Instructor: Dongyu Liu, Kemper 2123

Instructor Office Hours: by appointment (in person at Kemper 2123) 

Teaching Assistant: Madhumitha Venkatesan (mvenkat@ucdavis.edu)

TA Office Hours: Wednesdays 11AM to 12PM [Zoom Link]

GitHub Repository for Programming Assignments https://github.com/via-teaching/ecs273-26s

Textbook: There is no textbook, but you may find these three books helpful for this class:

  1. Visualization Analysis and Design, Tamara Munzner, A K Peters/CRC Press, December 2014 (UC e-Resources)
  2. Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O’Reilly Press. (UC e-Resources, code example)
  3. Data Mining : Concepts and Techniques. 3rd ed. Han, Jiawei, et al, Elsevier, 2012. (UC e-Resources)

Additional Tutorials: Check Recourses Page

Announcements and Discussion Piazza (Canvas Integrated)

We use Piazza for announcements, Q&A, discussion, and project team formation. Piazza is more structured and searchable than email or chat, and everyone is required to join. The course staff will check Piazza regularly, and all class-related questions should be asked there. We encourage active participation by posting and answering questions—this is the fastest way to get help with homework. If you would like your question to be seen only by the instructor and/or TAs, please use the private post option. Make sure your notification settings are turned on so you do not miss important updates.

TA office hours will take place through Piazza’s Live Q&A feature, where TAs will respond to questions in real time within the thread. Zoom may be used when a live conversation is necessary, but should be reserved for cases where Piazza is not sufficient.

Grading Breakdown:

    HW Assignment 1            10%

    HW Assignment 2            10%

    HW Assignment 3            15%

    HW Assignment 4            10%

    Project                               47%

    Class Participation            8%    (Workshops 2 * 1% + Project Presentations 2 * 1.5% + In-class activities 3 * 1%)

All homework and project DUE dates are at 11:55 PM PST.

SCHEDULE (Evolving!!)

DUE For marking any deadlines

PROJECT For marking any project-relevant tasks

ASSIGNED For marking any homework assignments

EX For marking example works from VIA Lab (see publication page)

 

Week 1: March 30 — Introduction

Tue 3/31 Lecture: Course Introduction
  • Your Instructor
  • Course Content and Learning Goals
  • EX Example Project: SmartAdP — Optimal Billboard Location Selection
Thu 4/2 Lecture: Visual Analytics (VA) + Data Science
  • Why Visualization? What is Visual Analytics?
  • Data Science Buzzwords
  • Data Science Lifecycle

Week 2: April 6 — Data and Computational (COMP) Approaches

Tue 4/7 Lecture: Data Collection, Storage, and Processing
  • Download/API/Scrape
  • TXT/CSV
  • SQL & No-SQL Databases
  • Data Cleaning
  • Data Integration

ASSIGNED HW1: Collecting data and conducting simple explorative analysis
Thu 4/9 Lecture: Data Analytics
  • Data Types
  • Analytics Tasks
  • EX Example Project: MTV — Multivariate Time Series Anomaly Detection

Week 3: April 13 — Visualization

Tue 4/14 Lecture: Data Visualization Fundamentals
  • Purpose of Visualization (Record, Analyze, Communicate)
  • Marks and Channels
  • Perception Accuracy

PROJECT Start to find your team members
Wed 4/15 DUE  HW1 (11:55pm, PST)

ASSIGNED HW2: Mining, Predictive Analysis   
Thu 4/16 Lecture: Effective Visualization and Interaction Techniques
  • Using Space (In)effectively
  • (De-)Obfuscating
  • (Mis)leading the Witness
  • Eight Interaction Techniques with Visualization

Week 4: April 20 — Web Development

Tue 4/21 Workshop I: Web Development and D3.js (Madhumitha Venkatesan)
Thu 4/23 Workshop II: Full-Stack Development — DB, FastAPI, and Interface (Madhumitha Venkatesan)

DUE HW2 (11:55pm, PST)

PROJECT DUE Form project teams

ASSIGNED HW3: D3 and Interactive Visualization   

Week 5: April 27 — Analytics

Tue 4/28 Lecture: Tabular Data 
  • Visualization Techniques for Tabular Data
  • Advanced Techniques for Multivariate Data
  • Dimension Reduction
Thu 4/30 Lecture: Temporal Data
  • Modeling Time
  • Visual Analysis Tasks for Temporal Data
  • Visualization Techniques for Temporal Data
  • EX Revisit MTV
TBD Lecture: Spatio-Temporal Data
  • Spatial Data and Map Visualization
  • Spatial + Temporal
  • EX Revisit SmartAdP
  • EX Example Project: TPFlow — Scalable and Human-Steerable Pattern Extraction for ST Data

Week 6: May 4 — Project Proposal Presentation

Tue 5/5 PROJECT Proposal Presentation Session I
Thu 5/7 PROJECT Proposal Presentation Session II

DUE HW3 (11:55pm, PST)

ASSIGNED HW4: Make it full-stack   
Fri 5/8 PROJECT DUE Proposal Report

Week 7: May 11 — Analytics

Tue 5/12 Lecture: Evaluation
  • What is Visualization Evaluation, and why is it needed?
  • Controlled Experiments (Formal User Study)
  • Informal User Study, Case Study, and Usability Study
Thu 5/14 Lecture: Graph Data
  • Graphs/Networks in Real Life
  • Graph Definition and Tasks
  • Graph Visualization Techniques (Node-link, Matrix, Trees)
  • Handling Large Graphs

Week 8: May 18 — Analytics

Tue 5/19 Lecture: Text Data
  • Text Data in Real Life
  • Research Questions and Elementary Tasks
  • What to Visualize?
  • Visual Analytics Systems for text data
  • EX Example Project: Awesum — Towards Dataset-scale and Feature-oriented Evaluation of Text Summarization in LLM Prompts 
Thu 5/21 Lecture: Humans and LLMs
  • Human-centered NLP
  • Human-LLM Collaboration

DUE HW4 (11:55pm, PST)
Fri 5/22 PROJECT DUE Progress Report

Week 9: May 25 — Analytics

Tue 5/26 Lecture: Image and Video Data
  • Text Data in Real Life
  • Research Questions and Elementary Tasks
  • What to Visualize?
  • Visual Analytics Systems for Image/Video data
  • Aligning Text and Images

EX Example Project: InterChat — Enhancing Generative Visual Analytics using Multimodal Interactions
Fri 5/30 Lecture: Image and Video Data II
  • Visual Analytics Systems for Image/Video data
  • Aligning Text and Images

EX Example Project: InterChat — Enhancing Generative Visual Analytics using Multimodal Interactions

Week 10: June 1 — Final

Tue 6/2 Research Talk or other plan
Thu 6/4 Research Talk or other plan
INSTRUCTION END
Fri 6/5 PROJECT DUE Final Report
PROJECT DUE Final Presentation Video
PROJECT DUE Final Project Implementation

 

About Curiosity:

"The important thing is not to stop questioning. Curiosity has its own reason for existing. One cannot help but be in awe when he contemplates the mysteries of eternity, of life, of the marvelous structure of reality. It is enough if one tries merely to comprehend a little of this mystery every day."
— Albert Einstein