CS-GY 6313: Information Visualization – Fall 2025

Instructor: Claudio Silva (csilva@nyu.edu)

Teaching Assistant (labs): Ryan Kim (rkim.dev)

Grader: TBD

Class Information

Dates: Sep 2 - Dec 11, 2025

Meeting Times: Fridays 11:00 AM - 1:30 PM

Classroom: Jacobs Hall, Room 215, Brooklyn Campus

Important Dates:

• Fall Break: October 13 (no class)
• Make-up Day: November 26 (Wednesday - Friday classes meet this day)
• Thanksgiving: November 28 (no class)

Discord: Join Course Discord

Course Prerequisites

The coursework includes extensive programming with JavaScript, the D3.js library, and web technologies (CSS, SVG, etc.). While previous knowledge of these technologies is not required, being proficient and comfortable with extensive programming is a fundamental prerequisite for this course. If you are not comfortable with programming please contact the instructor before enrolling.

Course Description

Being able to analyze and present data visually has become one of the most important skills for students who want to work in data science and related fields. Information Visualization teaches you how to design effective interactive visualizations of complex data for data understanding, discovery, and presentation.

The course is a blend of theoretical knowledge and practical work aimed at developing a well-rounded set of skills to ideate, design, implement, and evaluate sophisticated data visualizations. The theoretical part aims at providing a mental model to think about the visualization design space in a principled manner. This includes the theory of visual encoding, human perception and visualization techniques. The practical part aims at teaching the skills needed to develop effective interactive data visualizations for analysis and presentation. This includes teaching D3.js and JavaScript and practical labs on exploratory data analysis, sketching, graph design, analysis and critique.

The course also includes a series of small practical projects which enable students to gain experience with the development of fully-working interactive visualizations to solve non-trivial assigned problems. The work is organized in a way to simulate conditions happening in real-world data analysis and communication projects and includes activities to gain feedback from the instructor and the teaching assistant.

Course History

This course was originally developed by Professor Enrico Bertini while at NYU (currently at Northeastern University). It has been modified and taught by Professor Claudio Silva multiple times over the years. After a hiatus of several years, the course is being updated for Fall 2025 with substantial revisions, incorporating ideas and materials from Professor Jeff Heer’s visualization course at the University of Washington (CSE 512), along with new content including urban visualization topics and contemporary research in the field.

Course Organization

The course uses a traditional lecture + lab format. Each 2.5-hour class session is structured as:

• Lecture (90 minutes): Traditional lecture covering theory and concepts
• Lab (60 minutes): Hands-on lab work applying the lecture concepts

This structure allows for immediate application of concepts learned in the lecture, with guided practice and individual help during lab time. Students will complete exercises and work on projects during lab sessions, with instructor and TA support available.

Workload

The course is intense. We assign multiple assignments every week and we do a lot of practical work during the class. It is very important for you to do all the work on time otherwise you may get lost and catching up is hard. The good news is that you are going to learn a lot! Most of our students are extremely happy about how much they have learned by the end of the course. You are going to acquire very solid intellectual and practical skills.

Course Objectives

After this course, you should be able to:

• Design & Evaluate: Create effective visualizations by understanding design principles, visual perception, and evaluation methods
• Analyze & Critique: Deconstruct existing visualizations, identify problems, and propose improvements
• Build & Implement: Develop interactive web-based visualizations using Vega-Lite and D3.js
• Apply Domain Knowledge: Create visualizations for geographic, temporal, and network data
• Practice Ethical Design: Recognize deceptive practices and handle uncertainty appropriately
• Optimize Performance: Build scalable visualizations and interactive dashboards

Study Material and Textbooks

For this course there is no specific textbook. The following study material will be used instead:

• Lecture slides: Available through course website
• Research papers and articles: Assigned weekly readings from visualization literature
• Observable notebooks: Interactive coding exercises and examples
• Online resources: D3.js documentation, Vega-Lite tutorials, and visualization galleries

The material will be available through Brightspace and course website. Weekly readings will include both foundational papers and recent research from the visualization community.

The following texts are recommended (but not required):

• Visualization Analysis and Design, Tamara Munzner, CRC Press 2014.
• Information Visualization: Perception for Design, Colin Ware, Morgan Kaufmann 2019.
• Interactive Data Visualization for the Web, Scott Murray, O’Reilly Media 2017.
• Fullstack Data Visualization with D3, Amelia Wattenberger, Fullstack.io 2019.

Course Requirements

The course requires attendance, active participation and submission of all homework, including: exercises, quizzes and mini-projects. Students also have to complete a group project to be developed in teams of 2-3 students.

Exercises

The course contains a set of exercises assigned as homework. The exercises are meant to teach you data visualization design/evaluation skills as well as the programming skills necessary to develop the solutions you design. The exercises count for 35% of your final grade. Each exercise can be resubmitted after grading. A re-submission can give you up to 75% of the lost points back, provided it addresses all the issues raised.

Quizzes and Participation

Weekly quizzes and active class participation count for 5% of your final grade. Quizzes test knowledge from lectures and readings, while participation includes engagement during lectures and lab sessions.

Mini-Projects

There are 3 mini-projects focusing on different data types:

1. Mini-project 1: Geographic data visualization
2. Mini-project 2: Temporal data visualization
3. Mini-project 3: Network data visualization

Students develop these individually using D3.js. Each mini-project includes optional extra credit components. The mini-projects count for 35% of your final grade.

Group-Projects

The course includes a group-project to develop in a team of 2-3 students. The group project gives you the opportunity to work collaboratively with other students and to work on a topic of your own choice. The project is developed following a number of milestones that will be communicated during the course. Each student has to contribute equally to the development of the project. The total grade weight of the project is 25%.

Grading

The final grade in this course is calculated according to the following weights:

Grading breakdown:

• Quizzes & Participation: 5%
• Exercises: 35%
• Mini-Projects: 35%
• Group-Projects: 25%

Note: we do NOT have a final or a midterm exam in this course. The final grade will be computed from the grades you receive in individual assignments.

The final letter grade is calculated using the following grading schema:

Re-submission Policy

In this course it is always possible to resubmit the exercises and the mini-projects after receiving a grade and feedback from your instructor. With a re-submission you can obtain up to 75% of the points lost in your first submission. Re-submissions must be requested (by sending a message to the Grader) and must be submitted within 1 week from the date when the grade and feedback are released. Failure to resubmit on time disqualifies the student for gaining the lost points.

Late and No-submission Policy

While we are pretty flexible on re-submissions and grading, we are strict with late and missing submissions. This is for two reasons: (1) We want to prevent you from falling behind. If you fall behind, it is going to be very hard to catch up in this course; (2) If you submit after the solutions have been released and discussed in class you would have an unfair advantage.

These are the rules we will apply:

Extra-points: The mini-projects contain optional exercises that can be used to gain extra-points. Each additional exercise is worth up to 2 grade points on the final grade in the course, for a potential total gain of 6 points on the final grade. The extra-points will be weighted according to the grade received in the exercise.

Schedule Overview

For detailed lecture content, readings, lab activities, and assignments, see: Detailed Schedule

Week 1 (Sept 5) - Introduction and Evaluation

Learning Objectives: Understand what visualization is, when to use it, and how to evaluate effectiveness

Week 2 (Sept 12) - Analytical Questions and Data Transformation

Learning Objectives: Transform questions into visual queries; understand data transformation pipelines

Week 3 (Sept 19) - Fundamental Graphs and Visual Encoding

Learning Objectives: Master basic chart types and understand when to use each; apply grammar of graphics

Week 4 (Sept 26) - Deceptive Visualization and Design Ethics

Learning Objectives: Identify misleading visualizations; understand ethical design principles; recognize cognitive biases

Week 5 (Oct 3) - Visual Perception and D3 Foundations

Learning Objectives: Understand human visual perception principles; begin D3 programming

Week 6 (Oct 10) - Color and D3 Scales

Learning Objectives: Master color theory for visualization; implement D3 scales and color schemes

Fall Break (Oct 11-13) - NO CLASS

Week 7 (Oct 17) - Interaction and Animation

Learning Objectives: Design effective interactions; implement smooth animations and transitions

Week 8 (Oct 24) - Geographic and Urban Visualization I

Learning Objectives: Understand map projections and geographic data; create effective choropleth and point maps

Week 9 (Oct 31) - Temporal Data and Urban Dynamics

Learning Objectives: Design effective time series visualizations; understand temporal patterns in urban data

Week 10 (Nov 7) - Uncertainty and Data Quality

Learning Objectives: Represent uncertainty visually; assess and communicate data quality issues

Week 11 (Nov 14) - Network Data and Urban Systems

Learning Objectives: Understand network visualization techniques; apply to urban infrastructure and social systems

Week 12 (Nov 21) - Scalability and Performance

Learning Objectives: Handle large datasets; optimize visualization performance; understand progressive loading

Make-up Class (Nov 26 - Wednesday) - Geographic and Urban Visualization II

Learning Objectives: Advanced geographic techniques; intensive project development time

Week 13 (Nov 28) - NO CLASS (Thanksgiving)

Week 14 (Dec 5) - Advanced Topics and Project Presentations I

Learning Objectives: Explore emerging trends; present and critique visualization projects

Week 15 (Dec 12) - Project Presentations II and Course Wrap-up

Learning Objectives: Complete project presentations; reflect on learning; plan continued development

Assignment Summary

8 Exercises (35% total):

1. Visualization critique and basic Vega-Lite charts
2. Data questions and transformations using Vega-Lite
3. Chart design and encoding alternatives
4. Design misleading vs. honest versions of same data
5. Perception-based design decisions + D3 implementation
6. Color design with D3 scales
7. Interactive visualization with smooth transitions
8. Uncertainty visualization

3 Mini-Projects (35% total):

1. Geographic visualization - Create interactive maps and spatial visualizations
2. Temporal visualization - Design effective time series and temporal pattern visualizations
3. Network visualization - Implement network layouts and graph visualizations

1 Group Project (25% total):

• Team-based project on topic of choice
• Multiple milestones throughout semester
• Final presentation and demonstration

Quizzes & Participation (5% total):

• Weekly knowledge checks
• Active participation in lectures and labs

Collaboration, Plagiarism, Cheating Policies

The work students submit for individual assignments and class projects must be their own original work. When ideas are borrowed from existing work it is necessary to provide citations and a clear statement that describes which part has been adopted and which is original. For graded homework students are NOT allowed to collaborate with their peers. The submitted homework must be produced and submitted individually.

Academic Integrity

All students are expected to do their own work. Students may discuss assignments with each other, as well as with the course staff. Any discussion with others must be noted on a student’s submitted assignment. Excessive collaboration (i.e., beyond discussing the assignment) will be considered a violation of academic integrity. Questions regarding acceptable collaboration should be directed to the instructor prior to the collaboration.

It is a violation of academic integrity to:

• Copy or derive solutions from other students
• Copy solutions from textbooks or online sources
• Copy from previous course instances
• Copy from other courses covering similar topics
• Use AI-generated code without proper attribution and understanding

A key principle is that you must be able to explain and/or re-derive anything that you submit. Students caught in dishonest behavior will be reported to the school and may face academic consequences including course failure.

AI Policy

We live in the age of viable generative AI. Banning these tools is neither realistic nor desirable. In fact, learning to use these tools is an emerging skill. Note that AI tools do not always produce correct or accurate results. In addition, it is unwise to rely on them too much. There are situations where you won’t have access to these tools, for instance during technical interviews. Furthermore, there are skills someone with an advanced degree is expected to have on tap—without AI assistance or looking anything up.

To integrate both considerations, you can use generative AI tools to do the assignments in this class. If you use an AI to guide you in completing an assignment, you must disclose which parts were generated by the AI. Remember that you are responsible for any errors or inaccuracies in AI-generated content, and you must be able to explain all submitted work.

Moses Center Statement of Disability

If you are a student with a disability who is requesting accommodations, please contact New York University’s Moses Center for Students with Disabilities at 212-998-4980 or mosescsd@nyu.edu. You must be registered with CSD to receive accommodations. Information about the Moses Center can be found at www.nyu.edu/csd. The Moses Center is located at 726 Broadway on the 2nd floor.