2025 (Current Year) Faculty Courses School of Computing Department of Mathematical and Computing Science Graduate major in Mathematical and Computing Science
Information Visualization
- Academic unit or major
- Graduate major in Mathematical and Computing Science
- Instructor(s)
- Ken Wakita
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (W9-322(W931)) / 5-6 Fri (W9-322(W931))
- Class
- -
- Course Code
- MCS.T412
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- May 21, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course outlines the basics and the recent trends in information visualization and visual analytics (visual data analysis). It also gives the participants required practical skills.
Course description and aims
(1) Understand the concepts of information visualization and visual analytics
(2) Acquire the data processing technique required for information visualization and visual analytics
(3) Understand various visualization methods employed in information visualization and visual analytics
Keywords
Information Visualization, Visual Analytics, Interactive Data Analysis
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The course is structured by a lecture (LX#) followed by an exercise (EX#). During each exercise day, small groups of participants discuss case studies of the issues covered in the previous lecture. Programming assignment is given on each exercise day. A Python library called Plotly is used to create an interactive visualization system. The subject of the exercise will be an outbreak of an infectious disease in a hypothetical million city.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | LX01: Overview - What is VIS? | Definition of VIS by Tamara Munzner |
| Class 2 | LX02: What: Data and Data Abstraction | Learn about data and the concept of data abstraction |
| Class 3 | EX01: Exercise on LX2 | Exercise on LX2. Group discussion. |
| Class 4 | LX03: Why: Task and Task Abstraction | Purpose of information visualization |
| Class 5 | LX04: Arrange Table | Visualization techniques for low-dimensional quantitative data |
| Class 6 | LX05: Arrange Tables (continued) | Visualization techniques for small-scale tabular data |
| Class 7 | LX06: Visualization of Networks and Trees | Visualization techniques for relationship (keywords: node-link diagram, inclusion). |
| Class 8 | LX07: Visualization of Temporal Data | Visualization techniques for data that change over time |
| Class 9 | EX02: Analysis of Vastopolis dataset | Visualization techniques for data that change over time (Pandas, scikit-learn, Plotly) |
| Class 10 | LX08: Visualization of Large Datasets | Visualization techniques for large-scale data that has many attributes and many data items (Keywords: filtering, data abstraction, dimension reduction) |
| Class 11 | LX09: Facet into Multiple Views | Handling of visualization screens consisting of multiple views |
| Class 12 | EX03: Vastopolis Dataset Revisited | Analyze infection dynamics and propose countermeasures |
| Class 13 | LX10: Virtual Reality, Augmented Reality, and Visualization | Application of VR and AR to information visualization |
| Class 14 | Wrap up | Wrap up |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterward (including assignments) for each class. They should do so by referring to textbooks and other course material.
Textbook(s)
none
Reference books, course materials, etc.
The following are recommended readings:
1. Tamara Munzner, "Visualization: Analysis & Design," CRC Press, 2015.
2. Natalia Andrienko and others, "Visual Analytics for Data Scientists," Springer, 2020.
Evaluation methods and criteria
- Exercise (5 times, 50 points)
- Report (Once, 50 points)
Related courses
- MCS.T213 ： Introduction to Algorithms and Data Structures
- MCS.T332 ： Data Analysis
- MCS.T204 ： Introduction to Computer Science
- CSC.T271 ： Data Structures and Algorithms
- CSC.T253 ： Advanced Procedural Programming
- CSC.T421 ： Human Computer Interaction
- CSC.T272 ： Artificial Intelligence
- CSC.T343 ： Databases
Prerequisites
- Communication ability: active contribution to group discussions.
- Programming ability equivalent to the senior undergraduate students of the school of computing.
- Google Account (needed for Google Colaboratory)
Other
I plan to use Python 3, Google Colaboratory, Pandas, Plotly, NetworkX, scikit-learn for exercise.