2021 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
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 3-4 Tue / 3-4 Fri
- Class
- -
- Course Code
- MCS.T412
- Number of credits
- 200
- Course offered
- 2021
- Offered quarter
- 4Q
- Syllabus updated
- Jul 10, 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 about case studies of the issues covered in the previous lecture. Programming assignment is given on each exercise day. A Python library called Bokeh is used to create interactive visualization system. Final assignment is creation of a 5 minutes visual data storytelling.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | LX1: Overview on Information Visualization | Learn about the overview of information visualization |
| Class 2 | LX2: What: Data and data abstraction | Learn about data and the concept of data abstraction |
| Class 3 | EX2: Case studies on LX2 | Case study on LX2. Group discussion. |
| Class 4 | LX3: Why: Task and task abstraction | Purpose of information visualization |
| Class 5 | EX3: Case studies on LX3 | Case study on LX3 and group discussion |
| Class 6 | LX4: Visualization of low-dimensional quantitative data | Visualization techniques for low-dimensional quantitative data |
| Class 7 | EX4: Case studies on LX4 | Case study on LX4 |
| Class 8 | LX5: Visualization of high-dimensional quantitative data | Visualization techniques for high-dimensional quantitative data |
| Class 9 | EX5: Case studies on LX5 | Case study on LX5 |
| Class 10 | LX6: Visualization of Temporal data | Visualization techniques for data that is changing over time |
| Class 11 | EX6: Case studies on LX6 | Case study on LX6 |
| Class 12 | LX7: Interaction | Interactive data analysis |
| Class 13 | LX8: Visual Analytics Systems | Various visual analytics systems |
| Class 14 | LX9: Immersive VA | VR and AR technologies in visual data analytics |
| Class 15 | 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 afterwards (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.
Tamara Munzner, "Visualization: Analysis & Design," CRC Press, 2015.
Evaluation methods and criteria
- Active participation to the discussion during the class hour (20 points)
- Quiz (twice or three times, 40 points)
- Assignments (twice or three times, 40 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.
- Fluency with GitHub and git.
Other
I plan to use Python 3.x, Bokeh, NumPy Pandas, and SciPy for exercise.