2022 Information Visualization

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Academic unit or major
Graduate major in Mathematical and Computing Science
Wakita Ken 
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Tue3-4(W932)  Fri3-4(W932)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

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.

Student learning outcomes

(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


Information Visualization, Visual Analytics, Interactive Data Analysis

Competencies that will be developed

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/Required learning

  Course schedule Required learning
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

Out-of-Class Study Time (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.



Reference books, course materials, etc.

The following are recommended readings:

1. Natalia Andrienko and others, "Visual Analytics for Data Scientists," Springer, 2020.

2. Tamara Munzner, "Visualization: Analysis & Design," CRC Press, 2015.

Assessment criteria and methods

- 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 (i.e., required knowledge, skills, courses, etc.)

- 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.


I plan to use Python 3, Jupyter Notebook, Plotly, Pandas, and scikit-learn for exercise.

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