2019 Information Visualization

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Academic unit or major
Graduate major in Mathematical and Computing Science
Instructor(s)
Wakita Ken 
Course component(s)
Lecture
Day/Period(Room No.)
Mon3-4(W832)  Thr3-4(W832)  
Group
-
Course number
MCS.T412
Credits
2
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/9/20
Lecture notes updated
-
Language used
Japanese
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

Keywords

Information Visualization, Visual Analytics, Interactive Data Analysis

Competencies that will be developed

Intercultural skills Communication skills Specialist 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. Final assignment is creation of an info-graphics poster.

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

Textbook(s)

none

Reference books, course materials, etc.

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

Assessment criteria and methods

- Contribution to group discussion (40 points)
- Five programming assignments (30 points)
- Final project on Infographics (30 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.

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