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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
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- School of Science
- School of Engineering
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School of Environment and Society
- Department of Architecture and Building Engineering
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Mathematical and Computing Science
- Instructor(s)
- Wakita Ken
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue3-4(W932) Fri3-4(W932)
- Group
- -
- Course number
- MCS.T412
- Credits
- 2
- Academic year
- 2022
- Offered quarter
- 4Q
- Syllabus updated
- 2022/4/20
- Lecture notes updated
- -
- Language used
- English
- 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
| ✔ 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.
Textbook(s)
none
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.
Other
I plan to use Python 3, Jupyter Notebook, Plotly, Pandas, and scikit-learn for exercise.
