2022 Data Science for Transdisciplinary Research (I)

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Undergraduate major in Transdisciplinary Science and Engineering
Zhu Xinru  Taoka Yuki 
Class Format
Lecture / Exercise    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Thr7-8(南4号館 3階 情報ネットワーク演習室 第1演習室)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

There is a growing need for data science to extract and utilize the necessary information from a huge amount of data. In this lecture, we will learn theories and methods of exploratory data visualization for discovering valuable information from data and explanatory data visualization for effectively communicating the obtained knowledge to various recipients. Python is mainly used in the lectures, but the goal is to gain knowledge and skills that are not limited to specific tools.

Student learning outcomes

Students will acquire the following skills:
1) Skills for understanding the fundamental theories and methods of data visualization.
2) Skills for independently executing the process from data acquisition to data visualization.
3) Skills for creating data visualization that takes into account the purpose of communication and the attributes of the recipients of the information.


Data analysis, Data visualization, Visual communication

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

The course will be conducted mainly in the form of lectures and exercises. There will be a session for groups (or individuals) to create and present data visualization work.

Course schedule/Required learning

  Course schedule Required learning
Class 1 (Lecture/Exercise) Introduction Understand the purpose, history, and current status of data visualization, and then perform basic data visualization using Python.
Class 2 (Lecture/Exercise) Process of Data Visualization Understand the data visualization process (question formulation, data acquisition, hypothesis formulation, exploratory visualization, data analysis, and explanatory visualization), and perform data acquisition and hypothesis formulation.
Class 3 (Lecture/Exercise) Visual Marks and Visual Variables Understand the basic theory of data visualization and methods corresponding to different types of data, and perform explanatory visualization.
Class 4 (Lecture/Exercise) Visualization of Quantitative Data Learn visualization methods for quantitative data in detail along with several case studies and visualization (sample data: time-series data of world population, voting data of the UN General Assembly, etc.).
Class 5 (Lecture/Exercise) Visualization of Qualitative Data Learn visualization methods for qualitative data along with several case studies (sample data: data from the preamble of several countries' constitutions, data from international trade networks, etc.).
Class 6 (Exercise) Group Work and Q&A Session Perform data visualization on a topic of interest to the group (or individual) and create a piece of work for the final presentation.
Class 7 (Exercise) Presentation and Discussion The group (or individual) will present their data visualization work, and the class will discuss their communication effectiveness.

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 afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.



Reference books, course materials, etc.

1. Imai, Kosuke. Quantitative Social Science: An Introduction. Princeton University Press, 2018.
2. Kirk, Andy. Data Visualisation. SAGE Publications, 2019.
3. Wilke, Claus O. Fundamentals of Data Visualization. O’Reilly Media, 2019.

Assessment criteria and methods

Final presentation 60%, Exercises 40%

Related courses

  • TSE.C201 : Introduction to Transdisciplinary Science and Engineering
  • TSE.C202 : System Design Project
  • TSE.C203 : Transdisciplinary Design Project
  • TSE.M204 : Statistics and Data Analysis
  • TSE.A326 : Data Science for Transdisciplinary Research (Ⅱ)

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

No prior knowledge of programming or data analysis is needed.
If it is difficult to participate in the computer lab, each student should secure a computer to use. Students who have difficulty in securing a computer should consult with the lecturers in advance.

Page Top