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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
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Undergraduate major in Transdisciplinary Science and Engineering
- Instructor(s)
- Ishizuka Chikako Sugishita Kashin
- Class Format
- Lecture / Exercise (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Tue1-2(南4号館 3階 情報ネットワーク演習室 第1演習室)
- Group
- -
- Course number
- TSE.A326
- Credits
- 1
- Academic year
- 2022
- Offered quarter
- 4Q
- Syllabus updated
- 2022/5/11
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
There is a growing need for data science to extract and utilize the necessary information from huge amounts of data. It is essential to understand the characteristics and limitations of data, as well as to acquire skills for its specific visualization and analysis. In the first half of this class, we will learn how to express and understand the "uncertainty" that always accompanies measurement data obtained from experiments and observations. In the second half of the class, we will learn the skills to visualize and analyze data representing the relationships among the elements that make up a complex system in reality as a network.
Student learning outcomes
Students will be able to acquire the following skills:
1) Skills for primary data handling with Python.
2) Skills for quantitative evaluation of the uncertainty in data. Examples: safety assessment of pesticides residues in food, evaluation of radioactive waste amounts in the decommissioning of nuclear power plants.
3) Skills for visualizing networks and performing basic network analysis. Examples: transportation networks, social networks.
Keywords
Data analysis, Error, Uncertainty analysis, Network analysis, Visualization
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 conducted mainly in the form of lectures and exercises.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | (Lecture/Exercise) Uncertainty in measurement data obtained by experiments or observations | Understand the background of uncertainty quantification in this unit and learn the difference between an error and uncertainty. |
| Class 2 | (Lecture/Exercise) Gaussian process regression with Python | Compare the least mean-square error with the 95% confidence intervals predicted by the Gaussian process regression and discuss the relation between these quantities during this unit. |
| Class 3 | (Group work) Group work activity relating to uncertainty quantification | Discuss an uncertainty component around us and investigate how to analyze it in this unit. |
| Class 4 | (Lecture) Introduction to network science, network representation, measures and metrics (1) | Understand the usefulness of analyzing complex real-world systems as networks, and understand the basic concepts that characterize networks. |
| Class 5 | (Lecture) Measures and metrics (2), application of network science | Understand the basic concepts that characterize networks, and how network science is applied. |
| Class 6 | (Exercise) Processing of real data and network analysis using Python (1) | Understand how to process real data in Python, and how to construct, visualize, and analyze networks. |
| Class 7 | (Exercise) Processing of real data and network analysis using Python (2) | Construct and analyze a network of freely selected real data and discuss the results. |
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.
Textbook(s)
Textbooks will be introduced during the class.
Reference books, course materials, etc.
Textbooks will be introduced during the class.
Assessment criteria and methods
Reports 80&, Exercises 20%
Related courses
- TSE.M204 ： Statistics and Data Analysis
- TSE.A232 ： Engineering Measurement I
- TSE.A233 ： Engineering Measurement II
- TSE.A325 ： Data Science for Transdisciplinary Research (I)
Prerequisites (i.e., required knowledge, skills, courses, etc.)
None
Other
Students will use their own laptops in this lecture. Please do not forget to bring your laptop.
