2025 (Current Year) Faculty Courses School of Environment and Society Department of Technology and Innovation Management Graduate major in Technology and Innovation Management
Methodology of Political Science and Economics II
- Academic unit or major
- Graduate major in Technology and Innovation Management
- Instructor(s)
- Takuji Matsumoto
- Class Format
- Lecture/Exercise (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Sat (CIC)
- Class
- -
- Course Code
- TIM.A404
- Number of credits
- 0.50.50
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Jun 13, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course introduces the fundamental concepts of economics and modeling methods through an empirical analysis perspective, using real-world economic and market data.
Students will learn techniques for conducting quantitative analysis through applied data exercises, and develop the ability to interpret and apply analysis results from an economic perspective.
Course description and aims
This course aims to achieve:
(1) Understand the basic concepts of economics and analytical modeling.
(2) Acquire the ability to perform quantitative analysis using economic and market data.
(3) Develop the ability to explain and discuss analytical results from an economic viewpoint.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
This course draws on the instructor’s practical experience in data analysis at government agencies and private-sector companies, and incorporates real-world case studies into the curriculum.
Keywords
Economic data, Market data, Quantitative analysis, Econometrics
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The course combines lectures on the fundamentals of economics and modeling with hands-on data analysis exercises.
Students will also engage in peer discussions to deepen their understanding and strengthen their ability to interpret analytical results in practical contexts.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Guidance | Introduction to economic analysis |
| Class 2 | Regression Analysis | Understanding relationships in data |
| Class 3 | Causal Inference | Measuring causal effects |
| Class 4 | Detection of Anomalies and Structural Changes | Interpreting deviations in data |
| Class 5 | Forecasting and Machine Learning | Predicting future trends |
| Class 6 | Group presentation | Conduct presentation on contents of group work |
| Class 7 | Group presentation | Conduct presentation on contents of group work |
Study advice (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)
None specified
Reference books, course materials, etc.
Distribute lecture slides via LMS.
Evaluation methods and criteria
Student performance will be evaluated based on the following: Class participation (30%), Group presentation (30%), Final report (40%).
Related courses
- None
Prerequisites
None specified