2026 (Current Year) Faculty Courses School of Environment and Society Department of Technology and Innovation Management Graduate major in Technology and Innovation Management
Methodology of Mathematical and Computational Analysis I
- Academic unit or major
- Graduate major in Technology and Innovation Management
- Instructor(s)
- Shuto Miyashita
- Class Format
- Lecture/Exercise
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- TIM.A405
- Number of credits
- 0.50.50
- Course offered
- 2026
- Offered quarter
- 3Q
- Syllabus updated
- Mar 5, 2026
- Language
- Japanese
Syllabus
Course overview and goals
While the use of data is highly expected in business, a proper understanding of the methodology of data analysis is required for appropriate decision-making.
In this lecture, students learn basic knowledge for quantitative analysis, especially statistical analysis, in Management of Technology.
Course description and aims
The goals of this course are as follows:
- To understand the basics of data analysis so that students can use them to solve business problems.
- To be able to program elementary data analysis, especially statistical analysis.
Keywords
Statistics, data science, quantitative analysis, descriptive statistics, hypothesis testing, regression analysis
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Lectures provide knowledge of elementary data analysis and programming exercises are scheduled after each topic.
In the programming exercises, students will learn an overview of data analysis using Python and develop the ability to apply data analysis to business problems.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Guidance |
Gain an overview of data science and quantitative analysis, especially statistical analysis, in business |
| Class 2 | Descriptive Statistics |
Understand basic summary statistics and fundamental data visualization methods |
| Class 3 | Programming exercise (1) |
Acquire programming skills in Python basics, including data handling, through exercises |
| Class 4 | Hypothesis Testing |
Explain the procedures for hypothesis testing and identify key considerations when applying the method |
| Class 5 | Programming Exercise (2) |
Acquire programming skills for hypothesis testing through exercises |
| Class 6 | Regression Analysis |
Understand the type of problems suitable for regression analysis, the assumptions underlying the method, and how to interpret the results |
| Class 7 | Programming Exercise (3) |
Acquire programming skills for regression analysis through exercises |
Study advice (preparation and review)
It is recommended to read and review the relevant sections of the textbook after the lecture.
To master programming, learning by writing code outside of class is also recommended.
Textbook(s)
Abe, Masato, Introduction to Statistics: Essential Knowledge and Ideas for Data Analysis: Complete Coverage of Important Topics from Hypothesis Testing to Statistical Modeling, Socym (2021).
Reference books, course materials, etc.
Lecture materials will be distributed.
In addition to those listed below, references will be introduced in the lecture.
- Department of Statistics, Faculty of Liberal Arts, University of Tokyo (ed.), Introduction to Statistics, University of Tokyo Press (1991).
- Foster Provost, Tom Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking O'Reilly Media (2013)
Evaluation methods and criteria
Class assignment 20%, Exercise 30%, Term paper 50%
Related courses
- TIM.B412 ： Strategic Management for Research and Development I
- TIM.B413 ： Strategic Management for Research and Development II
- TIM.A414 ： Introduction to Models and Experiments in Social Science
- TIM.B535 ： Digital Marketing
- TIM.A406 ： Methodology of Mathematical and Computational Analysis II
- TIM.A415 ： Business Data Science
Prerequisites
No prerequisites.