2022 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)
- Kazutoshi Sasahara / Shuto Miyashita
- Class Format
- Lecture/Exercise (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Sat (CIC)
- Class
- -
- Course Code
- TIM.A405
- Number of credits
- 0.50.50
- Course offered
- 2022
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
Students will learn data science to utilize vast and diverse data for business, and acquire basic skills in data analysis. In particular, we will lecture on the characteristics of structured data and their analysis methods, keeping in mind its application to technology management, and acquire basic skills in data analysis through programming exercises.
Course description and aims
The goals of this course are as follows:
- To understand the basics of data visualization, statistical analysis, and machine learning
- To be able to use these methods to structured data for understanding and solving business problems
Keywords
Descriptive statistics, hypothesis testing, data visualization, regression, classification, prediction, clustering, association analysis
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
We will lecture on the basics of statistics and machine learning for structured data, and through programming exercises, students will solidify their understanding and develop practical skills for data analysis (using Python and R). In addition, we will invite a corporate data scientist to lecture on the cutting-edge applications of data science in business.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction to data science | Understand the overview of data science in business |
| Class 2 | Data visualization and statistical analysis | Understand theories and methods for data visualization and statistical analysis |
| Class 3 | Programming exercise (1) | Acquire programming skills for data visualization and statistical analysis |
| Class 4 | Supervised learning | Understand typical supervised learning methods, such as regression, prediction, classification. |
| Class 5 | Unsupervised learning | Understand typical unsupervised learning methods, such as clustering and association |
| Class 6 | Programming exercise (2) | Acquire programming skills for supervised and unsupervised learning |
| Class 7 | Guest lecture | Gain knowledge about cutting-edge data science applications in business |
Study advice (preparation and review)
It is recommended to read and review the relevant sections of the reference books after the lecture.
Textbook(s)
Slides will be provided.
Reference books, course materials, etc.
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 contribution 20%, Exercise 40%, Report 40%
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
Prerequisites
None