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 II
- Academic unit or major
- Graduate major in Technology and Innovation Management
- Instructor(s)
- Kazutoshi Sasahara
- Class Format
- Lecture/Exercise
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- TIM.A406
- Number of credits
- 0.50.50
- Course offered
- 2026
- Offered quarter
- 4Q
- Syllabus updated
- Mar 5, 2026
- Language
- Japanese
Syllabus
Course overview and goals
Students will learn data science to use vast and diverse data for business. In particular, the characteristics of structured and unstructured data and their analysis methods will be lectured, with application to technology management in mind, and students will acquire basic skills in business data analysis through programming exercises.
Course description and aims
The goals of this course are as follows:
- To understand the basics of machine learning, text analysis, network analysis
- To be able to use data analysis to understand and solve business problems
Keywords
Regression, classification, prediction, clustering, text analysis, network analysis, generative AI
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The theory of data science for analyzing structured and unstructured data will be lectured, and programming exercises will be used to consolidate understanding and develop practical skills in business data analysis. Python will be used for programming exercises.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Machine Learning (1) |
Understand basics and typical supervised learning methods, such as regression, prediction, and classification |
| Class 2 | Machine Learning (2) |
Understand typical unsupervised learning methods, such as clustering and dimensionality reduction |
| Class 3 | Network analysis (1) |
Understand the nature of network data and the principles and methods for visualizing and analyzing networks |
| Class 4 | Network analysis (2) |
Understand the theoretical models that generate networks and how to apply them to real-world problems |
| Class 5 | Text Analysis (1) |
Understand the fundamentals of text analysis using preprocessing and statistical techniques |
| Class 6 | Text analysis (2) |
Understand fundamentals and applications of text analysis using machine learning |
| Class 7 | Analysis using generative AI |
Understand of the fundamentals of generative AI and it's application techniques to real-world data |
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.
- Sebastian Raschka et al. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
- F. Menczer et al. A First Course in Network Science
Evaluation methods and criteria
Class contribution 20%, Report 80%
Related courses
- TIM.A405 ： Methodology of Mathematical and Computational Analysis I
- TIM.A415 ： Business Data Science
Prerequisites
None