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 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
- 2025
- Offered quarter
- 4Q
- Syllabus updated
- Mar 19, 2025
- 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
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 | Introduction to Business Data Science | Understand the big picture of business data science and learn the basics of programming |
| Class 2 | Supervised learning | Understand typical supervised learning methods, such as regression, prediction, and classification |
| Class 3 | Unsupervised learning | Understand typical unsupervised learning methods, such as clustering and dimensionality reduction |
| Class 4 | Programming exercise 1 | Develop programming skills for supervised and unsupervised learning |
| Class 5 | Network analysis | Understand the nature of network data and the principles and methods for visualizing and analyzing networks |
| Class 6 | Text analysis | Understand the nature of text data and the principles and methods of natural language processing, including morphological analysis and sentiment analysis |
| Class 7 | Programming exercise 2 | Develop programming skills related to network and text analysis |
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
Evaluation methods and criteria
Class contribution 20%, Exercise 40%, Report 40%
Related courses
- TIM.A414 ： Introduction to Models and Experiments in Social Science
- TIM.B535 ： Digital Marketing
- TIM.A405 ： Methodology of Mathematical and Computational Analysis I
Prerequisites
None