2021 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 / Cristian Andres Mejia Caballero
- Class Format
- Lecture/Exercise
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 3-4 Sat
- Class
- -
- Course Code
- TIM.A406
- Number of credits
- 0.50.50
- Course offered
- 2021
- Offered quarter
- 2Q
- 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 applied skills in data analysis. In particular, we will lecture on the characteristics of unstructured data and their analysis methods, keeping in mind its application technology management, and acquire applied skills in data analysis through programming exercises.
Course description and aims
The goals of this course are as follows:
- To understand the basics of text analysis, network analysis, deep learning, and reinforcement learning
- To be able to apply these methods to unstructured data for the creation of new businesses
Keywords
Text, morphological analysis, sentiment analysis, social network analysis, neural networks, deep learning, reinforcement learning
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 text analysis, network analysis, and deep learning for unstructured data (text, network, images, etc.), 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 and to have a discussion on the cutting-edge applications of data science in business.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Network analysis | Understand the nature of network data, theories and methods for visualizing and analyzing networks |
| Class 2 | Text analysis | Understand the nature of text data, principles and methods of text analysis, such as morphological analysis and sentiment analysis |
| Class 3 | Programming exercise (1) | Acquire programming skills for text analysis and network analysis |
| Class 4 | Deep learning | Understand the principles of deep learning and the methods for its application to unstructured data |
| Class 5 | Reinforcement learning | Understand the principles of reinforcement learning and the methods for its application to unstructured data |
| Class 6 | Programming exercise (2) | Acquire programming skills related to deep learning and reinforcement learning |
| Class 7 | Guest lecture | Gain knowledge about cutting-edge data science applications in business |
Study advice (preparation and review)
After the lecture, it is recommended to read and review the relevant sections of the reference books.
Textbook(s)
Slides will be provided.
Reference books, course materials, etc.
Albert-Laszlo Barabasi, Network Science, Cambridge University Press (2016)
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.A405 ： Methodology of Mathematical and Computational Analysis I
Prerequisites
None