2025 (Current Year) Special graduate degree programs Specially Offered Degree Programs for Graduate Students Center of Data Science and Artificial Intelligence
Progressive Applied Practical Data Science and Artificial Intelligence 2C
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Tsuyoshi Murata / Norio Tomii / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Katsumi Nitta / Isao Ono / Yoshihiro Miyake
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Fri
- Class
- -
- Course Code
- DSA.P623
- Number of credits
- 100
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
The purpose of this class course is to understand the current status and state-of-the-art of social implementation of AI and data science technologies, and to examine the applicability and challenges of these technologies. In each class, lecturers from companies in various fields such as architecture, IT, finance, and materials will introduce case studies of technology and product development using data science and AI.
The goal is for students to gain a broad perspective of the real world by acquiring knowledge about the application of data science and AI technologies in a wide range of fields, and by explaining their considerations about social applications in their assigned reports.
Course description and aims
This course aims to develop ability of each student to be more successful in the real world with the consideration of social implementation of data science and artificial intelligence.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
In this course, lectures based on practical experience are given by lecturers of NGK Insulators, Ltd., Mitsubishi Corporation, Nittetsu Engineering Corporation, JFE Engineering Corporation, Rakuten Group, Inc.
Keywords
Data science, AI, machine learning, materials, general trading companies, engineering companies, IT services
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
This course is classified as a high-flex type, but can only be taken in designated classrooms in Ookayama and Suzukakedai.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Digital Transformation Initiatives at NGK INSULATORS, LTD. | Overview of NGK Insulators' data utilization strategy and practical applications in the industry |
| Class 2 | Business Creation Utilizing AI and Data Science at Mitsubishi Corporation | To understand the social implementation of AI and DS through Mitsubishi Corporation's business |
| Class 3 | Practical Application of AI and DS in Plant Engineering Companies | Introducing the practical use of generative AI and data science in a plant engineering company. |
| Class 4 | Utilizing Data & AI technologies in Plant Engineering | This lecture will introduce how data and artificial intelligence are being utilized in Plant Engineering to solve business challenges, by presenting case studies. |
| Class 5 | Notes and development examples for building large-scale web services | The lecture will introduce the contents to be considered and matters to be noted when building large-scale Web services, based on case studies, as well as examples of development in payments. |
| Class 6 | R&D projects in Rakuten Group | In this lecture, we’d like to introduce the application of research outcomes in actual services at Rakuten. |
| Class 7 | Accelerating AI Development and Social Implementation | This lecture will discuss the trends in advanced AI, such as Large Language Models (LLMs), and address the challenges in AI product development and social implementation. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None required.
Reference books, course materials, etc.
Materials will be provided on Science Tokyo LMS in advance.
Evaluation methods and criteria
No final exam will be given. The evaluation will be based on the reports of each assignment and the term-end report.
Related courses
- XCO.T487 ： Fundamentals of data science
- XCO.T488 ： Exercises in fundamentals of data science
- XCO.T489 ： Fundamentals of artificial intelligence
- XCO.T490 ： Exercises in fundamentals of artificial intelligence
Prerequisites
Only students of doctor curse are acceptable. Other students must take DSA.P423 " Applied Practical Data Science and AI 2C" instead of this course.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Asako Kanezaki, Katsumi Nitta, Norio Tomii
lecture_ap[at]dsai.isct.ac.jp
Office hours
Contact by e-mail in advance to schedule an appointment.
Other
This class is a technical course that can be considered an entrepreneurship course. The GAs that this subject corresponds to are GA0D and GA1D.