2025 (Current Year) Special graduate degree programs Specially Offered Degree Programs for Graduate Students Center of Data Science and Artificial Intelligence 2
Progressive Applied Practical Data Science and Artificial Intelligence 3C
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Tsuyoshi Murata / Norio Tomii / Isao Ono / Katsumi Nitta / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Yoshihiro Miyake / Takayoshi Yokota / Yutaro Tachibana / Hirofumi Takaku / Kenji Aoyagi / Masahiro Akiba / Zaixing Mao / Yutaro Watanabe / Masaharu Abe / Taizo Nakao / Yoshihiro Ueda / Takashi Okada / Masahiro Miura / Takayuki Ito
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Fri (M-B07(H101), G2-202(G221))
- Class
- -
- Course Code
- DSA.P633
- Number of credits
- 100
- Course offered
- 2025
- Offered quarter
- 3Q
- Syllabus updated
- Sep 8, 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.
This course emphasizes dialogue with corporate lecturers. In addition to the seven class sessions, students shall, in principle, attend the DS&AI Forum to be held on the afternoon of November 28, 2025 at the Oookayama Campus. (Added on September 8, 2025)
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 Sumitomo Mitsui Trust Bank Corp., Daiichi Sankyo Corp. Ltd., Institute of Science Tokyo, Nippon Steel Engineering Corp. Ltd., TOPPAN Holdings Corp. and FANUC Corp.
The following lecture schedule has been revised to be more specific (September 8, 2025).
Keywords
Data Science, Artificial Intelligence, Pharmaceutical companies, engineering, robotics, finance
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 | Science-Based Thinking Required of Financial Institutions for Solving Social Issues |
Learn about financial trends while examining real-world examples of financial support incorporating scientific insights. |
| Class 2 | Discussion of the applicability of data science in life science |
This lecture will discuss how data science can contribute to the development of life sciences. |
| Class 3 | Applications of Data Analysis and AI in the Railway Industry |
In recent years, railway companies can collect large volumes and diverse types of data, such as operational performance data and passenger congestion data. Consequently, research applying machine learning and AI to this data to mitigate congestion and prevent delays has become increasingly active. This lecture will cover the background, methodologies, and application examples of such research. |
| Class 4 | Practical use of DS in Nippon Steel Engineering |
The lecture introduces examples of how data is used in Nippon Steel Engineering. Also, we will introduce how data scientists develop their career in our company. |
| Class 5 | TOPPAN's Utilization of Data Science and AI in DX |
This lecture aims to understand and acquire knowledge of data utilization in manufacturing. It will explore how academic research is introduced into industry, drawing from examples of image/document-related R&D and data analysis/solution development within the printing business. |
| Class 6 | Manufacturing and AI – Machine Tools and AI Applications |
Understand the challenges facing manufacturing, the necessity of AI, and the real-world issues and solutions for AI implementation in production environments through concrete application examples. Focusing primarily on machine tools. |
| Class 7 | Manufacturing and AI – Industrial Robots and AI Applications |
Understand the challenges facing manufacturing, the necessity of AI, and the real-world issues and solutions for AI implementation in production environments through concrete application examples. Focusing primarily on industrial robots. |
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.
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. Grades will be evaluated based on each assignment report, the term-end report and the participation report of the DS&AI Forum scheduled for November 28. Please note that it is not possible to submit assignment reports for missed lectures. Even if a student submits an assignment report for a lecture he/she has missed, it will not be graded. (Added on September 8, 2025)
Related courses
- XCO.T677 ： Fundamentals of Progressive Data Science
- XCO.T678 ： Exercises in Fundamentals of Progressive Data Science
- XCO.T679 ： Fundamentals of Progressive Artificial Intelligence
- XCO.T680 ： Exercises in Fundamentals of Progressive Artificial Intelligence
Prerequisites
Only students of doctor curse are acceptable. Other students must take DSA.P433 " Applied Practical Data Science and AI 3C" 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, Takayoshi Yokota
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 GA0M and GA1M.