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 3B
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Norio Tomii / Tsuyoshi Murata / Isao Ono / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Katsumi Nitta / Yoshihiro Miyake / Takayoshi Yokota / Yutaro Tachibana / Kazuya Ikoma / Hironori Tanji / Takashi Handa / Yoshitaka Okazaki / Kei Furukawa / Tsuyoshi Moriya / Ryosuke Nagumo / Masaru Sakamoto / Tomonori Takahashi / Kunihiro Takeoka
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Wed (M-B07(H101), G2-202(G221))
- Class
- -
- Course Code
- DSA.P632
- 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 5, 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)
This course will be taught by lecturers from Shimizu Corp., Tokyo Electron Corp., Panasonic Corp., Toyo Engineering Corp., MUFG Bank Ltd. and NEC Corp. based on their practical experience.
The following lecture schedule has been revised to be more specific (September 8, 2025).
Keywords
Data Science, Artificial Intelligence, FinTech, Manufacturing, Construction, Machine Learning, Data Utilization
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 | AI and Data Application in the Construction Industry |
Learn the importance of digitalization through examples of AI and data utilization initiatives. |
| Class 2 | The Future of Semiconductor Manufacturing Equipment Created by AI and Data Science |
Deepen the understanding of the contributions and applications of AI and DX in the semiconductor manufacturing process. |
| Class 3 | Application of AI Technology and Basics of Machine Learning in Semiconductor Manufacturing Processes |
Deepen the understanding of the basics and technical methods of machine learning. |
| Class 4 | Applications of Statistical Machine Learning in Manufacturing |
This paper introduces case studies applying statistical machine learning to practical data analysis. |
| Class 5 | Data Challenges and Utilization Approaches in the Construction Project Business |
This lecture outlines key challenges in data utilization for construction projects with unique characteristics and introduces basic approaches and technologies to address them. |
| Class 6 | Application of Data Science in Financial Market |
This session provides overview of applications of data science in foreign exchange market, especially from commercial bank perspective. |
| Class 7 | The Era of “Entrusting” Search: The Future of Knowledge Acquisition Pioneered by Agentic AI |
Understanding the Transformation and Impact on Daily Life Brought by Agentic AI, Grounded in the History of Search |
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.P432 " Applied Practical Data Science and AI 3B" 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.