2026 (Current Year) Special graduate degree programs Specially Offered Degree Programs for Graduate Students Center of Data Science and Artificial Intelligence
Applied Practical Data Science and Artificial Intelligence 2A
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Katsumi Nitta / Takayoshi Yokota / Norio Tomii / Kei Miyazaki / Keiji Okumura / Yutaro Tachibana / Yoshihiro Miyake / Jun Sakuma / Isao Ono
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue (M-124, J2-302(J233))
- Class
- -
- Course Code
- DSA.P421
- Number of credits
- 100
- Course offered
- 2026
- Offered quarter
- 2Q
- Syllabus updated
- Mar 5, 2026
- 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, instructors from Nippon Steel Engineering Co., Ltd., NEC Corporation, teamLab Inc., Sumitomo Mitsui Trust Bank, Limited, DIC Corporation, KAJIMA CORPORATION, and Kanadevia Corporation will provide lectures on problem-solving techniques based on their practical business experience.
Keywords
Data Science, Artificial Intelligence, Machine Learning, Finance, IT, digital art, general trading company, transportation
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 | Practical Use of AI and DS at Nippon Steel Engineering |
This lecture will introduce examples of how generative AI and data science are being utilized at Nippon Steel Engineering. |
| Class 2 | What Changes When We Let AI Do the Searching? — A History of Search and the Future of AI Agents |
Tracing the history of search and exploring how AI agents are transforming the way we find knowledge |
| Class 3 | Current Status of AI-Driven Software Development at teamLab and Changing |
To understand the current status and future outlook of AI-driven development processes at teamLab |
| Class 4 | Scientific Thinking for Financial Institutions: New Approaches to Solving Social Challenges |
his course examines how financial institutions can apply scientific knowledge to address social issues, using real-world examples. |
| Class 5 | How to Utilize Data Science in Materials Development |
Introduction to data science applications and case studies within chemical manufacturers. |
| Class 6 | Smart Campus Development through AI and Data Utilization |
Discuss how AI and data can be leveraged to create a better campus environment. |
| Class 7 | Business Growth and Digital Strategy at Kanadevia |
Practical Data-Driven Approaches for Growth in Environmental and Carbon Neutral Solution Businesses |
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. The evaluation will be based on the reports of each assignment.
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
Doctoral students must take DSA.P621 "Progressive Applied Practical Data Science and AI 2A".
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
Katsumi Nitta, 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.
・This course corresponds to Applied AI and Data Science B (XCO.T484), which was offered until FY2023. Students who took Applied AI and Data Science B as undergraduates should register for this course. Students who took Applied AI and Data Science B in graduate school may not register for this course.