2026 (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 1A

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 / Yusuke Nishigata / Yusuke Tashiro / Jun Deguchi / Tomoya Kodama / Ryota Yoshizawa / Kenta Naruse / Naoki Takahashi / Mitsuru Nakazawa / Katsuro Tanaka / Yusuke Ota / Shinya Kojima
Class Format: Lecture (HyFlex)
Media-enhanced courses: -
Day of week/Period (Classrooms): 7-8 Tue (M-B07(H101), J2-302(J233))
Class: -
Course Code: DSA.P611
Number of credits: 100
Course offered: 2026
Offered quarter: 1Q
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.
Therefore, in addition to the seven class sessions, this course emphasizes dialogue with company lecturers, and in principle, students shall participate in the DS&AI Forum to be held face-to-face on the Ookayama campus in late May.

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)

Lecturers from Kioxia Corporation, Rakuten Group, Inc., Mitsubishi UFJ Trust and Banking Corporation, Electric Power Development Co., Ltd. (J-POWER), DENSO CORPORATION, and Institute of Science Tokyo will deliver lectures on problem-solving techniques based on their practical experience.

Keywords

Data Science, Artificial Intelligence, Machine Learning, Finance, Semiconductor, AI & Law, electric power development, construction

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	R&D of AI in Kioxia	AI-applied in memory development /production and the concept of memory-centric generative AI
Class 2	Introducing AI Technologies and Use Cases from the AI & Data Division at　Rakuten Group, Inc.	In this lecture, we will present an overview of AI technologies driven by the AI & Data Division at Rakuten Group, Inc., which operates over 70 services, along with recent use cases, such as AI agents, PII masking, and sports video analysis.
Class 3	Overview of Search Engine	The overview of a search engine that returns relevant documents and products quickly.
Class 4	Positioning Technologies and AI / Data Science	This lecture examines the technological evolution of positioning systems, from early navigation techniques in the Age of Exploration to satellite positioning technologies developed during the Cold War, culminating in contemporary location information processing technologies. It further explores their relationship with rapidly advancing AI and data science technologies.
Class 5	Trust × AI: Applied Data Science for Empowering Financial Operations through Data	Learn the overall landscape and societal significance of AI utilization in trust banking.
Class 6	DSAI Applications and R&D at J-POWER: A Case Study of Autonomous Flight Drone Development	This lecture introduces examples of DSAI applications and research and development projects within an electric power
Class 7	Practical Introduction to Image Recognition	Learning image recognition using machine learning through practical examples of product development.

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 and the term-end report.
The evaluation will also include the results of participation in the DS&AI Forum to be held in late May.

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.P411 " Applied Practical Data Science and AI 1A" instead of this course.

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 GA0D and GA1D.
・This course corresponds to Progressive Applied AI and Data Science C1 (XCO.T689-1), which was offered until FY2023. Students who have taken Progressive Applied AI and Data Science C1 may not take this course.