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 3B

Academic unit or major: Center of Data Science and Artificial Intelligence
Instructor(s): Katsumi Nitta / Takayoshi Yokota / Kei Miyazaki / Keiji Okumura / Yutaro Tachibana / Yoshihiro Miyake / Jun Sakuma / Isao Ono / De Araujo Paulo Fernando / Mehdi Messaoudi / Takao Mori / Sourish Chatterjee / Pavel Ermakov / Fabio Pellegrini
Class Format: Lecture (Livestream)
Media-enhanced courses: -
Day of week/Period (Classrooms): 7-8 Wed
Class: -
Course Code: DSA.P432
Number of credits: 100
Course offered: 2026
Offered quarter: 3Q
Syllabus updated: Sep 17, 2026
Language: English

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 late November at the Oookayama Campus.

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. The lecture is conducted in English.

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 offers lectures based on practical industry experience, delivered by guest speakers from Rakuten Group, Inc., DENSO CORPORATION, Takenaka Corporation, Daiichi Sankyo Company, Limited, and Institute of Science Tokyo.

Keywords

Data Science, AI, Manufacturing, Construction, Pharmaceutical Companies, Machine Learning, Data Utilization

Competencies

Specialist skills
Intercultural skills
Communication skills
Critical thinking skills
Practical and/or problem-solving skills

Class flow

This lecture is an online lecture conducted via Zoom.

Course schedule/Objectives

	Course schedule	Objectives
Class 1	Tips and Tricks for Building Large Scale Web Services	This lecture covers key concepts of Web Scalability. Students will learn common terminologies of Distributed Systems, implications of the dynamics of growth, and strategies for dealing with high traffic web services, distributed data and scalable organizations.
Class 2	Cybersecurity in the age of Agentic AI - Securing the Future	As cyber threats become more sophisticated by the day, Cybersecurity professionals are constantly challenged to innovate in how they fulfill their mission of protecting their organizations. This lecture will cover the fundamentals of Cybersecurity as well as the perspectives the advent of Agentic AI has brought upon the industry.
Class 3	Inverse Problems & Solution Space in Agentic AI	Learning problem‑solving in Agentic AI through the perspectives of inverse problems and solution space.
Class 4	Robots meet Physical AI at Construction Sites: Physical AI from the perspective of General Contractors	In this lecture we would like to explain the recent developments in the area of Physical AI in the construction industry. We would also like to share the information in this lecture from the perspective of a General Contractor company.
Class 5	Causal Inference Methods for Evidence Generation in Pharma	This lecture will discuss the role of causal inference in evidence generation. Both RCTs and observational studies will be addressed, showing how fundamental causal inference is in the design and analysis of clinical studies. A philosophical perspective of causality and examples coming from other disciplines will be provided.
Class 6	The Question AI Cannot Answer Alone: From Data to Trusted Decisions	Generative AI can produce fluent answers, but organizations still need shared definitions, reliable evidence, controls, and accountable decision-makers. Using the deceptively simple question “How many customers do we have?”, this lecture reveals how trustworthy data and AI systems are engineered.
Class 7	AI Use Case Studies: How is AI&DS being used in business?	This lecture will teach participants about the recent business applications of AI and data science, providing examples of support cases from the perspective of an AI consultant who supports companies use of these technologies.

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 and the participation report of the DS&AI Forum scheduled for late November. 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.

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.P632 "Progressive Applied Practical Data Science and AI 3B".

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 Practical AI and Data Science C2 (XCO.T495-2), which was offered until FY2023. Students who took Practical AI and Data Science C2 as undergraduates should register for this course. Students who took Practical AI and Data Science C2 in graduate school may not register for this course.