2025 (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 1B
- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Asako Kanezaki / Katsumi Nitta / Norio Tomii / Kei Miyazaki / Keiji Okumura / Jun Sakuma / Yoshihiro Miyake / Isao Ono / Iurii Leonychev / De Araujo Paulo Fernando / Cayzac Julien / Roger Garriga / Koyori Tsunashima / Adrian Jimenez Pascual / Junichi Kosaka / Yoshiyuki Kobayashi
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Wed
- Class
- -
- Course Code
- DSA.P612
- Number of credits
- 100
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Mar 19, 2025
- 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.
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 the afternoon of May 28, 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.e.
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, lecturers from Rakuten Group, Daiichi-Sankyo, Sumitomo Co., Sony will lecture on problem-solving techniques based on their practical experience.
Keywords
Data Science, Artificial Intelligence, Machine Learning, Pharmaceutical manufacture, IT, General Trading Company
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
This course is a live class by Zoom.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Navigating Quality and Safety in LLM-Generated Source Code | . Introduction and Current State, 2. LLM Training Process, 3. RLHF Deep Dive, 4. Feedback Collection & Security Risks, 5. OWASP LLM & Quality Gates, 6. Future Trends & Best Practices, 7. Q&A, 8. Additional Resources |
| Class 2 | Tips and Tricks for Building Large Scale Web Services | • Key Concepts About Web Scalability • Internet Business Trends • Common Terminology for Distributed Architectures • Dynamics of Growth • Scalable Design: High Traffic, Distributed Data • How to Prepare Organizations for Growth |
| Class 3 | Mastering Software Architecture Communication: Tools, Techniques& Best Practices | In this lecture, you will learn how to effectively communicate software architecture within teams and across stakeholders. You will gain practical skills in documenting systems,components and flows, as well as in relating those to risks, constraints and decisions. By the end of this session, you will know how to convey complex architectural concepts and data flows efficiently using visual languages and structured documents. ※The lecture assumes students already have some basic knowledge in software architecture. |
| Class 4 | Data Science in Healthcare: Transforming the Patient's Journey with Artificial Intelligence | Introduce practical real-world applications of AI in medicine and provide an understanding of AI methodologies applicable to medical science |
| Class 5 | DX Strategies for “Sogo Shosha”, General Trading Company, Learning from the Fields— Practical Examples of Data Analysis and AI Utilization. | Understanding DX strategies and use cases of data science and AI in a general trading company. |
| Class 6 | Promotion of utilization and application examples of deep learning in Sony ＃1 | Learn about the current state of AI from a corporate perspective. |
| Class 7 | Promotion of utilization and application examples of deep learning in Sony ＃2 | Learn about examples of corporate efforts toward the AI era. |
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.
The evaluation will also include the results of participation in the DSAI Forum to be held on May 28, 2025.
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.P412 " Applied Practical Data Science and AI 1B" 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
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 C2 (XCO.T689-2), which was offered until FY2023. Students who have taken Progressive Applied AI and Data Science C2 may not take this course.