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- Academic unit or major
- Center of Data Science and Artificial Intelligence
- Instructor(s)
- Kanezaki Asako Murata Tsuyoshi Tomii Norio Miyazaki Kei Okumura Keiji Sakuma Jun Ono Isao Nitta Katsumi Miyake Yoshihiro Nishimoto Hideaki Seo Noriaki Takeshima Shota Oida Yoshiaki Sakata Ryuji Kanai Tsukasa Arisaka Sohei Morita Junya
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- Day/Period(Room No.)
- Fri7-8(M-B07(H101), J2-303(J234))
- Group
- -
- Course number
- DSA.P423
- Credits
- 1
- Academic year
- 2024
- Offered quarter
- 2Q
- Syllabus updated
- 2024/3/29
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
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.
Student learning outcomes
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.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| In this course, lectures based on practical experience are given by lecturers of Kajima Construction, Daiichi Life, Toyo Engineering, Nippon Steel, Fujitsu ltd., Sumitomo Mitsi Trust Bank, Panasonic. |
Keywords
Data Science, Artificial Intelligence, Life Insurance, Fintec, Material, Manufacturing Industry, Heavy Industry, Construction
Competencies that will be developed
| ✔ 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/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Kajima Corporation's efforts in Singapore to realize a data-enabled society | Learning the essentials of digital utilization with the example of the company's building "The GEAR |
| Class 2 | DX Promotion and Use of AI and Data Science in Life Insurance Companies | This lecture will provide a picture of how AI and data science can be used for DX promotion in life insurance companies, with examples. |
| Class 3 | Data challenges and approaches for unique projects. | The plant EPC business requires the handling of a wide variety of data, as each individual project is unique. However, the same data has different meanings and conditions depending on the project situation and background. These characteristics make data utilisation and analysis difficult. What approaches and case studies are available to address these challenges? Learn about specific approaches through case studies of data utilization. |
| Class 4 | Applications of DS and AI technologies in Nippon Steel Corporation | Digital transformation measures Nippon Steel Corporation has been promoting by utilizing DS and AI technologies |
| Class 5 | Design and Execution of AI Implementation Projects | This course introduces multiple real-world examples of practical AI implementation projects and provides an overview of key success factors of the project management. |
| Class 6 | Impact Orientation and Scientific Thinking in ESG Finance | Learning from the logical perspective of impact (societal outcomes) and the cutting-edge domain of ESG finance where science and finance merge |
| Class 7 | Kaggle and Practical Applications of Data Science | Learn the necessary knowledge to make use of data science, machine learning and other technologies in the real world. |
Out-of-Class Study Time (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 T2SCHOLA in advance.
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
Doctoral students must take DSA.P623 "Progressive Applied Practical Data Science and AI 2C".
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.titech.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 (added March 29, 2024).
・This course corresponds to Practical AI and Data Science B1 (XCO.T494-1), which was offered until FY2023. Students who took Practical AI and Data Science B1 as undergraduates should register for this course. Students who took Practical AI and Data Science B1 in graduate school may not register for this course.
