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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 Nitta Katsumi Ono Isao Miyake Yoshihiro
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- -
- Group
- -
- Course number
- DSA.P633
- Credits
- 1
- Academic year
- 2024
- Offered quarter
- 3Q
- 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 Dai-ichi Sankyo Comp. Ltd., Takenaka Corp., NGK Corporation, Mitsubishi Corporation. Please note that the companies giving the lectures are subject to change. |
Keywords
Data Science, Artificial Intelligence, Pharmaceutical companies, construction companies, materials, general trading companies
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 | Case Studies of Data Science and AI Implementations in the Enterprise | Understanding Data Science and AI Implementation Case Studies in Enterprises |
| Class 2 | Discussion of the applicability of data science in life science | This lecture will discuss how data science can contribute to the development of life sciences. |
| Class 3 | Data Science for Drug Development in Pharmaceutical Industry | How data science can contribute to drug development in pharmaceutical industry will be explained in this lecture. |
| Class 4 | Application Examples of Data Science and AI in Construction Companies | Understand data science and AI applications in construction companies |
| Class 5 | Data Science and AI Applications in Materials Companies | Understanding Data Science and AI Applications in Materials Companies |
| Class 6 | Data Science and AI Applications in a General Trading Company | Understanding Data Science and AI Applications in general trading company |
| Class 7 | Case Studies of Data Science and AI Implementations in the Enterprise | Understanding Data Science and AI Implementation Case Studies in Enterprises |
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.)
Only students of doctor curse are acceptable. Other students must take DSA.P433 " Applied Practical Data Science and AI 3C" 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.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 syllabus may be revised before the start of the third quarter.
