Data Science Courses at SFC | Yoshiaki Miyamoto, Assistant to the Dean of the Graduate School of Media and Governance
I never thought I would be contributing to the "Okashira Diary," so when I was approached, I was a bit unsure of what to write. However, looking through the archives of my predecessors, I noticed that there weren't many introductions to Data Science courses (DS courses). Therefore, I would like to take this opportunity, as one of the faculty members involved in data science at SFC, to introduce their characteristics.
Currently, SFC systematically offers Data Science courses as mathematics-related subjects. These consist of four DS1 courses covering fundamental content (Linear Algebra, Calculus, Probability, and Basic Statistics) and DS2 courses that apply that knowledge in various fields. A notable feature of DS1 courses is that each is offered in both Japanese and English. What makes SFC's mathematics courses unique compared to other universities or campuses is the highly diverse backgrounds of the faculty members. In addition to faculty specializing in mathematics, many faculty members like myself, who specialize in related fields, are also involved. While the course content and lecture materials for DS1 courses are standardized to some extent, the atmosphere of the classes and the nature of the small talk vary by instructor, truly reflecting a "to each their own" variety.
Incidentally, Data Science courses are positioned as "required courses" at SFC. In an environment like SFC, where students freely choose courses based on their interests, required courses are limited to languages, physical education, and information technology; this highlights the importance SFC places on Data Science. Certainly, various mathematics subjects do not always lead immediately to research that is useful to society. However, basic mathematical literacy serves as an important foundation not only for the diverse research fields handled at SFC but across many fields in general, and it is positioned as a fundamental understanding that SFC students should acquire.
The Data Science Education Committee, which discusses these Data Science courses, also brings together faculty members with truly diverse backgrounds. Their specialties vary widely, including mathematics, computer science, brain science, biology, economics, and earth and space science. Because our backgrounds differ, the topics I hear during discussions are fresh and stimulating. The fact that faculty members who do not specialize in mathematics teach mathematics courses is itself unusual, and I feel that differences in teaching methods and points of emphasis arise according to each instructor's expertise. I believe this is another aspect of what makes SFC unique.
In recent years, the development of AI has been remarkable. Many AI technologies, including generative AI, are based on mathematical sciences, making knowledge of linear algebra, calculus, probability, and statistics indispensable. In the future, using AI in some form will become commonplace. At that time, while it may be difficult to fully understand every mechanism, having a rough grasp of the principles behind how it works is important for understanding the limitations and weaknesses of AI. AI is just one example, but given this background, the importance of Data Science courses will remain steadfast, and they will continue to be positioned as required courses at SFC. As one of the faculty members, I want to contribute so that this becomes a strength for the students who study at SFC.