– School of Informatics –
The University of Tsukuba has established the Mathematics, Data Science, and AI Education Program for the three colleges that comprise the School of Informatics. This program is an educational program that offers a certificate of completion, which can be obtained upon completion of the requirements. Both university-wide and individual school common subjects make up this program so that all students in the School of Informatics can take the program comfortably. It is expected that almost 100% of the students in the College of Information Science and the College of Media Arts, Science and Technology and about 36% of the students in the College of Knowledge and Library Sciences will complete the program in the 2022 academic year.
(Accreditation valid until March 31, 2027)
Reference
Approved Program for Mathematics, Data science and AI Smart Higher Education (MDASH)/Advanced Literacy, Ministry of Education, Culture, Sports, Science and Technology (MEXT) (Japanese)
Application details for the Approved Program of the University of Tsukuba [PDF] (Japanese)
Overview of Program Initiatives
Program Features
Comprised of university-wide common subjects and individual school common subjects, allowing all students to take subjects comfortably
- Learning from the basics to the application of data science and AI
- Acquiring practical skills through practical exercises and project-based learning (PBL)
- Introductory video lectures in a variety of disciplines to increase interest and motivation among a diverse student body
- Utilizing Learning Management System (LMS) and various online communication tools (video conferencing, chat, bulletin board)
Regional Cooperation
- Providing course materials, program management expertise, self-inspection and evaluation results, etc.
Cooperation with Industry
- Education based on society’s needs, assessment of students by companies
Abilities That Can Be Acquired through This Educational Program
Students will learn the basic concepts, methods, and applications of AI and data science, and acquire practical skills through practical exercises and PBL which will help them acquire the ability to extract meaning from data and use it effectively, as well as the ability to solve problems by utilizing AI or building their own AI.
Completion Requirements
Students shall obtain a total of at least 10 credits. 8 credits from the core course categories that comprise the program and at least 2 credits from specialized education course categories.
授業科目
| 基礎科目群 | ・ 線形代数A（2単位） ・ 微分積分A（2単位） ・ 情報リテラシー（講義）（1単位） ・ プログラミング入門A（2単位） ・ 知能と情報科学（1単位） |
| 専門教育科目群 | ・データサイエンス（2単位） ・PBL型実験（各3単位）（ソフトウェアサイエンス実験A、情報システム実験A、知能情報メディア実験A、情報メディア実験A、ビジネスシステムデザイン基礎II、ソフトウェアサイエンス実験B、情報システム実験B、知能情報メディア実験B、情報メディア実験B、ビジネスシステムデザイン実践II） |
各授業科目のシラバスはこちらをご覧ください。
Class Details (Curriculum)
This program consists of three applied basic cores. The curriculum included in each applied basic core corresponds to the Model Curriculum for Mathematics, Data Science and AI (Advanced Literacy Level), and is covered by the class subjects listed above.
Advanced Literacy Core: I. Data Representation and Algorithms
- 1-6. Fundamentals of Mathematics
- 1-7. Algorithms
- 2-2. Data Representation
- 2-7. Basics of Programming
Advanced Literacy Core: II. Basics of AI and Data Science
- 1-1. Data-driven Society and Data Science
- 1-2. Analytical Design
- 2-1. Big Data and Data Engineering
- 3-1. History and Applications of AI
- 3-2. AI and Society
- 3-3. Fundamentals and Outlook of Machine Learning
- 3-4. Fundamentals and Outlook of Deep Learning
- 3-9. Construction and Operation of AI
Advanced Literacy Core: III. Practices of AI and Data Science
- Basics of Data Engineering
- Planning, Implementation and Assessment of Data and AI Utilization
Self-inspection and Evaluation System
System for improving and advancing the program
The School of Informatics Curriculum Committee studies and deliberates on the following matters in order to improve and advance this program.
(1) Matters related to the basic policy of information education for the following academic year
(2) Matters related to the organization of the curriculum and syllabus for the following academic year
(3) Matters related to the university-wide computer system
(4) Matters related to the improvement of class implementation methods
(5) Other matters related to information education
System for self-inspection and evaluation of the program
The Headquarters for Interdisciplinary Education on Mathematics-Data Science-AI will conduct self-inspections and evaluations of this program.
Results of 2021 Self-inspection and Evaluation (Japanese)
Results of 2022 Self-inspection and Evaluation (Japanese)
Results of 2023 Self-inspection and Evaluation (Japanese)