Kochi University Literacy-Based Mathematics and Data Science Education Program
This program has been certified under the Ministry of Education, Culture, Sports, Science and Technology’s “Certification System for Mathematics, Data Science, and AI Education Programs (Literacy Level).” (Certification valid until March 31, 2027)
1. What is the Kochi University Literacy Level "Mathematics and Data Science Education Program"?
In recent years, with the rapid advancement of digital transformation (DX), there has been a growing demand in society for professionals equipped with the skills to understand and work with data science. To cultivate such professionals, our university has been offering a foundational mathematics and data science education program since the 2021 academic year.
2. Program Overview
本プログラムの対象は、令和２年度以降入学の全学部生です。
令和２年度〜令和５年度までの入学生は初年次必修科目「情報処理」を履修し、選択科目A群、B群よりそれぞれ１科目以上履修した学生が修得要件を満たします。プログラム修了に必要な科目は全て、共通教育において開講し、希望する全学部生が受講可能となっています。
令和６年度以降入学生は、初年次必修科目「情報とデータリテラシー」および「データサイエンス入門」を修得すれば、全員がプログラムの修得要件を満たすことができます。
また、プログラム修了者を対象としたアンケートの回答内容、並びにプログラム構成科目の担当教員及び参加企業・自治体からの意見を基に自己点検・評価を行い、プログラムの改善・進化に努めています。
3. Skills you can acquire through the program
The ability to understand how data is used to inform decision-making in the real world, as well as to evaluate the reliability of that data
The ability to understand the mechanisms of AI as it is utilized in the real world
4. Program Structure
Students Enrolled from the 2020 to 2023 Academic Years
| Category | Course Title | Unit | Item 1 | Item 2 | Item 3 | Item 4 | Item 5 |
|---|---|---|---|---|---|---|---|
| Required | Information Processing | 2 | ○ | ○ | ○ | ○ | |
| Option A | Digital Transformation and Business Creation | 2 | ○ | ○ | ○ | ○ | |
| Hands-On AI Course: — From Basic Theory to Practical Applications Using Cloud Services — | 2 | ○ | ○ | ○ | ○ | ||
| Option B | Introduction to Programming for Data Analysis | 2 | ○ | ○ | ○ | ○ | |
| Practical Data Science Exercises | 2 | ○ |
Students Enrolling in the 2024 Academic Year and Beyond
| Category | Course Title | Unit | Item 1 | Item 2 | Item 3 | Item 4 | Item 5 |
|---|---|---|---|---|---|---|---|
| Required | Information and Data Literacy | 2 | ○ | ○ | ○ | ○ | |
| Introduction to Data Science | 2 | ○ | ○ | ○ | ○ | ○ |
*Items 1–5 are the evaluation criteria for the Ministry of Education, Culture, Sports, Science and Technology’s “Certification System for Mathematical, Data Science, and AI Education Programs (Literacy Level).”
This program is structured so that graduates can meet all the content and elements outlined in these five items.
[Reference] The five evaluation criteria and Corresponding Section: Item | Assessment Item | Corresponding Section in Model Curriculum
| Item | Evaluation Criteria | Sections aligned with the model curriculum |
|---|---|---|
| Item 1 | Mathematics, data science, and AI are making significant contributions to the ongoing social changes (such as the Fourth Industrial Revolution, Society 5.0, and the data-driven society), and they are closely intertwined with our daily lives. | Introduction 1-1. Changes Taking Place in Society 1-6. Latest Trends in Data and AI Utilization |
| Item 2 | The scope of "data used in society" and "areas of data application" addressed by mathematics, data science, and AI is extremely broad, and these fields have the potential to serve as useful tools for solving everyday problems and societal challenges. | Introduction 1-2. Data Used in Society 1-3. Areas of Application for Data and AI |
| Item 3 | Case studies of data utilization from various real-world settings were presented, demonstrating that mathematics, data science, and AI create value when combined with insights from diverse fields such as distribution, manufacturing, finance, services, infrastructure, public sector, and healthcare. | Introduction 1-4. Technologies for Data and AI Utilization 1-5. Real-World Applications of Data and AI |
| Item 4 | However, mathematics, data science, and AI are not panaceas, and it is important to take into account various considerations when utilizing them (such as ELSI, personal information, data ethics, and social principles for AI). | Guidelines 3-1. Points to Consider When Utilizing Data and AI 3-2. Points to Consider When Protecting Data |
| Item 5 | Topics related to the basic applications of mathematics, data science, and AI—such as "reading, interpreting, and handling data"—using real-world examples from society, including exercises based on actual data and real-world problems (such as academic data). | Basics 2-1. Reading Data 2-2. Describing Data 2-3. Working with Data |
5. Graduation Requirements
(Students enrolled from FY2020 to FY2023)
Students who complete at least one course from each of the Required Courses, Elective Group A, and Elective Group B (for a total of 6 credits or more) will be certified as having completed the university’s Mathematical and Data Science (Literacy Level) program.
Please note that no application or other procedures are required to participate in or receive certification for the program. Completion is certified upon earning the required credits, and a certificate will be issued.
(Students Enrolling in FY2024 and Later)
Upon completing the first-year required courses “Information and Data Literacy” and “Introduction to Data Science” (a total of 4 credits), you will be certified as a graduate of the university’s Mathematical and Data Science (Literacy Level) program.
Please note that since these are university-wide required courses, all students will be certified as program graduates upon graduation.
6. Track Record
| fiscal year | Number of graduates | Breakdown |
|---|---|---|
| Fiscal Year 2023 | 18名 | 人文社会科学部６名、理工学部３名 農林海洋科学部９名 |
| Fiscal Year 2022 | 11名 | 人文社会科学部２名、理工学部７名 農林海洋科学部２名 |
| Fiscal Year 2021 | 33名 | 人文社会科学部17名、理工学部６名 農林海洋科学部８名、地域協働学部２名 |
7. Implementation Structure
| Committees, etc. | Role |
|---|---|
| Director of Education | Program Director |
| 教育情報委員会・データサイエンスセンター | Program Implementation and Improvement |
| University-wide Education Organization Meeting | Program Self-Assessment and Evaluation |