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
This program is open to all undergraduate students who enrolled in or after the 2020 academic year.
Students who enrolled between the 2020 and 2023 academic years will fulfill the program requirements by completing the first-year required course “Information Processing” and taking at least one course from each of the Elective Groups A and B. All courses required for program completion are offered as part of the general education curriculum and are open to all undergraduate students who wish to take them.
Students admitted in the 2024 academic year and thereafter will fulfill the program’s completion requirements simply by completing the first-year required courses “Information and Data Literacy” and “Introduction to Data Science.”
Furthermore, we conduct self-assessments and evaluations based on responses to surveys of program graduates, as well as feedback from faculty members teaching program courses and participating companies and local governments, and we strive to improve and evolve the program.
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 | 6 students from the Faculty of Humanities and Social Sciences, 3 students from the Faculty of Science and Engineering 9 students from the Faculty of Agriculture, Forestry, and Marine Sciences |
| Fiscal Year 2022 | 11名 | 2 students from the Faculty of Humanities and Social Sciences, 7 students from the Faculty of Science and Engineering 2 students from the Faculty of Agriculture, Forestry, and Marine Sciences |
| Fiscal Year 2021 | 33名 | Faculty of Humanities and Social Sciences: 17 students; Faculty of Science and Engineering: 6 students Faculty of Agriculture, Forestry, and Marine Sciences: 8 students; Faculty of Regional Collaboration: 2 students |
7. Implementation Structure
| Committees, etc. | Role |
|---|---|
| Director of Education | Program Director |
| Committee on Educational Information and Data Science Center | Program Implementation and Improvement |
| University-wide Education Organization Meeting | Program Self-Assessment and Evaluation |