Educational and Research Policy

The School of Data Science and Management is founded on the principle of contributing to the growth of regional industries and the local economy in a future society--"Society 5.0"--through the promotion of education, research, and community engagement in data science and management.
To achieve this goal, students are expected to take practical action in real-world settings to address emerging challenges in regional industries. This calls for expertise in both data science and management, as well as the ability to apply them in practice.
Accordingly, the School aims to cultivate next-generation professionals who can acquire practical knowledge in data science and management, and utilize it for problem-solving, decision-making, and value creation.

Key Features of Education and Activities

1. Educational Program to Develop "Three Key Competencies"

To equip students with the skills needed for problem-solving, decision-making, and value creation, the School fosters the following three competencies. Students are expected to develop a multidisciplinary perspective and fully demonstrate their abilities in real-world contexts.

• Data Science Competency

The ability to extract valuable insights from data and analyze them effectively.

• Management Competency

The ability to identify issues and develop solutions from a management perspective.

• Social Implementation Competency

The ability to apply the above competencies to problem-solving, decision-making, and value creation, and put them into practice in real-world contexts.

2. Flexible Specialization Tracks Based on Students' Interests

From enrollment through the first semester of the second year, students study the fundamentals of both data science and management while exploring their future interests.
From the second semester of the second year, students select a specialization track to deepen their expertise. Students may also take courses from the non-selected track, allowing them to maintain a broad perspective across both fields.

• Data Science Track

This track focuses on acquiring knowledge and skills to extract valuable information from data by combining mathematical theory and statistics.

• Management Track/strong

This track focuses on understanding how organizations efficiently use resources such as people, assets, capital, and information to achieve growth.





3."Social Implementation Practicum" Using Real-World Data




This practicum course is designed to develop "social implementation competency" by integrating knowledge gained in mathematics and data science, management, and interdisciplinary and social implementation subjects up to the first semester of the third year. Students work in groups consisting of members from both tracks and visit partner companies and local governments. As part of Project-Based Learning (PBL), they analyze real-world data provided by these partners using statistical methods, machine learning, and AI technologies. The results are then shared with the data providers.
In addition, students participate as members of management or planning teams within partner organizations, gaining firsthand experience in real business and administrative settings.