24 monthsProgramme duration
10,869 USDTuition Fee/year
Programme overview
Main Subject
Data Science and Artificial Intelligence
Degree
Other
Study Level
Masters
Study Mode
On Campus
The Graduate Degree Program of Applied Data Sciences aims to nurture practitioners who possess both academic expertise and the practical skill to apply and deploy it in the modern society with “bridging theory and practice” as a key phrase. The three Graduate Schools of Economics, Science and Technology and Environmental Studies at Sophia University are collaborating to develop human resources who can respond to rapid changes in society and solve problems using their data science knowledge. Under the theme of applied data science, the three Graduate Schools have established an organized and systemized cross-divisional degree program. The program is designed to train professionals who will contribute to solving problems in the coming data-driven society and pioneer a new world.
Curriculum
Academic faculty members in this program will provide guidance mainly from an academic perspective, while practical faculty members will provide guidance with an emphasis on practical skills for students to become active in the modern society after completing the Master’s Program. Students will acquire the necessary knowledge to become practitioners who can lead our data-driven society immediately through practical training courses centered on exercises, elective courses that broadly cover the basic and applications of data science, and collaborative courses that broaden and deepen their academic knowledge in data science and business administration. In addition to the faculty members, we also offer a number of courses taught by lecturers dispatched from companies. In the second year, internships courses are offered in actual work sites to deepen the students’ understanding.
Programme overview
Main Subject
Data Science and Artificial Intelligence
Degree
Other
Study Level
Masters
Study Mode
On Campus
The Graduate Degree Program of Applied Data Sciences aims to nurture practitioners who possess both academic expertise and the practical skill to apply and deploy it in the modern society with “bridging theory and practice” as a key phrase. The three Graduate Schools of Economics, Science and Technology and Environmental Studies at Sophia University are collaborating to develop human resources who can respond to rapid changes in society and solve problems using their data science knowledge. Under the theme of applied data science, the three Graduate Schools have established an organized and systemized cross-divisional degree program. The program is designed to train professionals who will contribute to solving problems in the coming data-driven society and pioneer a new world.
Curriculum
Academic faculty members in this program will provide guidance mainly from an academic perspective, while practical faculty members will provide guidance with an emphasis on practical skills for students to become active in the modern society after completing the Master’s Program. Students will acquire the necessary knowledge to become practitioners who can lead our data-driven society immediately through practical training courses centered on exercises, elective courses that broadly cover the basic and applications of data science, and collaborative courses that broaden and deepen their academic knowledge in data science and business administration. In addition to the faculty members, we also offer a number of courses taught by lecturers dispatched from companies. In the second year, internships courses are offered in actual work sites to deepen the students’ understanding.
Admission Requirements
- Students who can actively utilize their expertise in data science to address diverse challenges in modern society and contribute to the development of human society through applied data science.
- Students who are motivated to use their knowledge and skills in applied data science to pioneer and develop new fields in society that utilize practical data.
Tuition fees
Domestic
International
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