Master's Program
Data Science Foundation
Students must take at least 3 courses from Data Science Foundation courses (take at least 1 course from each category Data Science I, Data Science II, and Data Science III). And it must include at least 1 course from the courses marked with * (star).
* Syllabus can be found on the following page
Data Science I
~Technology Innovation~
These courses are designed to develop students’ ability to invent new technologies by combining new sensing techniques and existing data and/or build systems of sensing, storage, communication, or computation.
- High-Performance Computing (Fall Semester)
- Computer Structures (Spring Semester)
- Foundations of Software Science (Fall Semester/Odd-numbered year only)
- Internet and Information Security (Fall Semester/Intensive course)
- Computer Architecture (Fall Semester)
Data Science II
~Data Analytics~
These courses are designed to develop students’ ability to extract necessary information from big data and plan to organize and perform big data analysis.
- Data Science Basic (*) (Fall Semester)
- Natural Language Processing (Fall Semester)
- Statistical Modeling (Fall Semester)
- Cryptology (Fall Semester)
- Computer Science Fundamentals (Fall Semester/Intensive course)
- Computer Vision (Fall Semester)
- Design and Analysis of Information Systems (Fall Semester/Odd-numbered year only)
- Physical Fluctuomatics (Spring Semester)
- Intelligent Systems Science (Spring Semester/Odd-numbered year only)
- Intelligent Control Systems (Spring Semester/Even-numbered year only)
- Information Technology Fundamental (Spring Semester/Intensive course)
- Econometrics I (Spring Semester)
- Econometrics II (Fall Semester)
Data Science III
~Problem Finding and Solving~
These courses are designed to develop students’ ability to find fundamental problems and formulate processes to solve them.
- Information Ethics (*) (Spring Semester)
- Information Biology (Fall Semester)
- Interdisciplinary Information Sciences (*) (Fall Semester)
- Legal System in Information Society (*) (Spring Semester)
- Mathematical Urban Modeling (Spring Semester)
- Applied Intelligence Software (Fall Semester)
- System Control Science (Spring Semester)
- Applied Data Sciences (Spring Semester)
- Spatial Economics (Fall Semester)
- Advanced Integrative Life Sciences I (Brain and Nervous System) (Fall Semester Intensive course)
- Advanced Ecological Developmental Adaptability Life Sciences II (Ecological Dynamics) (Fall Semester Intensive course)
- Advanced Molecular and Chemical Life Sciences II (Molecular and Network Genomics) (Spring Semester Intensive course)
Practical training
- Data Science Skill Up Exercise (1 credit / elective)
This course is designed to improve students’ skills to participate in Data Science Training Camp. - Data Science Training Camp I (1 credit, compulsory)
This course is designed to provide knowledge and methods to process and analyze big data. - Data Science Training Camp II (1 credit, compulsory)
This course is designed to provide opportunities to solve practical problems using skills obtained from Data Science Training Camp I.
Short-term Overseas Training
- Short-term Overseas Training (1 credit, optional)
In this optional training, Master’s students are supported to stay at an educational institution abroad to participate in a short-term overseas training (8-14 days). Please contact the office of your department about financial support for traveling expenses.
Master’s thesis and the seminar
- Master’s thesis and the seminar (8-16 credits, Compulsory)
Please contact your graduate school regarding the content of the training.
Supplementary course for English skills
- Practical Data Science English (Spring Semester)
- Practical Data Science English (Fall Semester)
Doctoral program
Project training
- Big Data Challenge (2 credits, Compulsory)
This course is designed to provide opportunities to use learned skills to deal with and analyze big data and lead a group to solve practical problems.
Overseas Training
- Data Science Special Training (3 credits, Compulsory)
Doctoral students must stay at an educational institution abroad for at least 6 months and conduct a collaborative research.
Advanced Seminars
- Advanced Seminar I (1 credit, Compulsory)
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Advanced Seminar II (1 credit, Compulsory)
Details will be announced by the GPDS office.
Doctor’s thesis and the seminar
- Doctor’s thesis and the seminar (8-16 credits, Compulsory)
Please contact the office of your department about the content of the seminar.
Note: Six credits of Data Sciences I, II, III are compulsory also for the students who participate in the GP-DS program after entering Doctor's course.