COURSE REQUIREMENTS

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

GSIS Syllabus

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 (Spring 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.

Financial support

Advanced Seminars

  • Advanced Seminar I (1 credit, Compulsory)
  • 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.