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.

  • [Spring] Computer Structures
  • [Spring] Intelligent Integrated Systems
  • [Spring] Highly-Reliable System Design
  • [Fall] High-Performance Computing
  • [Fall] Computer Architecture
  • [Fall] Foundations of Software Science
  • [Fall Intensive] Internet and Information Security

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.

  • [Spring] Machine Learning Basics*
  • [Spring] Physical Fluctuomatics
  • [Spring] Intelligent Systems Science (Odd-numbered year only)
  • [Spring] Intelligent Control Systems (Even-numbered year only)
  • [Fall] Natural Language Processing
  • [Fall] Statistical Modeling
  • [Fall] Cryptology
  • [Fall] Computer Vision
  • [Fall] Advanced Applied Data Analysis
  • [Fall] Mathematical Informatics
  • [Fall] Fundamental Artificial Intelligence
  • [Fall] Design and Analysis of Information Sciences (Odd-numbered year only)
  • [Spring Intensive] Information Technology Fundamental
  • [Fall Intensive] Computer Science Fundamentals
Courses by the Graduate School of Economics and Management
  • [Spring] Econometrics I
  • [Fall] Econometrics II

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.

  • [Spring] Information Ethics*
  • [Spring] Legal System in Information Society*
  • [Spring] Mathematical Urban Modeling
  • [Spring] System Control Engineering I
  • [Fall] Interdisciplinary Information Sciences*
  • [Fall] Applied Data Sciences
  • [Fall] Information Biology (Even-numbered year only)
  • [Fall] Applied Intelligence Software
  • [Fall] Spatial Economics
  • [Fall] Information Content
Courses by the Graduate School of Life Sciences
  • [Fall Intensive] Advanced Integrative Life Sciences I (Brain and Nervous System)
  • [Fall Intensive] Advanced Ecological Developmental Adaptability Life Sciences II (Ecological Dynamics)
  • [Spring/Fall Intensive (Spring English/Fall Japanese)] Advanced Molecular and Chemical Life Sciences II (Molecular and Network Genomics)

Practical training

  • [Spring Intensive] Data Science Programming Basics (1 credit・Optional)
  • [Spring Intensive] Data Engineering (1 credit・Compulsory)
  • [Spring Intensive] Data Science Training I (1 credit・Optional)
  • [Spring Intensive] Data Science Training II (1 credit・Compulsory)

Short-term Overseas Training

  • Short-term Overseas Training (1 credit・Optional)
    In this optional training, Master’s students are supported to stay at an 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 (10-16 credits・Compulsory)
    Please contact your the office of your department about the content of the seminar.

Doctoral program

Project training

  • Data Science 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 institution abroad and conduct a collaborative research.
    Training duration: Although a six-month period is recommended, students are required to complete a minimum of three months of training if this is not feasible.

Financial support

Advanced Seminars

  • Advanced Seminar I (1 credit・Compulsory)
    In principle, students are required to give a research presentation at a presentation session organized by GP-DS in the first year of the doctoral program.
  • Advanced Seminar II (1 credit・Compulsory)
    In principle, students are required to present their research progress at a presentation session organized by GP-DS in the second year of the doctoral program.

Doctor’s thesis and the seminar

  • Doctor’s thesis and the seminar (10-16 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.