Imagine a society with an abundance of data science trained human resources who can create new value from the vast amount of data accumulated every day. A society in which data science professionals continuously challenge themselves to develop new solutions to every day social environment problems.

The foundation of the Graduate School of Data Science’s Data Science major lies in the necessity to solve the lack of awareness of the importance of knowledge in the field of conventional data analysis. With that, we make it our mission to provide an education centred on Project-based Learning (PBL). Students will learn the three fundamental data science skills of data analytics, data engineering, and social development, and will be challenged to implement their knowledge in solving real world problems.

Degree awarded Master of Data Science, Doctor of Data Science

Duration 2 Years (MDS)

Medium of instruction Japanese

Study in English Not available
Language requirements JLPT N2 or higher, IELTS/TOEFL-ITP/TOEIC IP

Non-degree program Not available

Course Structure



Master's Program

For the purpose to illustrating the structure of the curriculum, most of the following course names have been roughly translated from Japanese.

M1 Sem 1 M1 Sem 2 M2 Sem 1 M2 Sem 2
DS research guidance I-II (8 total in 1 or 2 years), Master's thesis (0)
PDS I (2) PDS II (2) PDS III (2) -
Advanced Statistics (2) Advanced Machine Learning (2) - -
Advanced Design Thinking (1) Advanced Data Management (2) - -
Applied ethics (1) - - -
Statistical science pathway
Advanced Lectures on Multivariate Statistical Analysis (2)
Optimization Basics and Advanced Lectures (2)		 Advanced Series of Time Series Data Analysis (2)
Advanced Lecture on Urban Environmental Data Analysis (2)		 - -
Experimental Design and Causal Inference (2)
Advanced Sample Survey (2)		 Other research subjects (2) -
Computer science pathway
Advanced Cloud Computing (2)
Advanced Computer Statistics (2)		 Special Lecture on Big Data Processing Infrastructure (2)
Advanced Structured Data (2)		 - -
Advanced Programming (2) Experiment and Simulation (2)
Advanced Data Visualization (2)		 - -
- Advanced Natural Language Processing (2) - -
Special lecture on data analytics, special lecture on data engineering, special lecture on data sign development (intensive lecture, etc.) (2)


Completion requirement: 30 credits

  • Special Research / Exercise: 14 credits PDS (required) 6 credits, seminar / master's thesis (required) 8 credits
  • Common subjects: 16 credits Lectures / Seminar (compulsory) 8 credits, Lectures / Seminar (optional) 8 credits or more





Doctoral Program

For the purpose to illustrating the structure of the curriculum, most of the following course names have been roughly translated from Japanese.

Special Research / Special Exercise Common subjects
DS Special Exercise I (2) DS Special Lecture I (2)
DS Special Exercise II (2) DS Special Lecture II (2)
DS Special Exercise III (2) DS Special Study I (2)
DS Special Research Guidance I (2) DS Special Study II (2)
DS Special Research Guidance II (2) DS Special Study III (2)
DS Special Research Guidance III (2) DS Special Study IV (2)
DS Special Research Guidance IV -


  • "DS Special Exercise" is a doctoral dissertation exercise
  • "DS Special Research Guidance" is a special research subject of doctoral dissertation.
  • "DS Special Lecture" is a joint lecture by all faculty members of the Graduate School of DS.
  • "DS Special Study" is a course offered by all faculty members of the Graduate School of DS.


Remedial subjects
For all subjects other than "PDS I, II, III" and "DS Research Guidance I-IV" (Master course subjects: 0)


Completion requirements: 20 credits

  • Special Research / Exercise: 14 credits (Seminar / Doctoral Dissertation (required) 14 credits)
  • Common subjects: 6 credits (lecture / practice (compulsory) 4 credits, lecture / practice (choice) 2 credits or more)


First year tuition



Admission fee

Yokohama residents and graduates of Yokohama City University: 141,000 JPY
Non-residents: 282,000 JPY

Tuition fee

Annual fee: 535,800 JPY

Other payments

Academic research fee: 2,000 JPY
Sponsorship fee: 30,000 JPY
 

Building on the 3 Foundational Skills of Data Science

A major feature of this department is that it eliminates the lack of awareness of the importance of knowledge in the field in conventional data analysis, and fosters data science skills through "Practical Data Science Exercises" centered on PBL from the first semester.
gsds_3 pillars
 

Acquiring theoretical knowledge and contributing to the development of society through PBL

 

Extensive guidance system in each specialized area




In order to nurture specialized data science skills, we have established a system that allows us to receive generous guidance from full-time faculty members assigned to specialized fields such as science, informatics, health sciences, pedagogy, and agriculture. In addition, the analytics, engineering, and social development skills required for data science research are organically entangled, allowing you to acquire true data science capabilities without closing into individual specialized fields.
 

Practical exercises in which everyone participates




While studying basic theory, students will focus on practical data science exercises. Students will learn to recognize and clarify the problems at the site through reflective thinking and observation, make hypotheses, draft research plans, verify the validity of the hypotheses, and make inferences based on them. Students can acquire data science skills through a series of processes of verifying the usefulness of theory through social implementation.
 

Exceptional and unique curriculum




The curriculum provides a well-balanced way of cultivating the three fundamental skills of data analytics, data engineering, and social development. In particular, the establishment of "Practical Data Science Exercises" rooted in problem-solving learning is a major feature.