Graduate School of Data Science: Department of Data Science
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.
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.
OVERVIEW
- 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
STRUCTURE
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.
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"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)
Department of Data Science Faculty List
Click on the links below to view our Faculty List.