Position：Professor
email：shu.yamada(at)keio.jp
Home Page：
https://lab.ae.keio.ac.jp/~yamada_lab/
Data science, Quality management, Design of experiments, Management system
The Yamada research group is currently developing the theory, concept, models, and methodology to collect and analyze data that help manage the organization and create values with stakeholders, including customers. It can be classified into data collection and analysis methodology, data analysis approaches by various models, and management of the organization for value creation.
Statistical data analysis, Applied statistics, Advanced course on total quality management, Advanced course on the application of experimental design
Data collection in a planned manner based on purpose is the first step in adopting data science strategies. Experiments are designed in the form of a series of statistical techniques to support data collection and analysis. The Yamada research group develops various experiment designs, including supersaturated design and space-filling design. In addition, suitable analysis methods such as inverse regression and applying the Gaussian process for computer simulations are adopted in the process.
To obtain the desired results, the Yamada research group develops methodologies and cases based on various types of data analysis. The data are collected in various processes, including planning, design, production and service provision, and customer satisfaction. The analysis methodology includes multivariate statistical analysis, machine learning, text mining, etc.
There is a need to evaluate the situation based on the data obtained. Such evaluation helps with processes such as quality management and environmental management, which are essential for managing the organization. Based on a quantitative approach, the Yamada research group focuses on the effectiveness of management system standard such as ISO 9001, 14001, etc. In addition, quality management in education is also investigated; analyzing data to interpret the situation forms the basis of such investigation.
Understanding fundamental theory as well as its application is crucial to be able to leverage statistical methods. Based on this philosophy, the courses deal with the theoretical as well as application-based acquisition of knowledge. In particular, statistical methods are derived from mathematical models such as the linear model with the sum assumption of distribution. In the application-based part of the course, exercises based on the implementation of the theories are conducted. The application of computer software is one such exercise.
