Today, a large amount of various data is available, and how to utilize such data is important in business and society. Data science is the development and application of methods to extract useful information from such data. However, in order to use data science to solve various problems in modern business and society, it is not enough just to learn data science technology. Knowledge of social sciences such as economics, business administration, marketing, law, and sociology is also required. The appeal of this faculty and graduate school is that you can systematically study social science in addition to data science technology. On top of that, in business innovation courses, social problem-solving courses, PBL exercises, seminars, etc., you can combine social science and data science to learn practical methods to solve business and social problems.・This is the charm of the graduate school.

In terms of education, I will be in charge of finance-related classes such as "Finance and Data Science" and "Financial Market Data Analysis" in the undergraduate course, and "Frequency Asset Price Data Analysis" in the master's course. In "Finance and Data Science", lectures will be given on the basic theory and empirical analysis of finance. In "Financial Market Data Analysis", we will give a lecture on how to analyze how asset prices fluctuate on a daily basis using time series analysis, and practice using actual stock price data. In "Frequency Asset Price Data Analysis", lectures and exercises will be given on the analysis of high-frequency asset price data during the day. In terms of research, we develop a time series model that expresses volatility fluctuations using high-frequency asset price data during the day and apply it to financial risk management. . Until now, we have mainly used the Bayesian estimation method as a metric method, but we would like to incorporate machine learning in the future.