Information technology supports today’s management science and engineering in various situations as a fundamental technology for e-commerce and business information systems, and as a calculation tool for data analysis and simulations. The Information Technology area provides various courses for learning from the theoretical basis of this fundamental technology to examples of its application in management science and engineering.
Course name | Course description | Target year |
---|---|---|
Seminar on Information Technology | In this course, introductory training on object-oriented programming and data base technology (RDB and SQL) computer simulation will be provided during the first and second 5 class sessions, respectively. | 2 – 4 |
Computer Science | In this course, students will learn the basics of data structures, algorithms, and computational complexity. They will also study some example cases of application on computer networks. | 2 – 4 |
Simulation | In this course, students will learn techniques to obtain unbiased data through the minimum possible experiments (experimental designs) and computational techniques for experiments on computers (computer simulations). | 2 – 4 |
Information Networks | This course will explain the basic configurations and forms of networks, as well as protocols and data transmission methods using actual application examples such as e-mail and WWW. It will also explain network security threats and countermeasures, cryptosystems and authentication methods, and key management systems. | 2 – 4 |
Data Analysis | In this course, students will learn the basic principles of statistics, and actually use various techniques for data analysis. They will also practice data analysis through specific programming coding. | 2 – 4 |
Machine Learning for Management | Machine learning methods useful in business data analysis will be widely picked up and taught, such as linear regression, logistic regression, principal component analysis, clustering methods, cross-validation, bootstrap, regularization, decision trees, support vector machines, and deep learning. | 2 – 4 |