About Department of Data Science for Business Innovation
In the Department of Data Science for Business Innovation, students are able to study various methods and techniques relating to data science for business and management. Our department has a comprehensive curriculum designed to equip students with the knowledge and skills they require to utilize data science in a variety of fields within the business world. These include statistical theories relating to data science and the information technology skills needed to implement them in a practical setting.
In the first year, students learn mathematics, statistical theory and computer science, which form the basis for their studies from then on. From their second year, they systematically extend their knowledge of data science and engineering through a variety of other courses. As well as studying advanced methods in machine learning, operations research, data analysis and database management, they also learn how to apply their skills in many different fields, including financial engineering, marketing research, operations management, quality control and Kansei engineering. In addition to this, students acquire further practical skills through participation in problem-based learning and experiments. In their senior year, all students carry out research for the writing of a graduation thesis. Following this, many also choose to continue with their studies, pursuing advanced research at graduate school.
In recent years, the terms “big data” and “data scientist” have become key terms throughout the business world. Our department plays a central role in preparing students to excel in whichever field they choose to enter.
Fields of Interests
- Actuary
- Applied Statistics
- Applied Optimization
- Data Analysis
- Financial Engineering
- Human Media Engineering
- Intelligent Informatics
- Intelligent Systems Engineering
- Kansei / Affective Engineering
- Marketing Science
- Machine Learning
- Natural Language Processing
- Operations Management
- Operations Research
- Probability Analysis
- Quality and Environment Management
- Reliability Engineering
- Soft Computing
- Statistical Science