As the first step toward becoming a data scientist, students collect, process, and analyze real-world data, utilizing basic data analysis techniques acquired over the previous two years for their projects. Through realistic project tasks, students identify insights relevant to real-world decision-making based on data sets such as high-volume purchasing data, everyday sensor data containing significant levels of noise, and dialogue data that is typically difficult to analyze. Through practical training, students learn how to set analysis goals, design data collection, obtain and input data, perform data cleansing on raw data, identify which analysis methods to use and when to use them, and learn the sequence of analysis steps. It is sometimes said that today we live in an age of uncertainty, at the mercy of vast amounts of data without a clear view of our future. Data Analytics II equips students with practical skills in areas such as social research, statistics, text mining and machine learning, all of which are indispensable for surviving in the world of the future.

In this course, students apply their management skills to formulate a business plan based on their own ideas, as a culmination of the management subjects they have studied up to the second year. The best plans may be deemed suitable for submission to external business plan contests. Students also learn practical management decision-making by playing simulation games of corporate management in teams. By studying the processes involved in identifying needs, creating ideas, pursuing collaboration, and conducting market research, students develop the ability to realize future business ideas.

In the Intelligent Information Systems Development II course, students create AI systems by combining their understanding of AI and machine learning acquired through to second year with information systems development programming techniques. Students are free to create whatever they like using machine learning, and can come up with their own ideas and implementations that could include elements of natural language processing, speech processing, image processing, data processing, game systems, and/or market forecasting. Practical training is used to equip students with the many skills required of system developers, including programming as well as scheduling, documentation, and presentation techniques designed to convey the functions and benefits of the software. Students are exposed to the full range of training and evaluation experiences involved in AI development, such as investigating existing technologies, system design, programming languages, and machine learning training and evaluation.