As the first step toward becoming a data scientist, students will actually collect, process, and analyze data. The basic data analysis techniques learned in the previous two years will be used in actual project practice. As realistic project tasks, students will discover knowledge that can be used for decision-making in the real world from very large scale purchasing data, noisy sensor data in daily life, and dialogue data that is difficult to analyze. Through practical training, students will learn how to set analysis goals, design data collection, obtain and input data, cleanse cluttered raw data, learn which analysis methods to use and when to use them, and experience a series of analysis steps. It is said that today we live in an “age of uncertainty” where we are at the mercy of vast amounts of data and cannot see the future. In this course, students can learn social research, statistics, text mining, and machine learning techniques, which are indispensable for surviving in the future society, in a practical manner.

In this class, students apply their management skills to formulate a business plan based on their own ideas as a culmination of the management-related subjects they have studied up to the second year. Excellent business plans may be entered in off-campus business plan contests. Students also learn practical management decision making by playing a simulation game of corporate management in teams. By learning a series of processes to explore needs, create ideas, co-create ideas, and conduct market research, and by learning how to give shape to these ideas, students will develop the ability to envision future businesses.

In the Intelligent Information Systems Development II class, students create artificial intelligence systems by combining the knowledge of artificial intelligence and machine learning they have learned by the second year with the programming techniques of information systems development. What to create using machine learning is up to each individual, and students will come up with their own themes and implement their own systems, such as natural language processing, speech processing, image processing, data processing, game systems, and market forecasting. During the practical training, students hone the various skills required of system developers, including not only programming, but also development schedule management, document writing, and presentation methods to convey the functionality and appeal of the software. In fact, students are able to experience the full range of training and evaluation required for artificial intelligence system development, including surveying existing technologies, system design, development using programming languages, and machine learning training and evaluation.