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Department of Data Science Seminar
Learn data science use cases
In the Spring Semester [Orientation Seminar], students will learn concrete examples of solving social issues using data science. There are various themes such as how to use data science at work and the fusion of business administration and data. We also prepare basic mathematics subjects according to the level of proficiency to prepare for learning from the second year onwards.
List of seminar themes (3rd year)
| research theme | Third-year student presentation topics | Faculty name |
|---|---|---|
| research theme will be centered on educational measurement, and will cover a wide range of topics, from creating questionnaires and tests to data analysis. Specifically, students will design and develop tests and questionnaires to obtain reliable data, collect data, and statistically analyze the collected data. Students will learn a method called Item Response Theory (IRT), and develop practical skills through data processing and visualization using R and other tools. |
1. Creating sequence problems using generative AI 2. Information Technology Exam Preparation Using Generative AI 3. Analysis of answer data generated by generative AI using item response theory 4. Visualizing topics and trends using natural language processing 5. The effects of sleep deprivation |
Special Duty Professor Kiyoka Arai |
| research theme is the consideration and exploration of quantum information science (a science that combines quantum mechanics and information science). Is the performance of quantum computers really superior to that of supercomputers? Is it really impossible to break quantum cryptography? What is teleported in quantum teleportation? We will learn the basics so that we can accurately explain and answer such questions. |
An introduction to quantum mechanics for data science 1. New applications of quantum mechanics 2. Basic concepts of quantum mechanics 3. The world of state vectors 4. Towards qubits |
YOSHIDA Ritsu Professor |
| research topic is game informatics. Students will acquire applied machine learning techniques through analyzing game situations using AI and analyzing win/loss data using statistical methods. |
1. Expressing the danger of situations that are not reflected in the evaluation value of shogi AI 2. Behavioral analysis aimed at feature imitation and cheating detection in action games 3. Automated exploration competition for dungeon games with wall-breaking rules 4. Skill prediction based on fighting game behavior statistics 5. Experimental Laboratory Work of prediction methods to horse racing |
HIRAOKA Kazuyuki Professor |
| research field is Library (Library Administration) and information science. Students will independently collect, process, and analyze various information available on the web. Through this, they will learn how to handle data and analytical techniques. Although the instructor targets Library (Library Administration), students can work on any topic of their interest, not just Library (Library Administration), in their graduation research. | 1. What kind of business can bring you big profits? 2. Color analysis 3. Relationship between GDP and carbon dioxide emissions 4. Search behavior trend analysis of generative AI boom | Professor AGATA Teru Lecturer Tomoya Igarashi |
| research themes will be the following three: Music: By understanding music mathematically, we can analyze the reference structure, hierarchical tree structure, and musical scale shifts within a piece of music. 《Logic》What AI lacks these days is the ability to reason logically. We visualize the reasoning in AI's head through logic puzzles. Language: Why are only humans able to speak language? We will consider how language works by observing grammar and various linguistic phenomena. | 1. Structural analysis of illogical questions in driver's license tests 2. Analysis of logic puzzles 3. Music analysis using grammatical theory 4. Is Mahjong a game of luck? 5. The evolution of music | TOJO Satoshi Professor |
| Using data science to analyze the evolution of music and predict its future. What kind of music will be popular in the future? We are conducting research to predict the music of the future by making full use of data science and analysis. | 1. Analysis of brain waves when listening to chords 2. The historical background seen from changes in pop song lyrics 3. VR Batting Practice System 4. Development of an IoT device for authenticating student ID cards using NFC 5. AI test takers | HORI Gen Professor |
| research theme is to combine physics and business administration to create a new theory of data science. Students are expanding the concept of energy and working to elucidate the mechanisms of psychological energy, such as "oshi" and "oshi-katsu." | 1. Visualizing local energy 2. Visualizing the emotional energy of customer engagement 3. Baseball and Psychology 4. Considerations on the application of feedback bi-optimization models: Transformers, digital twins, and price/wage cycles 5. Considerations regarding "oshi" and motivation | ISHIZUKA Takao Associate Professor |
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<Updated on January 26, 2026>
Department of Data Science