AI & Data Science at TDU

AI deals with technologies that enable machines to learn and use that learning for recognition, decision-making, and action. Data science is a field that collects, organizes, and analyzes data to identify useful patterns and information. Because machine learning and data mining are relevant to both, there is no clear boundary between them. In addition to programming, mathematics such as probability and statistics provides an important foundation for studying either field. Tokyo Denki University offers a mathematics, data science, and AI education program certified at the Literacy Level by the Ministry of Education, Culture, Sports, Science and Technology. The designated course is listed as “Information Literacy (Introduction to Mathematics and Data Science)” for students who enrolled in or before the 2025 academic year and “Introduction to Mathematics and Data Science” for students enrolling in or after the 2026 academic year. Specialized study differs by department and academic division. In the Information System Design Division of the School of Science and Engineering at the Saitama Hatoyama Campus, students learn the foundations of informatics in their first year and select courses beginning in their second year. Laboratory materials for the 2027 academic year list research in areas such as artificial intelligence, machine learning, and data mining. The curriculum of the Department of Information System Engineering in the School of System Design and Technology at the Tokyo Senju Campus includes probability and statistics, Bayesian statistics, data science, and machine learning, and identifies data science and machine learning as research fields. When comparing options, look beyond the use of “AI” in a name and check how the foundations of mathematics and programming lead to the applications and research that interest you.

AI and data science are not entirely separate areas of study. Tokyo Denki University’s published curricula include artificial intelligence and machine learning among AI-related subjects, and probability and statistics, data analysis, and databases among data science-related subjects. Both involve mathematics, including calculus and linear algebra, as well as programming. Viewed by role, mathematics provides the foundation for understanding specialized subjects, while statistics is an entry point for evaluating and analyzing data. Programming is a means of implementing processing methods. Data handling also includes database design and searches using SQL. In information systems, students also study content needed to build systems, including networks, operating systems, and security. Rather than checking only whether a course has “AI” in its name, examine the sequence in which these subjects are arranged. It is also important to determine whether the curriculum is designed to allow students to learn from the foundations. The University has announced that, beginning in the 2026 academic year, it will introduce a common general education curriculum at both the Saitama Hatoyama and Tokyo Senju campuses, with mathematics classes organized by level of understanding and foundational data science taught through mathematics and information courses. However, courses and their classification as required or elective differ by department, academic division, and curriculum year. Check the course allocation table and syllabi for the academic year in which you plan to enroll.

In the School of Science and Engineering, foundational subjects such as mathematics, natural sciences, and information processing are offered primarily in the first year. The curriculum is also structured so that exercises, experiments, seminars, and similar activities are studied systematically according to the student’s year. Guidance on course selection and classes organized by proficiency level have also been introduced. In the Department of Information System Engineering, students begin studying programming immediately after enrollment. The University explicitly states that faculty members and graduate students provide guidance to beginners. Students first learn Java and then progress to languages such as C, R, and Python according to their purposes. Before enrollment, first review the high school fundamentals rather than specialized knowledge of AI. For the School of Science and Engineering, mathematics and English are particularly important. For the Department of Information System Engineering, students are expected to have a sufficient understanding of Mathematics I, II, A, B, and C, and it is considered desirable to have studied Mathematics III and either the full-level high school subject Physics or Chemistry, rather than the corresponding basic-level subject. If questions arise after enrollment, students can use the Learning Support Center at either the Saitama Hatoyama or Tokyo Senju Campus to ask questions, receive advice on study methods, and obtain individual instruction by appointment.

When comparing curricula, it is important not to judge them solely by whether they include a course with “AI” in its name. Check how students progress from foundations such as calculus, linear algebra, probability and statistics, algorithms, and programming to specialized subjects. For example, one curriculum is structured so that students learn probability and statistics as mathematical foundations and then apply them to data analysis using Python and other tools. In the information media field, courses include data structures, object-oriented programming, machine learning exercises, natural language processing, and mathematical optimization, with an emphasis on applications such as images, audio, and recommendation systems. Points to compare include how extensively mathematical theory is covered, whether there are practical exercises using software, and whether the application areas match your interests. Required and elective classifications, the years in which courses are offered, and whether they are lectures or exercises also differ by department and academic division, so check the course arrangements and syllabi for the academic year in which you plan to enroll.

Study related to AI and data science is not limited to lectures. Some courses begin with mathematics and statistics and connect them to exercises in programming and data analysis. For example, in “Probability and Statistics,” students deepen their understanding by analyzing data with Python, R, Matlab, and other tools in addition to working with mathematical formulas. In experiments and practical training, students work on assignments such as data analysis and database construction. Project courses in which teams of approximately five to ten students experience the process from planning through production are also listed. Other activities include machine learning exercises, project-based learning, comprehensive exercises, and graduation research. When reviewing a curriculum, look beyond course names and check how foundational subjects connect to exercises, projects, and graduation research. Class formats and enrollment requirements differ by department and academic division, so it is important to check the latest curriculum and syllabi.

When choosing a path in AI or data science, consider not only accuracy and implementation but also how the curriculum examines bias in data and decisions, fairness, effects on people and society, and the potential for misuse. These are general points to check when considering responsible AI, not facts about any particular department. At Tokyo Denki University, the curriculum policy of the School of System Design and Technology states that courses related to information ethics and engineering ethics are included. The Department of Information System Engineering also has a policy of considering the relationship between information technology and people and society. In addition, the 2025 curriculum materials for the information media field list courses dealing with psychology, ethics, law, and governance. However, these materials alone do not show the extent to which current classes address AI bias, fairness, and misuse, or whether those courses are required. When comparing prospective programs, check the latest syllabi for course content, the year in which each course is offered, and whether it is required or elective.

Compare academic paths not only by whether they are called “AI” or “data science,” but also by the questions you want to address. If you want to identify trends and regularities in data and evaluate and analyze them, your interests are closer to data science. If you want to explore systems that learn, recognize, and reason, your interests are closer to AI. However, the two overlap in areas such as machine learning, probability and statistics, and programming. Beginners should consider not whether they can use AI from the outset, but whether they can learn the foundations step by step. In the School of Science and Engineering at the Saitama Hatoyama Campus, students study specialized foundations and other subjects in their first year, then select a primary course and a secondary course in their second year. In the Department of Information System Engineering at the Tokyo Senju Campus, students study programming progressively before moving on to probability and statistics, databases, machine learning, networks, and other subjects. The University-wide general education curriculum beginning in the 2026 academic year also lists mathematics classes organized by level of understanding and foundational data science courses, but check the latest implementation status. Choosing AI early does not necessarily lead immediately to narrow specialization. The 2027 laboratory guide describes uses of AI in areas including image recognition and bioinformatics at Saitama Hatoyama, and robotics and information security at Tokyo Senju. The important distinction is whether you want to research AI itself or use it as a method for addressing problems in another field. Note that this guide is a future-dated resource showing the breadth of research themes and does not itself represent the current curriculum.