Future-class data science

About Data Science

With the invention and development of information technology, we can now obtain big data in various fields such as natural sciences, economics, and medicine. Data science is the process of extracting important information hidden within this data.
Data science is the "reading, writing, and arithmetic" of the digital age, and today, all students, regardless of their field of study (humanities or sciences), are required to systematically acquire the fundamental knowledge and skills to properly understand and utilize mathematics, data science, and artificial intelligence (AI).
The Ministry of Education, Culture, Sports, Science and Technology has implemented the "Mathematics, Data Science, and AI Education Program Accreditation System" to promote these types of education, and our university has been accredited for both the literacy level and the applied foundation level programs.

Future University Hakodate - Mathematics, Data Science, and AI Education Program

The Future University Hakodate Mathematics, Data Science, and AI Education Program is certified by the Ministry of Education, Culture, Sports, Science and Technology's "Mathematics, Data Science, and AI Education Program (Literacy Level)" (Certification Period:June 2027 March, 2027 Until), "Mathematics, Data Science, and AI Education Program (Applied Basic Level)" (Accreditation Period:June 2028 March, 2028 It is certified (until [date]).

Let's open the door to new possibilities with data science unique to Future University.

Today, there is a great need for individuals who can extract new meaning, value, patterns, and relationships from vast amounts of data. As a university that has specialized in information science since its founding and possesses a wealth of research examples related to data science, we are committed to cultivating individuals who can excel as data scientists in the real world.
Here are some of the distinctive features of data science at Future University.

Learn from a group of experts in data science-related fields.

Data science requires a wide range of expertise, from knowledge of analysis and statistics in information processing and mathematics, to skills in aggregating and visualizing data in the optimal format, and skills in machine learning and statistical modeling to uncover hidden patterns in data. Our university has all the experts in these areas.

Systematic data science education tailored to the specialization of each course.

Our university's curriculum begins with "Introduction to Data Science" in the first year, and after students are assigned to a course in their second year, data science education tailored to the specialization of that course unfolds. For example, in the Information Systems Course and the Advanced ICT Course, students learn not only analysis but also how to implement it as a system and how to make it useful to society, while in the Information Design Course, students can learn about fieldwork-related topics such as collecting data in the real world. In the Complex Systems Course, students learn techniques for scientifically dealing with unpredictable social phenomena using mathematical modeling methods, and in the Intelligent Systems Course, students learn how to utilize artificial intelligence, which is inextricably linked to data science, in data analysis.
Thus, regardless of which course you choose at our university, our curriculum is designed to allow you to systematically learn data science.

Numerous research examples related to data science.

A distinctive feature of the research conducted at Future University is the large number of themes deeply related to data science. Learning data science based on cutting-edge research that contributes to the local community is not merely theoretical. A major strength of our university is the opportunity to learn, through research examples, how data science interacts with society and contributes to people's well-being.

Research case

Experience the learning of data science

This video presents a re-edited version of educational content and lectures about learning data science.
Explore the growing demand for learning data science.

About our unique certificate program for all students and working professionals

As a university specializing in information science, we have conducted cutting-edge research related to data analysis and processing, including machine learning, deep learning, applied mathematics, and information engineering, and we have an environment in place to learn data science knowledge and its applications. Furthermore, with the aim of acquiring these advanced data science skills, we offer a unique certificate program, the "Data Science Open Program (DSOP)," for all students and working professionals.

Certificate Program "Data Science Open Program (DSOP)"

Curriculum example

Introduction to Data Science: The Foundations of Data Science

Using a programming language suitable for statistical analysis (R), students will acquire fundamental data science concepts through assignments and exercises, and will also gain an understanding of more advanced topics.

Probability theory and statistics, data analysis

This course will provide you with a foundation in mathematical representation and statistical methods for probabilistically fluctuating phenomena. Furthermore, you will gain an understanding of the fundamental concepts of estimation and statistics necessary for the foundation of information theory.

Signal Analysis: Theory and Practice of Data Science

You will acquire fundamental concepts of signal processing and image processing used in various fields such as mechanical engineering, electronics engineering, economics, biological sciences, and astrophysics.

Cutting-edge fields of machine learning and data analysis

Machine learning is a technique that uses data to train machines, discover underlying patterns, and predict and analyze the future based on that data. This course will provide you with a foundation in machine learning.

Artificial intelligence aims for more advanced information processing.

You will acquire fundamental concepts of methods and algorithms for building intelligent and flexible problem-solving systems (artificial intelligence).