Curriculum - Sophia University,Faculty of Science and Technology

Curriculum Features

The curriculum is structured on three core pillars. These include a cluster of applied chemistry and life-sciences courses that build knowledge in materials and biological sciences, a cluster of mechanical engineering, electrical and electronic engineering, and physics courses that develop system-oriented expertise, and a cluster of data science and digital technology courses that support both fields through digital transformation（DX）.

Data Science Cluster

Data Science Cluster comprises foundational and advanced courses in statistics, along with a suite of courses in machine learning/AI and their applications. In the statistics courses, the aim is to quantify uncertainty, distinguish correlation from causation, and build sound foundations for valid conclusions and decision-making. By contrast, machine learning/AI courses cultivate the ability to implement pattern discovery, prediction, and optimization, thereby enabling the automation and advancement of solutions to societal challenges.

Informatics Cluster

In this cluster, students learn not only core technologies that underpin the real-world deployment of digital systems—such as databases and information security—but also cutting-edge fields including quantum information and quantum computing. At the same time, they systematically master foundational subjects such as data structures and algorithms and discrete mathematics. Through this coursework, students develop the ability to design and operate secure, scalable information infrastructures and to lead the resolution of societal challenges and the creation of new value, while balancing reliability and efficiency.

Common Subject Cluster

In this cluster, students study Intellectual Property Rights, Law and Ethics in Science and Technology, and International Standardization. They cultivate the ability to appropriately protect and leverage research outputs and ideas; to assess the legal and ethical issues associated with data use and AI and ensure full compliance; and to understand international standards and engage in stakeholder coordination and rule‑making. These studies establish a practical foundation for promoting the trustworthy and equitable real‑world deployment of DX and GX.

Mathematical Science Cluster

In this cluster, students systematically acquire the mathematical foundations that underpin study in data science, digital and information technologies, applied chemistry, life sciences, physics, mechanical engineering, and electrical and electronic engineering. By mastering the fundamentals of linear algebra and calculus, they develop the ability to represent data and analyze change; through abstract linear algebra and multivariable calculus, they tackle the understanding of high-dimensional structures as well as the optimization and modeling of complex systems. This cluster cultivates the capacity to bridge theory and application.

Applied Chemistry and Life Sciences Cluster

In this cluster, students build data‑science‑grounded expertise in materials science and biotechnology through lectures and laboratory courses. Linking this with the digital skills cultivated in the first and second years, they further develop their research and development capabilities through a structured curriculum from the third year onward. By integrating chemistry and biology with data - a form of convergent knowledge - they contribute to the creation of new technologies in the environment, resources, and health fields. domains.

Mechanical Engineering, Electrical and Electronic Engineering, and Physics Cluster

In this cluster, students first acquire foundational knowledge in thermodynamics and electromagnetism, along with systems analysis (numerical analysis and optimization). Building on this, and grounded in data science, they progress from fundamental physics to energy management systems, cultivating the ability to design sustainable energy operations; and apply systems analysis to robotics and autonomous driving, developing strong capabilities in the integration of complex systems. In addition, through the study of geographic information systems (GIS), they practice evaluation and decision‑making based on real‑world data.

Curriculum Overview

This program aims to cultivate engineers who will help build a sustainable society by driving green
transformation (GX) through science and technology grounded in digital transformation.

First Year

Build a solid foundation for later study through introductory data science courses and DX courses including programming, along with fundamentals in mathematics, physics, chemistry, and biology.
Second Year

Broaden studies in data science and digital technologies, then focus on either applied chemistry and life-sciences or mechanical engineering, electrical and electronic engineering, and physics clusters as preparation for advanced coursework, while developing practical skills through laboratory work and internships.
Third Year

Students who have completed applied chemistry and life-sciences advance to materials and biological science clusters, while those who have completed mechanical engineering, electrical and electronic engineering, and physics progress to system‑oriented clusters to deepen expertise in their chosen field. Students also take advanced GX courses built on a DX foundation to strengthen their ability to contribute to new technologies.
Fourth Year

Through graduation research, establish practical research and development capability as an engineer or researcher and cultivate the ability to define and solve social problems. Further develop global leadership through clusters of courses on entrepreneurship, intellectual property, and international standardization.

All
Faculty of Science and
Technology Common Subject Group I
Faculty of Science and
Technology Common Subject GroupⅡ
Department Core Courses
Department Specialized Courses

Provides the foundational knowledge essential to studies in science and engineering—mathematics, physics, chemistry, biology, and related areas. As faculty‑wide required courses, they deepen understanding in a stepwise manner and build a solid foundation that enables a smooth progression to Science and Engineering Common Courses II, Department Core Courses, and Departmental specialized Courses. Functioning as a shared platform, this coursework fosters at an early stage the cross‑disciplinary perspective and learning orientation required in the field of Digital Green Technology. Offers broad study across applied chemistry, life sciences, mechanical engineering, and electrical and electronic engineering from the first through third years. Students develop logical analytical skills rooted in crossdisciplinary knowledge, insight for interpreting data, and the ability to apply environmental science and technology in society. These courses foster a comprehensive perspective and the capability needed to pursue digital green technologies. Develop practical implementation skills that every student in the Department of Digital Green Technology is expected to master, through programming courses as well as laboratory and practicum exercises. In addition, through entrepreneurship studies, they cultivate leadership that guides society through dialogue and collaboration. In the final‑year seminar and graduation research, students acquire the ability to pursue inquiries independently and solve problems. Deepen expertise in applied chemistry, life sciences, mechanical engineering, and electrical and electronic engineering, while also learning technologies that extend these fields on a foundation of data science. The areas are organized into Group A (Data Science & Informatics), Group B (Applied Chemistry & Life Sciences), and Group C (Mechanical Engineering & Electrical and Electronic Engineering). Building on the learning in Group A and, according to their interests, taking courses across the groups, students cultivate integrated, convergent‑knowledge‑based problem‑solving skills.

First Year: Build a solid foundation of science and engineering for later study

Second Year: Broaden studies in data science and digital technology as preparation for advaced coursework

Third Year: Deepen specialized knowledge including advanced GX coursed built on a DX foundation to strengthen ttheir abilities to contribute to new technologies

Fourth Year: Through graduation research, establish practical and development capability as an enigneer or a researcher and cultivate the ability to define and solve social problems

Department Core Courses: Statistical Learning
Programming (Python)

Faculty of Science and Technology Common Subject GroupⅡ

Mathematics A2 (Abstract Linear Space) Mathematics B2 (Multivariable Calculus)
Regression Analysis Bayesian Statistics
Machine Learning Artificial Intelligence
Data Structures and Algorithms
Quantum Computing Information Fluency (C Programming)

Chemical Analysis Inorganic and Organic Structural Chemistry Physical Chemistry Data Driven Environmental Science Introduction to Modeling of Natural Phenomena
Environmental Biochemistry
Bioinformatics

Electromagnetism Thermodynamics Materials Science Energy Conversion Industrial Dynamics Applied Mathematics Mathematical Optimization Fundamentals of System Analysis
Study Abroad Program (UC Davis, UNC Charlotte)

Compulsory Elective Courses: 12 credits・Elective Courses: 18 credits

Up to 4 credits from other English-taught
Courses of Faculty of Science and
Technology can be included

Department Specialized Courses: Group A (Data Science & Informatics)
Time Series Analysis Multivariate Analysis Non-Parametric Analysis Natural Language Processing
Theory and Practice of Deep Learning Data Visualization
Computer Vision: Theory and Practice

Database Discrete Mathematics Information Security Quantum Information Theory Introduction to Quantum Machine Learning

Group B (Applied Chemistry & Life Sciences)
Chemical Reactivity: Energy and Interactions Polymer Chemistry Inorganic Materials Organic Materials Theoretical Chemistry Chemical and Materials Informatics Molecular Science Organic and Natural Product Chemistry Computational Chemistry Applications of Databases in Chemical Data Science Biopolymers Biomaterials Biomass Biological Image Analysis Medicinal Informatics

Group C (Mechanical Engineering & Electrical and Electronic Engineering)
Introduction to Robotics Autonomous System Engineering
Introduction to Geographic Information System
Energy Management Systems Fundamentals of Network Security
Network Science Social Network Analysis
Intellectual Property Rights International Standardization Law and Ethics in Technology

Elective Courses: 30 credits
Credits from other English-taught Courses of Faculty of Science and Technology departments can be included

Faculty of Science and Technology Common Subject Group I: Mathematics A1 (Linear Algebra) Mathematics B1 (Basic Calculus) Mathematics C (Basic Statistics)

Basic Chemistry
Basic Biology
Basic Physics

Compulsory Courses: 12 credits

Department Core Courses: Introduction to Digital Green Technology

DIGITAL GREEN TECHNOLOGY EXPERIMENTS 1
PROJECT-BASED PRACTICUM
INTERNSHIP

ENTREPRENEURSHIP
DIGITAL GREEN TECHNOLOGY EXPERIMENTS 2

SEMINAR 1・2
GRADUATION RESEARCH 1・2

STUDIES IN CHRISTIAN HUMANISM
LIBERAL ARTS OF THE BODY
CRITICAL THINKING AND DISCUSSION
OVERVIEW OF DATA SCIENCE
THINKING ABOUT ISSUES, PERSPECTIVES AND POSITIONALITY
ACADEMIC WRITING1・2

General Studies（Elective Courses）

General Studies（COMPULSORY ELECTIVE COURSES）

General Studies　Compulsory Courses: 8 credits・Compulsory Elective Courses: 6 credits・Elective Courses: 12 credits
Language 　Compulsory Courses: 4 credits

Data Science Cluster
Informatics Cluster
Applied Chemistry and Life Sciences Cluster
Mechanical Engineering, Electrical and Electronic Engineering, and Physics Cluster
Mathematical Science Cluster
Common Subject Cluster
General Studies
Language
Others