For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| AIO2001 | Introduction to Business | 3 | 6 | Major | Bachelor | 1-4 | Artificial Intelligence for Operation | Korean | Yes |
| 1. Course Overview: This course serves as an initial step into the fundamental principles and concepts of business management. Through this course, students gain a profound understanding of management and corporate activities within the modern business environment. Additionally, the course emphasizes the significance of ethical considerations in business, aligned with future management perspectives. 2. Learning Objectives: ● Acquire foundational knowledge of major areas and specializations in business management. ● Establish awareness of various management activities and theories. ● Gain deep insights into future management strategies and their ethical implications. 3. Main Content: ● Basic Principles of Management: Introducing fundamental concepts, principles, and the scope of management activities. ● Financial Management: Providing a foundational understanding of corporate financing, investment decisions, and asset management. ● Marketing: Learning the basic concepts of product, price, promotion, and distribution, along with consumer behavior and market research. ● HR/Organization: Exploring core theories and practical methods related to human resource management, organizational structure and culture, leadership, and teamwork. ● Accounting: Introducing the basic principles and practices of accounting, including financial information provision and interpretation, budgeting, and cost analysis. ● Production Management: Studying the key concepts and strategies | |||||||||
| AIO2002 | Innovation and Social Responsibility | 3 | 6 | Major | Bachelor | 1-4 | Artificial Intelligence for Operation | Korean | Yes |
| 1. Course Overview: This course comprehends the importance of ethical considerations amidst the socio-economic environment of the 21st century due to the advancements in technology such as artificial intelligence (AI), and deeply explores the essence of entrepreneurship from the perspectives of innovation and social responsibility. 2. Learning Objectives: ● Encourages students to discover and recognize their inherent potential as entrepreneurs. ● Understands the impact and significance of entrepreneurship with an emphasis on ethical considerations and social implications. ● Learns the entire process of building and growing an ethics-centered enterprise. ● Discusses in-depth the innovation strategies and risk acceptance in the AI industry as future entrepreneurs. 3. Main Content: ● The Essence of Entrepreneurship: Explores the meaning and significance of entrepreneurship in the modern socio-economic environment, emphasizing the necessary ethical foundation in today's world. ● The Entrepreneur and Ethics: Examines the mindset, actions, and attitudes of an entrepreneur, focusing on ethical decision-making and social responsibility. ● The Birth and Growth of Ventures: Detailed exploration of the journey from business inception to success, emphasizing ethical challenges and solutions. ● Understanding the AI Industry: Identifies the main trends and dynamics of the AI industry. l | |||||||||
| AIO2003 | Accounting and Business Decision Making | 3 | 6 | Major | Bachelor | 1-4 | Artificial Intelligence for Operation | Korean | Yes |
| Accounting is considered the language of business because it plays a role in summarizing a company's activities according to a certain system and reporting information necessary for decision-making. The purpose of this course is to introduce accounting-related fundamental concepts in a way that makes it as easy as possible to understand the business operations of a company. To achieve this goal, the lectures will specifically focus on the following content: · Understanding accounting information, financial statements, and accounting standards. · The relationship between managerial activities and accounting information. · Cost accounting and managerial decision-making accounting. · Utilization of financial statements and accounting transparency. These topics aim to facilitate a comprehensive understanding of a company's financial and managerial activities, enabling students to utilize accounting information effectively for decision-making purposes. | |||||||||
| AIO2004 | Statistics for AI | 3 | 6 | Major | Bachelor | 2-3 | Artificial Intelligence for Operation | Korean | Yes |
| 1. Course Overview: This course covers the basic concepts of statistical methods widely used in the field of business administration. After learning the concepts of probability distributions and the principles of statistical inference, students will study descriptive statistics for summarizing given data, regression analysis for examining relationships among variables, and smoothing and forecasting for time series data. Through this course, students will lay the foundation for AI-based data analysis, including deep learning. 2. Learning Objectives: ● Cultivate statistical thinking to logically approach and solve business problems. ● Understand and apply the statistical principles that underpin AI and machine learning techniques. ● Analyze and interpret complex business data to derive practical insights. ● Develop the ability to make data-driven decisions using statistical methodologies. | |||||||||
| AIO2005 | Business Data Management and Analysis | 3 | 6 | Major | Bachelor | 2-3 | Artificial Intelligence for Operation | Korean | Yes |
| 1. Course Overview: This course aims to teach methods for data collection, organization, analysis, and visualization using spreadsheets and applying these skills to business decision-making. Students will gain an understanding of the fundamental principles of data management and analysis, developing the ability to solve business problems. They will acquire practical skills to support data-driven decision-making in various fields such as accounting, finance, and marketing. The course emphasizes the development of data utilization skills required in corporate environments through a combination of theoretical knowledge and hands-on practice. 2. Learning Objectives: ● To understand the fundamental principles of data collection, organization, analysis, and visualization, and apply these to solving business problems. ● To enhance practical skills by practicing analysis and visualization techniques required for data-driven decision-making using spreadsheets. ● To develop the ability to effectively utilize data to support decision-making in various business domains, including accounting, finance, and marketing. ● To systematically cultivate data management and utilization skills required in data-centric business environments through a combination of theory and practice. | |||||||||
| AIO2007 | Human-Centered Digital Innovation | 3 | 6 | Major | Bachelor | 2-3 | Artificial Intelligence for Operation | Korean | Yes |
| 1. Course Overview: This course integrates fundamental concepts and theories of human motivation, communication, leadership, decision-making, organizational culture, and organizational development with AI-based digital innovation. Students will understand basic theories of human behavior and learn how to effectively implement digital innovation within organizations. Additionally, the course will analyze the impact of AI technology on organizational change and innovation, fostering the leadership and decision-making skills necessary in the era of digital transformation. 2. Learning Objectives: ● Understand and apply fundamental concepts and theories of human motivation, communication, leadership, decision-making, organizational culture, and organizational development. ● Analyze the impact of AI-based digital innovation on organizations and develop effective digital innovation strategies based on this analysis. ● Cultivate the leadership and decision-making skills necessary to lead human-centered organizational innovation in the era of digital transformation. | |||||||||
| AIO3001 | Big Data Visual Analytics | 3 | 6 | Major | Bachelor | 1-4 | Artificial Intelligence for Operation | - | No |
| 1. Course Overview: This course focuses on the visual analysis of data through data visualization techniques and information design to convey complex statistical analysis results in a form understandable even to non-experts. Students will acquire the skills to design and effectively analyze business-applicable data stories. 2. Learning Objectives: ● Understand key components and concepts about cognitive science and information visualization. ● Process and analyze data to extract meaningful information and visualize it graphically with design principles. ● Effectively analyze information using a variety of visualization techniques. 3. Main Content: ● Data Management and Visualization Fundamentals: Learning the basic concepts of data management, understanding human cognition, and using Tableau for visual analysis of data and dashboard construction. ● Advanced Data Visualization and Analysis Techniques: Deepening skills in visualizing complex geospatial data, analyzing temporal distributions and correlations among diverse advanced data analysis techniques. ● Storytelling and Conveyance of Analysis Results: Developing strategies to turn analysis results into understandable stories and effectively formatting and sharing them. | |||||||||
| AIO3004 | Introduction to MIS | 3 | 6 | Major | Bachelor | 2-4 | Artificial Intelligence for Operation | Korean | Yes |
| Management Information Systems (MIS) is a field of study that examines how organizations utilize information technologies to achieve business objectives and gain competitive advantage. This course introduces the fundamental concepts and roles of information systems from a business–technology integration perspective and explores how IT contributes to strategy development, operational efficiency, decision support, and customer experience innovation in real-world business environments. Key topics include the core components of information systems (hardware, software, data, networks, and people), systems development approaches, databases, cloud computing, and digital transformation technologies. The course also covers emerging technologies such as AI, big data, blockchain, and the Internet of Things, focusing on their managerial and strategic implications through case-based learning. Students will develop skills in analyzing enterprise information systems, understanding security and ethical issues, and evaluating digital business models. This course provides foundational knowledge essential for advanced studies in areas such as data analytics, digital strategy, and IT-enabled service innovation. Upon completion, students will gain a solid understanding of the interaction between information technology and business, along with fundamental analytical capabilities and innovative thinking required in the evolving digital era. | |||||||||
| AIO3006 | Data Science for HRD | 3 | 6 | Major | Bachelor | 3-4 | Artificial Intelligence for Operation | - | No |
| This course systematically covers the theory and practice of data-driven human resource management, specifically ‘People Analytics’. It aims to move beyond traditional methods reliant on intuition and experience, fostering evidence-based decision-making capabilities that enhance organizational performance through the use of data and statistical methodologies. Students will study analytical modules organized around the seven core HR domains: talent acquisition, performance management, compensation systems, employee engagement, turnover management, organizational culture, and diversity management. Through case studies (Case Reports) using real corporate data, participants define and solve specific on-the-job problems such as ‘department-specific talent selection’, ‘analysis of high-performer characteristics’, ‘identifying resignation causes using survival analysis’, and ‘analyzing differences in organizational culture perception’. Throughout this process, students will directly perform data preprocessing, exploratory data analysis (EDA), statistical hypothesis testing, and predictive modeling using data analysis tools like R programming. This will cultivate analytical thinking skills that integrate HR domain knowledge with data science techniques, enabling immediate practical application. | |||||||||
| AIO3008 | Data modeling and SQL | 3 | 6 | Major | Bachelor | 3-4 | Artificial Intelligence for Operation | Korean | Yes |
| In today’s data-driven business environment, organizations must effectively manage and utilize data to support decision-making and gain competitive advantage. This course aims to provide students with a strong foundation in data modeling and SQL (Structured Query Language), focusing on relational database systems widely adopted in practice. Students will learn fundamental concepts of database systems, including data structures, schema design, conceptual/logical/physical data modeling, and normalization. Practical exercises using MySQL will allow students to apply SQL techniques such as table creation, data retrieval and manipulation, JOINs, subqueries, and aggregation functions to real-world datasets. By completing this course, students will gain essential skills to design appropriate data models for business requirements, understand data relationships, and perform accurate data queries and analysis. This foundational knowledge will prepare students for advanced courses in database systems, data analytics, big data technologies, and AI applications. | |||||||||
| AIO3009 | Data-Driven Decision Making | 3 | 6 | Major | Bachelor | 3-4 | Artificial Intelligence for Operation | Korean | Yes |
| Business Analytics is a foundational course aimed at equipping students with the ability to leverage organizational data to generate insights and inform managerial decision-making. In this course, students will learn fundamental concepts of statistics and data analysis, and practice analytical techniques such as data exploration, summarization, visualization, group comparisons, regression modeling, and causal inference, using real business datasets. Students will also develop the mindset to interpret analytical results and connect them to business problem solving or opportunity identification. The course begins by introducing the objectives and processes of data analysis, followed by hands-on work with actual datasets using R or a comparable statistical software: data preparation, exploration, and visualization. Next, students study descriptive statistics, hypothesis testing, group comparison methods, regression models, panel data techniques, and causal inference methods—all within a business analytics context. Finally, students engage in case-based discussions and report writing to apply analytical findings to business situations. | |||||||||
| CHS2003 | Robust System Design with Big Data Analytics and Artificial Intelligence | 2 | 4 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| In this course, the fundamental theories and methodologies on big-data analytics and artificial intelligence (AI) algorithms for prognostics and health management (PHM) of engineering systems are mainly covered. More specifically, the reliability analysis, sensor-based big-data collection, signal processing, statistical feature extraction and selection, and AI-based modeling are studied, and the hands-on practices are also carried out. In addition, various case examples are introduced to study the robust engineering system design using the big-data analytics and AI algorithms. | |||||||||
| CHS2008 | The Fourth Industrial Revolution and Start-up Business | 1 | 2 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| The fourth industrial Revolution is regarded as a key driving force to lead the new national growth method and changes the industrial structure. Therefore, major advanced economies are already proactively focusing on creating new business models in the fourth industrial revolution. On the other hand, the korea response system to the fourth industry and human resource development performance are considered insufficient. This subject is to aware of the necessity of Startiup a business in the era of the Fourth Industrial Revolution for lower-grade students at universities and to explain the fourth industrial revolution technology. Based on this background knowledge, students will learn business model development theory, startup team building, and how to draw up a business plan. In particular, this subject will secure successful start-up cases or related videos to encourage students fun and eventually cultivate basic skills to start Business. | |||||||||
| CHS2015 | AI-based Neuroscience and Neurotechnology | 3 | 6 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| This course will introduce fundamentals of how human brain works and the state-of-the-art of neuroscience research. This course will cover the convergence of cognitive neuroscience and neurotechnology with humanities and social sciences (e.g., brain-computer interface, neuromarketing, neurolinguistics, neuroergonomics, etc.), AI applications to advance neuroscience/engineering, and future directions through class discussions. This course aims for students to ① understand the literature in the fields of cognitive neuroscience and neurotechnology based on the understanding of humanities and social sciences; ② understand the state-of-the-art of AI and its applications to advance neuroscience; ③ articulate the domains and contexts in which cognitive neuroscience and neurotechnology may be effective; ④ develop an ability to lay out the open questions and address challenges in cognitive neuroscience and neurotechnology research today;and ⑤ prepare themselves to be more knowledgeable and proficient professionals. | |||||||||
| CHS2019 | Information and Communication Technology Based on Quantum Mechanics | 1 | 2 | Major | Bachelor | Challenge Semester | - | No | |
| In the first half, basic physics lectures such as electron and photon, particle and wave duality, quantum superposition, entanglement, uncertainty principle, quantum tunneling effect, and Schrödinger equation, Maxwell's wave equation, and basic mathematics lectures such as Hilbert space, Bloch sphere, and bra and ket vector, which are essential for understanding qubits and quantum superposition, are fundamentally educated for understanding the information and communication technologies based on quantum mechanics. In the second half, current five types of qubit generation methods based on mechanical conservation using inductor and capacitor, quantum gates, quantum circuits, their quantum computer applications, sensitivity increased quantum sensors, and basic principles and current technologies of innovative quantum cryptography and quantum teleportation are taught. | |||||||||