For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CHS2001 | New humanity Phono Sapiens created by Smartphone | 1 | 2 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| The humanity which have begun using smartphones are showing changes in consumer psychology, consumer behavior and market ecosystems due to rapidly changing lifestyles. This is Phono Sapiens, the new humanity is the hero of the revolution. The purpose of this course learn about the changes in business models due to the development of big data, artificial intelligence, and digital platforms by the change of consumption civilization. And it analyzes the development direction of continuously developing 5G, Internet of Things, robot, drone, autonomous vehicle, and smart factory. On this basis, companies present and understand new business innovation and directionality of change for new consumers called phono sapiens | |||||||||
| CHS2002 | Data Science and Social Analytics | 1 | 2 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| This course is intended to examine human behaviors and social phenomena through the lens of data science. Students also may learn online data collection and analysis in social media spaces. It deals with both theory and practice, but relative portion may change in each semester without prior notice. | |||||||||
| CHS2013 | The Convergence of Cognitive Neuroscience and Neurotechnology with Humanities and Social Sciences | 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, neuroscience-based cognitive computing, neuroergonomics, etc.), their applications 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) articulate the domains and contexts in which cognitive neuroscience and neurotechnology may be effective; 3) develop an ability to lay out the open questions and address challenges in cognitive neuroscience and neurotechnology research today; and 4) prepare them to be more knowledgeable and proficient professionals. | |||||||||
| CHS7003 | Artificial Intelligence Application | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| Cs231n, an open course at Stanford University, is one of the most popular open courses on image recognition and deep learning. This class uses the MOOC content which is cs231n of Stanford University with a flipped class way. This class requires basic undergraduate knowledge of mathematics (linear algebra, calculus, probability/statistics) and basic Python-based coding skills. The specific progress and activities of the class are as follows. 1) Listening to On-line Lectures (led by learners) 2) On-line lecture (English) Organize individual notes about what you listen to 3) On-line lecture (English) QnA discussion about what was listened to (learned by the learner) 4) QnA-based Instructor-led Off-line Lecture (Korean) Lecturer 5) Team Supplementary Presentation (Learner-led) For each topic, learn using the above mentioned steps from 1) to 5). The grades are absolute based on each activity, assignment, midterm exam and final project. Class contents are as follows. - Introduction Image Classification Loss Function & Optimization (Assignment # 1) - Introduction to Neural Networks - Convolutional Neural Networks (Assignment # 2) - Training Neural Networks - Deep Learning Hardware and Software - CNN Architectures-Recurrent Neural Networks (Assignment # 3) - Detection and Segmentation - Generative Models - Visualizing and Understanding - Deep Reinforcement Learning - Final Project. This class will cover the deep learning method related to image recognitio | |||||||||
| CHS7003 | Artificial Intelligence Application | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| Cs231n, an open course at Stanford University, is one of the most popular open courses on image recognition and deep learning. This class uses the MOOC content which is cs231n of Stanford University with a flipped class way. This class requires basic undergraduate knowledge of mathematics (linear algebra, calculus, probability/statistics) and basic Python-based coding skills. The specific progress and activities of the class are as follows. 1) Listening to On-line Lectures (led by learners) 2) On-line lecture (English) Organize individual notes about what you listen to 3) On-line lecture (English) QnA discussion about what was listened to (learned by the learner) 4) QnA-based Instructor-led Off-line Lecture (Korean) Lecturer 5) Team Supplementary Presentation (Learner-led) For each topic, learn using the above mentioned steps from 1) to 5). The grades are absolute based on each activity, assignment, midterm exam and final project. Class contents are as follows. - Introduction Image Classification Loss Function & Optimization (Assignment # 1) - Introduction to Neural Networks - Convolutional Neural Networks (Assignment # 2) - Training Neural Networks - Deep Learning Hardware and Software - CNN Architectures-Recurrent Neural Networks (Assignment # 3) - Detection and Segmentation - Generative Models - Visualizing and Understanding - Deep Reinforcement Learning - Final Project. This class will cover the deep learning method related to image recognitio | |||||||||
| CHS7004 | Thesis writing in humanities and social sciences using Python | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course is to write a thesis in humanities and social science field using Python. This course is for writing thesis using big data for research in the humanities and social sciences. Basically, students will learn how to write a thesis, and implement a program in Python as a research methodology for thesis. Students will learn how to write thesis using Python, which is the most suitable for processing humanities and social science related materials among programming languages and has excellent data visualization. Basic research methodology for thesis writing will be covered first as theoretical lectures. Methodology for selection of topics will be discussed also. Once a topic is selected, a lecture on how to organize related research will be conducted. In the next step, students learn how to write necessary content according to the research methodology. Then how to suggest further discussion along with how to organize bibliography to complete a theoretical approach. The basic Python grammar is covered for data analysis using Python, and the process for input data processing is conducted. After learning how to install and use the required Python package in each research field, the actual data processing will be practiced. To prepare for the joint research, learn how to use the jupyter notebook as the basic environment. Learn how to use matplolib for data visualization and how to use pandas for big data processing. | |||||||||
| CHS7004 | Thesis writing in humanities and social sciences using Python | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course is to write a thesis in humanities and social science field using Python. This course is for writing thesis using big data for research in the humanities and social sciences. Basically, students will learn how to write a thesis, and implement a program in Python as a research methodology for thesis. Students will learn how to write thesis using Python, which is the most suitable for processing humanities and social science related materials among programming languages and has excellent data visualization. Basic research methodology for thesis writing will be covered first as theoretical lectures. Methodology for selection of topics will be discussed also. Once a topic is selected, a lecture on how to organize related research will be conducted. In the next step, students learn how to write necessary content according to the research methodology. Then how to suggest further discussion along with how to organize bibliography to complete a theoretical approach. The basic Python grammar is covered for data analysis using Python, and the process for input data processing is conducted. After learning how to install and use the required Python package in each research field, the actual data processing will be practiced. To prepare for the joint research, learn how to use the jupyter notebook as the basic environment. Learn how to use matplolib for data visualization and how to use pandas for big data processing. | |||||||||
| CHS7007 | AI-Based Media Text Comprehension | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course aims to equip students with the ability to critically analyze and understand various forms of media texts—such as news, advertisements, films, and social media—through the use of artificial intelligence (AI). Students will learn techniques in natural language processing (NLP), including sentiment analysis, keyword extraction, and text summarization, as well as methods for analyzing visual content using AI models like CNNs and GANs. The course also addresses issues of trustworthiness and ethical concerns related to AI-generated content. Combining theoretical instruction with practical application, students will complete hands-on assignments and projects using Python-based AI tools such as GPT and Gemini AI. | |||||||||
| CHS7007 | AI-Based Media Text Comprehension | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course aims to equip students with the ability to critically analyze and understand various forms of media texts—such as news, advertisements, films, and social media—through the use of artificial intelligence (AI). Students will learn techniques in natural language processing (NLP), including sentiment analysis, keyword extraction, and text summarization, as well as methods for analyzing visual content using AI models like CNNs and GANs. The course also addresses issues of trustworthiness and ethical concerns related to AI-generated content. Combining theoretical instruction with practical application, students will complete hands-on assignments and projects using Python-based AI tools such as GPT and Gemini AI. | |||||||||
| CON4013 | Artificial Intelligence Data Analytics | 3 | 6 | Major | Bachelor/Master | Consumer Science | Korean | Yes | |
| This course covers advanced data analysis methodologies using modern artificial intelligence techniques, including machine learning and deep learning. Students will develop the ability to perform in-depth processing and analysis of data in various forms and scales, and derive meaningful insights. Through this course, students will acquire sophisticated analytical capabilities applicable to various research fields, including consumer studies, and cultivate problem-solving skills to propose solutions for real-world problems through hands-on programming exercises. | |||||||||
| CON4014 | Data Science for Causal Inference | 3 | 6 | Major | Bachelor/Master | Consumer Science | Korean | Yes | |
| This course covers advanced data analysis techniques for evaluating the causal effects of interventions designed to influence consumer behavior. Topics include the potential outcomes framework, causal analysis methods, model estimation and validation using data analysis tools, and real-world applications through replication studies. Students will gain an understanding of the causal inference in data-driven decision making and develop the skills to apply these concepts. | |||||||||
| CON4015 | Data-Based Quantitative Research Methods | 3 | 3 | Major | Bachelor/Master | Consumer Science | English | Yes | |
| Data-Based Quantitative Research Methods is a methodological course that introduces the core principles of quantitative research and the procedures of data-driven empirical analysis used across the social sciences. The course is designed to help students understand the full process through which quantitative research formulates research questions, organizes and analyzes data, interprets statistical results, and ultimately derives evidence-based conclusions. Students will learn essential concepts in quantitative inquiry, including research design, variable measurement, sampling strategies, and assessments of validity and reliability. Through hands-on work with Stata, they will conduct key stages of empirical analysis such as data cleaning, descriptive statistics, exploratory data analysis, and regression modeling. This practical engagement will enhance their applied research skills. By the end of the course, students will be able to recognize data structures and patterns, interpret analytical outputs, and use empirical evidence to explain social phenomena. The course focuses on building a strong conceptual understanding of quantitative research and developing students’ capacity to apply data effectively across a wide range of social science research contexts. | |||||||||
| COV7001 | Academic Writing and Research Ethics 1 | 1 | 2 | Major | Master/Doctor | SKKU Institute for Convergence | - | No | |
| 1) Learn the basic structure of academic paper writing, and obtain the ability to compose academic paper writing. 2) Learn the skills to express scientific data in English and to be able to sumit research paper in the international journals. 3) Learn research ethics in conducting science and writing academic papers. | |||||||||
| DAI5001 | Fundamentals of Artificial Intelligence | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Artificial Intelligence | - | No |
| Artificial intelligence is a field of research into information processing models that can mimic human intelligence and cognitive functions. As a fundamental problem of artificial intelligence, it deals with theories and fundamental computational problems on the methods of empirical exploration, reasoning, learning and knowledge expression. It deals with logic-based proof of theorem, game theory, intelligent agent, etc., learns the basic principles of neural network, evolutionary computation, and beigean network, and examines areas such as expert system, computer vision, natural language processing, data mining, information search and bioinformatics as examples of its application. | |||||||||
| EDU5086 | Analysis of Factors | 3 | 8 | Major | Master/Doctor | 1-4 | Education | Korean | Yes |
| The purpose of this course is to enable an investigator to properly utilize factor analysis as a research tool. Such utilization requires an understanding of when the differences in possible mathematical procedures may have a major impact upon the substantive conclusions and when the differences might not be relevant for a given research study. So, This course has stress on the mathematical foundations of factor analysis. | |||||||||