For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CHS7004 | Thesis writing in humanities and social sciences using Python | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| 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. | |||||||||
| CHS7008 | Strategic Decision-Making with AI | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | Korean | Yes | |
| This course equips students with the essential skills to make strategic decisions using generative AI. As technology rapidly transforms every industry, the ability to critically evaluate AI-generated information and integrate it into decision-making has become a core competency. This course addresses that need by preparing students to work with AI not just as a tool, but as a collaborative partner. Students will gain a foundational understanding of AI systems, particularly the workings of generative AI models. They will learn to analyze unstructured outputs such as text and predictions, assess data reliability, and identify biases. Ethical and responsible use of AI is emphasized to build both technical and social awareness. Going beyond traditional data analysis, the course explores human-AI collaboration in solving real-world problems. Students will examine decision-making case studies across industries including business, healthcare, finance, and policy. Through team-based projects, they will apply AI tools to complex strategic challenges. The curriculum reflects global academic standards, drawing inspiration from courses at MIT, Stanford, and Carnegie Mellon. By aligning with leading institutions, the course helps students build globally competitive capabilities. Ultimately, this course develops future-ready professionals who combine AI fluency with critical thinking and leadership. It is an essential foundation for navigating a world where AI shapes strategy. | |||||||||
| 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 | Korean | Yes | |
| 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. | |||||||||
| GSP5241 | Spatial Modeling for Social Science Research | 3 | 6 | Major | Master/Doctor | Public Administration | - | No | |
| Spatial dependence is prevalent in the society. Geographically proximate individuals, groups, and localities have similar characteristics and behave similarly via spillover effect. Also, the local governments that are closely located often pursue similar policy directions as they are affected by each other. This class aims to explore how to apply spatial dependence in social science research. The class topics include the concept and origin of spatial dependence, global/local spatial analysis, visualization of spatial dependence, and various spatial regression models such as spatial lag, spatial error, geographically weighted regression, and spatial Durbin. | |||||||||
| MCJ5122 | Network Analysis for Communication Research | 3 | 6 | Major | Master/Doctor | 1-4 | Media and Communication | - | No |
| The primary goal of this course is to explore theories and concepts of network analysis in communication perspectives. Moreover, this course will practice with data and network analysis programs to show how network analysis can be used to understand various communication phenomena and problems. | |||||||||
| MCJ5128 | Meta-Analysis | 3 | 6 | Major | Master/Doctor | 2-4 | Media and Communication | - | No |
| Meta-analysis refers to the quantitative analysis of study outcomes. Meta-analysis consists of a collection of techniques that attempt to analyze and integrate effect sizes (indices of the association between an independent variable and a dependent variable) that accrue from research studies. This course deals with the process of performing meta-analysis and how to interpret analysis results. Students will have an opportunity to conduct meta-analysis on research topics of interest using meta-analysis software and to write a research paper based on the analysis results. | |||||||||
| PSD5113 | Future Social Risks and Political Theory | 3 | 6 | Major | Master/Doctor | Political Science | - | No | |
| This course diescusses the new developments in future, the risks they bring about and proper strategies for coping with them. The topics include the climate changes, natural disastors, demographic changes, new discoveries and developments in sciences and technologies and their impacts on our society. The goal of this course is to deliberate on how to react to those changes and challenges properly, which may determine the tufure of individual societies as well humankind. | |||||||||
| PSY4002 | Computational Cognitive Sciences | 3 | 6 | Major | Bachelor/Master | 2-3 | - | No | |
| This course is an introduction to computational approaches to human cognition in an advanced level. This course covers computational models of knowledge representation, learning and reasoning founded on artificial intelligence and Bayesian framework. Students mainly learn materials from lectures, in-class activities and homework. This course is designed for graduate students and highly motivated 4th year undergraduate students, who are well equipped with statistics and computer programming. | |||||||||
| PSY4003 | Neuroimaging Research Methods | 3 | 6 | Major | Bachelor/Master | 1-2 | - | No | |
| This course focuses on learning about non-invasive neuroimaging methods used in cognitive neuroscience research. Students will learn the principles of functional (fMRI, EEG) and structural (dMRI, sMRI) neuroimaging methods, as well as data analyis procedures and how these methods can be utilized to further our understanding of traditional psychology research topics. Course is designed incorporate a combination of lecture and hands-on data analysis sessions for graduate students and highly motivated undergraduate students who had completed [Cognitive Psychology] and [Statistical Analysis in Psychology]. Students are encouraged to take [Brain and Mind] or [Neuroscience of Human Behavior] before signing up. | |||||||||
| PSY4004 | Developmental Psychopathology | 3 | 6 | Major | Bachelor/Master | - | No | ||
| Developmental Psychopathology studies psychopathology based on developmental perspective with a goal of clarifying developmental pathways of various psychopathologies. Such research can provide critical knowledge on how psychopathologies development and change across the lifespan that contribute to effective preventive strategies. This course is a seminar course that involves heavy weekly article readings, presentation, and disucssion. The course is designed for graduate students and undergraduate students who have taken various basic and applied courses in psychology including [Abnormal Psychology] and [Clinical Psychology]. | |||||||||
| PSY4006 | Advanced Industrial Psychology | 3 | 6 | Major | Bachelor/Master | 1-4 | Korean | Yes | |
| This course covers various contents of industrial psychology including job analysis, employee selection, and performance appraisal in an intermediate level. Students will also practice these techniques in and outside classroom. The class will consist of lecture, discussion, and project/presentations. This class is for undergraduate and graduate students who have completed industrial psychology. | |||||||||
| PSY4007 | Multivariate analysis and statistical learning | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course covers principles and practice of multivariate data analysis in psychology and related fields. Applications of multiple regression, logistic regression, principal component analysis, factor analysis, cluster analysis and other multivariate techniques for psychological research will be illustrated. In addition, supervised learning and unsupervised learning will be discussed in relation to the multivariate techniques. This course is designed for graduate students or 3rd- or 4th-year undergraduate students in psychology. | |||||||||