For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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. | |||||||||
| ISS3234 | Introduction to Social Problems | 3 | 6 | Major | Bachelor | 1-4 | English | Yes | |
| This course discusses various social problems in society, examines factors associated with these problems, and explores possible solutions. The course are organized into three main parts: 1) understand social problems from multiple perspectives, 2) brainstorm possible solutions to address social problems, 3) explore possible ways to conduct a research project. In order to achieve these leaning objectives, the course will meet daily as a seminar, along with class activities in which each member will have opportunities for contributing to the purpose of each session through prepared, active participation. | |||||||||
| ISS3286 | The Foundations of Entrepreneurship | 3 | 6 | Major | Bachelor | English | Yes | ||
| This class is an introductory course intended to provide students with a solid foundation in terms of the vital role played by entrepreneurs and entrepreneurship in the 21st century global economy. Entrepreneurship is approached as a way of thinking and acting, as an attitude and a behavior. Our emphasis is on entrepreneurship as a manageable process that can be applied in virtually any organizational setting. Our principal focus will be on the creation of new ventures, the ways that they come into being, and factors associated with their success. | |||||||||
| MCJ5122 | Network Analysis for Communication Research | 3 | 6 | Major | Master/Doctor | 1-4 | Media and Communication | Korean | Yes |
| 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 | Korean | Yes | |
| 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. | |||||||||
| PSD5120 | Data Science and Study in Political Science) | 3 | 6 | Major | Master/Doctor | 1-4 | Political Science | - | No |
| The scientific study of political science has been one of the most noted fields that so-called `big data revolution’ and a rapid development in computational approaches have changed the way we understand our world. This course aims at introducing fundamental techniques of data science from a social science perspective, with a focus on political science. In this course, we will discuss a set of cutting-edge methods related to data science, including text analysis/mining, network analysis, machine learning, information extraction methods and data visualization techniques. We will then shift to an in-depth discussion on how to apply them to various research topics in politics such as political behavior, the roles of media and interest groups, and more. Students will also learn basic data analysis tools using R programming language or Python through problem sets. | |||||||||
| PSY2004 | Modern Social Psychology | 3 | 6 | Major | Bachelor | 2-3 | Psychology | English | Yes |
| Social psychology studies how people think about, relate, and influence one another. The major topics of the course include social cognition, social relations, and social influence. This course aims to enhance students’ understanding of basic concepts and theories in social psychology. To this end, the course will introduce representative studies directly relevant to important real-world issues, such as self, social judgment, person perception, attitudes, interpersonal relations, and stereotypes. | |||||||||
| PSY4007 | Multivariate analysis and statistical learning | 3 | 6 | Major | Bachelor/Master | Psychology | - | 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. | |||||||||
| PSY5136 | Structural Equation Modeling and Related Methods | 3 | 6 | Major | Master/Doctor | 1-4 | Psychology | - | No |
| This course is an introduction to structural equation modeling(SEM) which is quite broad and deep as a multivariate method. Students are required to understand the basic theory of SEM and to analyze multivariate data with hypotheses of linear structural relations. Also they are trained to be able to understand published articles demonstrating use of SEM as the major analytic method. | |||||||||
| PSY5188 | Structural Equation Modeling | 3 | 6 | Major | Master/Doctor | Psychology | Korean | Yes | |
| This course covers fundamentals of latent variable modeling, path analysis, and structural equation modeling, combining theoretical and practical perspectives. The course is designed to provide details of structural equation modeling, from the statistical underpinnings to how to conduct various types of structural equation analyses. | |||||||||
| SIC5020 | Longitudinal Data Analysis | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| This course covers empirical frameworks for drawing causal inferences from longitudinal data. Topics include longitudinal study design, exploring longitudinal data, random effects and fixed effects models; and quasi-experimental research design such as diff-in-diffs regression, propensity score matching, and regression discontinuity design. | |||||||||
| SIC5021 | Social Big Data Analysis | 3 | 9 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course aims to provide the students with a knowledge and skill about how to collect, save and analyze online text data. Specifically it seeks to help students scrap and crawl text data via online news sites, blogs, and SNS and analyze the data using unsupervised machine learning techniques. It focuses on how to use beginners or intermediate levels of natural learning process (NLP) techniques and how to visualize the corpus. The analyzes center around probabilistic topi models in different levels, ranging from LDA, to DTM and ETM. This course is designed to help students apply the techniques obtained to the data the students themselves crawl and write a research note that could potentially be submitted for journal publication. | |||||||||
| SIC5022 | Predictive Modeling using Regression Analysis | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| This course offers an introduction to predictive analytics and statistical learning using regression techniques. Students will be exposed to technical aspects of regression analysis, model selection, regularization, and data pre-processing, and learn how to use a programmable software in estimating and validating predictive models. This course prepares students for a more advanced course in machine learning. | |||||||||
| SIC5028 | Machine Learning with Python | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course aims that students implement machine learning algorithms with Python programming. In the beginning of this course, students will learn the basics about Python programming. In the latter part, students will implement various machine learning algorithms such as supervised and unsupervised learning with Python so that they could exactly understand the algorithms. | |||||||||