For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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. | |||||||||
| SIC5033 | Using big data to address social and cultural inequality | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| Thiscoursereviewsthesocialinnovationresearchinwhichmachinelearningtechniquesareusedasaprimaryempiricaltoolforanalysis.Topicsincludeequalityofopportunity,education,health,environment,criminaljustice,andpossiblyothersdependingonthecharacteristicsofclass.Inthecontextofthesetopics,thecourseprovidesanintroductiontobasicdataanalytictechniquesandmachinelearningmethods,includingregressionanalysis,quasi-experimentalmodeling,artificialneuralnetworks,andtree-basedmethods. | |||||||||
| SIC5034 | Hierarchical Linear Modeling | 3 | 6 | Major | Master/Doctor | 2-8 | Convergence for Social Innovation | English | Yes |
| The purpose of this course is to develop the skills necessary to identify an appropriate technique, estimate models, and interpret results for independent research and to critically evaluate contemporary social research using hierarchical linear modeling. Social research focuses on issues that examine the relationship between individuals and the social contexts in which they work, live, or learn. This involves multilevel research, which investigates individuals within groups. In multilevel research, the nature of the data structure is hierarchical. For example, in educational research, the data typically consists of schools and pupils within these schools. In this example, pupils are nested within schools. When analyzing multilevel data, we need special statistical skills and techniques, because single-level analysis of multilevel data brings about misleading standard errors and significance tests. The hiearchical linear modeling addresses this issue, accurately dealing with a hierarchical data set, often individuals within groups. This course will be applied in the sense that we will focus on estimating models and interpreting the results, rather than understanding in detail the mathematics behind the techniques. | |||||||||
| SIC5035 | Multivariate Regression Analysis | 3 | 6 | Major | Master/Doctor | 1-8 | Convergence for Social Innovation | Korean | Yes |
| Introduction to data analysis via linear models. Topics include basic assumptions of the linear model, methods for transforming data, estimation and interpretation of the classical linear model, derivations of the estimators of interest, and diagnostics of results and/or potential fixes for violations of assumptions. This course lays the foundations for more advanced statistical modeling techniques used in data science and academic research. | |||||||||
| SIC5038 | Longitudinal Categorical Data Analysis | 3 | 6 | Major | Master/Doctor | 1-8 | Convergence for Social Innovation | Korean | Yes |
| This course will cover the foundations of longitudinal categorical data. Upon successful completing of this course, students will be able to (a) understand the types of hypotheses and research questions for which categorical data analytical produces are used, (b) perform number of cross sectional and longitudinal analytical procedures including regression with binary, ordinal, and multinomial outcomes, survival analysis, (first- and second-order) growth curve modeling with categorical data, and (c) read and evaluate research articles regarding testing of for which cross-sectional and longitudinal categorical data analytcial procedures are used. The course topics are as follows: Review of basic regression model. Introduction to Logistic and Profit Regression. Introduction to Count Data. Introduction to Latent Growth Model. Latent Class (Transition) Model. Growth Mixture Model with categorical data. Introduction to Survival Analysis. | |||||||||
| SOC5061 | Elementary/Intermediate Statistics | 3 | 9 | Major | Master/Doctor | Sociology | Korean | Yes | |
| This class provides the Graduate-level, social-science majoring students with a variety of Elementary and Intermediate levels of statistics, besides Advanced statistics, which include descriptive and a variety of inferential statistics (e.g., z-test, t-test, χ2-test, F-test, Simple Regression, Multiple Regression, Logistic Regression, etc.). | |||||||||
| SOC5062 | Factor Analysis / Covariance Structure Analysis | 3 | 9 | Major | Master/Doctor | Sociology | - | No | |
| This class provides the Graduate-level, social-science majoring students with a variety of Advanced Statistics (besides Elementary & Intermediate Statistics), which includes, most importantly, Factor Analysis (EFA & CFA) and Covariance Structure Analysis. | |||||||||
| USS2001 | Research Methods in Social Sciences | 3 | 6 | Major | Bachelor | 1-4 | Social Sciences | English,Korean | Yes |
| Survey methods and techniques of empirical research in sociology will be introduced. Topics include the nature of scientific knowledge, principles and techniques of measurement, sample survey, experimental designs, and other research procedures and techniques. | |||||||||
| USS2002 | Principles of Statistical Analysis | 3 | 6 | Major | Bachelor | 1-4 | Social Sciences | Korean,English,Korean | Yes |
| Statistical methods are extensively used by social scientists in their research. This course is designed to provide students majoring in social sciences with basic concepts and principles of social statistics and with skills to analyze quantitative data. | |||||||||
| USS2003 | SOCIAL SCIENCE AND ARTIFICIAL INTELLIGENCE | 3 | 6 | Major | Bachelor | Social Sciences | Korean,Korean | Yes | |
| Artificial intelligence technology is expected to be a means of efficiently solving various problems in the field of social sciences through the analysis and learning of data. In order to effectively utilize the AI technology, it is necessary to understand the concept of state space, the concept of search (Search) algorithms, and to learn the basic theory of expression and reasoning of knowledge based on logic. In addition, through practicing various examples based on theories such as probabilistic reasoning, neural networks, deep learning, genetic algorithms, etc., which have recently been spotlighted, you will be able to acquire the basic ability to solve problems in the field of social sciences using artificial intelligence technology furtheron. | |||||||||
| USS3004 | Social Problems and Public Policy | 3 | 6 | Major | Bachelor | Social Sciences | Korean | Yes | |
| Social phenomena and events which we encounter everyday are closely related with social science concepts and theories from public administration, public policy and other disciplines. Theories, frameworks, models and concepts from public administration and other social science disciplines provide lens and perspectives in understanding, explaining social problems and developing solutions for them. The students should embody and apply new perspectives and insight in understanding and in suggesting solutions for public problems occurring in the contemporary. For this goal, this class focuses on providing cases of social problems, government's policymaking, public services, and analyzing the causes and processes of, and designing solutions for the cases. We will ponder over 1) what are public problems or issues 2) why these problems should be dealt by governments or public governance, not by market or private sector 3) what policies or tools should be applied in solving problems . Drawing on case analyses of applying theories and concepts on public issues, this class aims to help students to understand the social phenomena in depth, and to develop public policies for addressing them. | |||||||||
| USS3005 | Social Sciences Individual Research 1 | 2 | 4 | Major | Bachelor | Social Sciences | Korean | Yes | |
| This is an independent study course for students who have finished an excellent accomplishment of the course requirements and designed for giving credits which make an excellent record to the students for their research works. | |||||||||