For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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. | |||||||||
| CHS7002 | Machine Learning and Deep Learning | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course covers the basic machine learning algorithms and practices. The algorithms in the lectures include linear classification, linear regression, decision trees, support vector machines, multilayer perceptrons, and convolutional neural networks, and related python pratices are also provided. It is expected for students to have basic knowledge on calculus, linear algebra, probability and statistics, and python literacy. | |||||||||
| CHS7002 | Machine Learning and Deep Learning | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course covers the basic machine learning algorithms and practices. The algorithms in the lectures include linear classification, linear regression, decision trees, support vector machines, multilayer perceptrons, and convolutional neural networks, and related python pratices are also provided. It is expected for students to have basic knowledge on calculus, linear algebra, probability and statistics, and python literacy. | |||||||||
| CON3032 | Consumer Big Data Analysis | 3 | 6 | Major | Bachelor | 2-4 | Consumer Science | Korean | Yes |
| Introduction to machine learning for consumer science. This course covers foundational concepts in machine learning such as overfitting, cross validation, and bias-variance tradeoff, and application of machine learning algorithms to consumer big data analysis. | |||||||||
| 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. | |||||||||
| DAI5001 | Fundamentals of Artificial Intelligence | 3 | 6 | Major | Master/Doctor | 1-8 | Applied Artificial Intelligence | Korean | Yes |
| 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. | |||||||||
| ERP4001 | Creative Group Study | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course cultivates and supports research partnerships between our undergraduates and faculty. It offers the chance to work on cutting edge research—whether you join established research projects or pursue your own ideas. Undergraduates participate in each phase of standard research activity: developing research plans, writing proposals, conducting research, analyzing data and presenting research results in oral and written form. Projects can last for an entire semester, and many continue for a year or more. SKKU students use their CGS(Creative Group Study) experiences to become familiar with the faculty, learn about potential majors, and investigate areas of interest. They gain practical skills and knowledge they eventually apply to careers after graduation or as graduate students. | |||||||||
| GFP5017 | Population Research and Social Policy | 3 | 6 | Major | Master/Doctor | 1-4 | Future Policy Studies | Korean | Yes |
| This course provides a substantive overview of the field of demography -- the study of human populations, past, present and future. We focus on trends and causes and consequences of change in the three the basic components of population change: mortality, fertility, migration. We will also cover a few sub-fields outside the big three, including population & environment. By the end of the semester, students will have been introduced to the major substantive issues, debates, and methods that characterize the field. The course is non-technical but assumes graduate-level literacy in statistics and quantitative reasoning. We will focus on understanding general trends in global population, the inter-related nature of fertility, mortality, migration, and age structure, and how the demographic explanations of social phenomena are critical for understanding political, economic, and cultural changes. This course will help students dive into demographic research as well as subfields like family, aging, stratification, and health disparities in other courses specifically dedicated to these topics. | |||||||||
| GFP5019 | Mental Health and Future Society | 3 | 6 | Major | Master/Doctor | 1-4 | Future Policy Studies | - | No |
| This course is designed to help students obtain comprehensive and critical knowledge of the relationship between mental health and society. Readings and lectures deal with a variety of theories and empirical research of sociology of mental health. In particular, this course concerns stress process theory, labeling theory, and social construction of mental illness. Moreover, it examines a range of topics related to sociology of mental health including social stratification, gender, race, identity, family, work, and social relationship. This course will place much emphasis on the link between stress and mental health. In addition, it will underscore the ways that social inequality manifests itself in the area of mental health, focusing on social patterns, processes, and outcomes, as well as the relevance of social contexts for contributing to disparities in mental health. Further, this course will examine the ways that we can reduce mental health inequality in society. The main contents in this course are as follows: 1. Sociological theories on sociology of mental health 2. Sociological analyses about the relationship between stress and mental health 3. Social factors, processes, and contexts for mental health disparities in society 4. Sociological insights into reducing mental health inequality in society 5. Sociological knowledge about future society and mental health issues | |||||||||
| GSP5241 | Spatial Modeling for Social Science Research | 3 | 6 | Major | Master/Doctor | Public Administration | Korean | Yes | |
| 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. | |||||||||
| ISS3290 | Introduction to Big Data Analysis | 3 | 6 | Major | Bachelor | English | Yes | ||
| Understand the genesis of Big Data Systems • Understand practical knowledge of Big Data Analysis using Hive, Pig, Sqoop • Provide the student with a detailed understanding of effective behavioral and technical techniques in Cloud Computing on Big Data • Demonstrate knowledge of Big Data in industry and its Architecture • Learn data analysis, modeling and visualization in Big Data systems | |||||||||
| KID2001 | Developmental Psychology | 3 | 6 | Major | Bachelor | 2-3 | Child Psychology and Education | Korean | Yes |
| Systematic overview of the current knowledge concerning children's cognitive, language, social and emotional development. | |||||||||
| MCJ2005 | Cybercomm. Theory | 3 | 6 | Major | Bachelor | 2-3 | Media & Communication | - | No |
| This course covers communication situations of the cyber space on the basis of internet networks. It mainly focuses on similarities and differences between the old communication situations - that is, newspaper, radio, television, etc. - and the new communication situations. It also discusses about new communication models explaining the cyber communication. | |||||||||
| 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. | |||||||||