For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| QAE5005 | Big Data Analysis and Machine Learning | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This course aims to cultivate the ability to understand various machine learning methods as a tool for analyzing big data and to use it for actual data analysis. To do this, we introduce various machine learning models and techniques and study the concepts and principles of the methods from a statistical point of view. In addition, various data analysis examples will cultivate the flexibility to select and flexibly apply machine learning methods to the given data and analysis objectives. It is also expected to lay the foundation for applying machine learning techniques to big data analysis and interpreting the results in the future. | |||||||||
| QAE5006 | Labor Economics: Quantitative Approach using Big Data | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This class aims to help students learn how to analyze various types of data including big data, focusing on labor market and education-related issues, and to equip them with the ability to use them in their practice. To this end, the class focuses on empirical analysis, focusing on labor market, income distribution, demographic change, and education, which are key research topics in labor economics. In addition, this class concentrates on the process of arranging, analyzing and interpreting various types of data including text data in a form that can be analyzed using the R program. | |||||||||
| QAE5007 | Data Science for Industrial Organization | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This class aims to introduce the impact of big data analysis on the analysis of economic policy and research in economics related to the interaction and market structure between firms and consumers, and to learn techniques for collecting and analyzing these data. In particular, by utilizing various types of big data such as public data that is open to the public and large-scale private data that can be accessed in real time across various industries, monopoly market, collusion, price discrimination, antitrust and competition laws, which are the main research topics of industrial organization theory. In addition, the class is expected to cultivate the ability to apply or evaluate actual economic policies and decision-making within companies by learning quantitative analysis techniques of corporate and consumer behavior and market competition. | |||||||||
| QAE5008 | Health Economics: Quantitative Approach using Big Data | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This class aims to help students learn how to analyze various types of data including big data and focus on health economic issues and to be able to use them in practical work. To this end, the course focuses on empirical analysis, focusing on related topics such as the demand and supply of medical care, the health insurance market, and the production and cost of health, which are the key research topics in the fields of health, welfare and insurance economics. In addition, this class focuses on the process of analyzing and interpreting various types of health data using the R program. | |||||||||
| QAE5009 | Big Data Analytics in Macroeconomics | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This class empirically analyzes macro issues such as GDP, consumption, unemployment rate, inflation rate, and exchange rate, using big data. To this end, we need to understand deeply the principles of statistical/measuring techniques that form the theoretical basis with an academic perspective, and our class focuses on how to interpret the results produced using the R program and how to apply them to reality.It also aims to cultivate the ability to apply these techniques to practical decision-making issues from a practical point of view. | |||||||||
| QAE5010 | Quantitative Finance | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This class covers the statistical and quantitative skills, which are needed to conduct quantitative analysis of assets in financial markets. We use a variety of cross-sectional and time series models to predict the risks and returns of financial assets, and also use informal methods to apply vast amounts of financial data to machine learning to predict stocks that exceed market benchmarks or create portfolios. We aim at being able to configure. The goal of this course is to provide students with the ability to understand the quantitative approach being taken in the real financial industry, to use their financial and non-financial data to solve investment decisions and to evaluate the results. This course is also an informatics area that focuses on investment decision-making using large amounts of financial data. | |||||||||
| QAE5011 | Machine Learning and Economic Forecasting | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This course aims to learn a variety of prediction methods and statistical evaluation methods between prediction models, from traditional time series techniques used for the prediction of macro and financial time series to recently used machine learning techniques. Prediction based on time series model setting, prediction using high-dimensional data through LASSO method, and prediction using machine learning method will be discussed. It also introduces forecasting issues such as Direct Forecasting, as well as the issues related to forecasting evaluation, such as forecasting error functions and methods of testing predictive excellence. | |||||||||
| QAE5012 | Law and Economics with Big Data | 3 | 6 | Major | Master/Doctor | - | No | ||
| In this course, the law economics theory,whish is composed of core concepts such as property right and transaction cost, is used to infer due diligence on 'finding a good law by analyzing various civil, administrative, and criminal laws. In particular, based on the large-capacity data that can be collected by smart devices, wireless communication technologies, and hyper-connections, the company will cultivate more accurate prediction or verification capabilities regarding how various laws and systems are actually executed and what end results will be achieved. | |||||||||
| QAE5013 | Applied Economics Seminar | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| The Applied Economics Seminar covers various cases based on data analysis and up-to-date analytical methodologies that draw a keen attention in the fields of micro-economics, macro-economics, and financial economics. We invite experts in charge of big data and machine learning related tasks from domestic and overseas leading companies and institutions to hear about the latest trends in the industry, and learn various analysis methodologies and recent cases that are used in the field. In addition, researchers who are teaching and conducting research by utilizing big data and machine learning techniques from academia will be invited to learn about the latest development direction and research methodology in the field of big data and machine learning. This seminar aims to help students get ideas for using big data and machine learning techniques in their respective fields of work as well as academic research in the future, and have the ability to use them in practice. | |||||||||
| QAE5014 | Seminar on Data Analysis | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This course aims to prepare students to begin empirical analysis in various topics of economics using advanced techniques of statistics, econometrics, or machine-learning. Students will have opportunities to present and discuss recent studies to learn state-of-the-art methodologies of data analysis. | |||||||||
| QAE5015 | Portfolio and Risk Management | 3 | 6 | Major | Master/Doctor | - | No | ||
| The purpose of this course is to introduce portfolio theory and risk management that are encountered in portfolio theory and practice. In modern portfolio theory, risk is an important part of investment decision making. In addition, understanding the risks is very important in making investment decisions. Portfolio and Risk Management is an iterative process, including setting investment objectives, allocating assets, evaluating portfolio performance and measuring risk, and it is made through the process of portfolio redistribution. This course focuses on asset allocation, performance evaluation and risk measurement. | |||||||||
| QAE5016 | Basics for Economic Data Analysis | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| Demand for experts in economic data analysis is increasing, and data analysis, interpretation, and programming skills are now establishing themselves as basic skills in various occupations as well as researchers. This course introduces statistical programs for data analysis and introduces methods of analyzing various types of data used in the field of economics. This course aims to help students have the analytical ability to conduct academic research on their own through this process. This course covers data preprocessing, web data collection, textual data analysis, and, most importantly, interpreting analysis results. In addition, this course introduces various visualization methods to convey the analysis results effectively. We also replicate recent academic and policy studies in economics and other social sciences. | |||||||||
| QAE5017 | Econometric Analysis:Theory and Practice | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course aims to provide students to learn advanced econometrics, a data analysis method in the field of economics, and to practice methodology using data. Lecture will cover basic econometric methods(LS, GMM, IV, MLE), basic time-series/forecasting(stationary ARMA, ARCH/GARCH, nonstationary process, VAR), panel data analysis, High Dimensional Data analysis, Survival Analysis, Discrete Choice. The course will go through lecture and practices with computer and statistical software. | |||||||||
| QAE5018 | International Field Study | 1 | 2 | Major | Master/Doctor | 1-4 | - | No | |
| This is an elective (1 credit) course during the winter (January-February) or summer (July-August) breaks in which students participate in lectures at prestigious universities abroad for approximately five days and explore major industrial sites in their field of interest in large or small groups. Participation in the field trip is recognized as class participation and credit can be earned. | |||||||||