Yonsei University

The course critically evaluates rock music's musical content and contemporary cultural and social roles; ideally, the course serves to develop your general intellectual capacities of the music industry from the 1950s to 1960s (the so-called "rock and roll" era, arguably the most turbulent yet important period in popular music history). It's NOT a music course, per se, but we are listening to a lot of music as we consider the effects of recorded sound on popular culture. Thus, this is the quintessential "media and culture" course. We study the origin and growth of the recording industry and music business, consider the impact new technology had (and continues to have) on the development of popular music and examine the mutual influence between rock music and other media (film, television, radio, etc.). Following a loose chronology, we begin with an introduction to listening and some musical fundamentals, gradually developing a vocabulary with which to discuss and experience selected works from the history of rock. We trace the evolution of specific musical styles and investigate issues related to culture, performance, technology, and reception. Reading assignments introduce the distinct musical styles, performers, and works that comprise each genre and a certain time period. They also cover the relationship of rock music to American and global popular culture, historical representation, and authenticity.

This course provides a basis for handling and analyzing business data. This course also gives a chance to learn R, which is becoming a default analytics platform. This course presents students with real datasets and gives opportunities of ‘learning by doing’ through hands-on experience. Specifically, we study basic concepts in business analytics, and techniques and skills related to data exploration, data utilities, conducting statistical tests, data mining, and causal inference modeling. Topics include web data crawling and analysis, big data, regression models, panel model, classification model, causality model, instrumental variable model, and matching model.

This course is a broad introduction to computer vision for data science. Topics include various low-level image processing methods and high-level vision tasks like image classification and object detection, with modern approaches based on deep learning. Student learn computer vision algorithms and implement them in python. Students should be proficient in python programming with numpy for assignments. Other python libraries, such as opencv (cv2), scipy, and matplotlib will be used, but students do not have to be proficient with them.

The goal of this course is in introducing popular skills for analyzing economic data. We attempt to achieve this goal by getting familiar with the well-known econometric analyses and linking this to the knowledge on the numerical outputs generated by standard statistical packages. In attaining this goal, our interests will be focused more on cross-sectional data and their slight extensions. There are two reasons for this focus. First, analysis of cross-sectional data is a building bloc for the analysis of many other data sets. Second, the analysis of cross-sectional data is easier than analyzing other data sets as they do not involve too much complication that comes from the variation assumptions. Eventually, by these, studying cross-sectional data becomes a good starting point for achieving the specified objectives, even though their applicability is not so limited. After completing this course, students are expected to be able to conduct the following: Understanding the implicit assumptions behind economic data analysis; Interpreting the numerical outputs generated by standard statistical packages.

Prerequisite: Mathematics for economics and statistics; Recommended: Mathematical statistics.

This course examines the literature of wisdom, both ancient and modern, and looks at how reading literature can deepen, enrich, and improve one's life in modern society.

This course aims to provide basic mathematical concepts that have been widely used in economics. Topics include feasibility (Farkas Lemma), convex sets, linear programming, and non-linear programming. Economic applications are discussed throughout the course in order to illustrate how mathematics is used in economic theory.

Prerequisite: Mathematics for Economics I, Microeconomics

Digitalization has significantly impacted modern society, especially the media industry. It has changed the way we deliver messages and led the change of media users, who are now both audience and creator. Digitalization has also catalyzed the prevalence and importance of data. Specifically, in marketing communication, information about audiences is abundant and various. This course explores the new concept of brand communication in the current marketing and media environment from theoretical and practical perspectives, and provides students with diverse applications of experience to brand marketing discipline.