For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| DSC2001 | Introduction to Data Science | 3 | 6 | Major | Bachelor | 2-3 | Data Science | English | Yes |
| This course will survey the foundational topics in data science such as data manipulation, statistical data analysis, machine learning, communication through data visualization, and working with big data. This course is designed primarily for those students without computing background. The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will give you the opportunity to sample and apply the basic techniques of data science. | |||||||||
| DSC2004 | Data Science and Python | 3 | 6 | Major | Bachelor | Data Science | English,Korean | Yes | |
| In this course, students acquire basic understanding and skills of python scripting tool that is being widely used for data analysis. Specifically, students learn how to use a variety of python libraries useful for data analysis and visualization. | |||||||||
| DSC2005 | Data Science and R | 3 | 6 | Major | Bachelor | Data Science | English | Yes | |
| This course introduces R, a basic for data analysis. R is a language for statistical computing and graphics including data manipulation and graphical display, and provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, clustering, and etc.). This course focuses on understanding statistical concepts and practices of R. | |||||||||
| DSC2006 | Data Science and Linguistics | 3 | 6 | Major | Bachelor | 2-3 | Data Science | - | No |
| This course introduces students to the semiotics and linguistics, systematics of knowledge organization, and text analysis. This course also covers the practical understanding of indexing dictionary of search engine. Throughout the course, the students will be examining a number of ways in which vocabulary is data but well controled mental system. Students also will get a good grasp of linguistic principles and understand more about how languages work in information system. | |||||||||
| DSC2008 | Mathematics 1 for Data Science | 3 | 6 | Major | Bachelor | Data Science | Korean | Yes | |
| The primary goal of this course is to learn core concepts and theories in calculus. Students will learn limit, continuity, function, series, derivative, and integral. Upon completing the course, students will be able to build a solid mathematics foundation required to understand more advanced data analytics methods. | |||||||||
| DSC2009 | Mathematics 2 for Data Science | 3 | 6 | Major | Bachelor | Data Science | Korean | Yes | |
| The primary goal of this course is to learn core concepts of linear algebra and their applications. In particular, vector space, vector, linear transformation, and matrix will be covered. Upon completing the course, students will be able to understand linear sets of equations and their transformation properties. | |||||||||
| DSC2012 | Computing 1 for Data Science | 3 | 6 | Major | Bachelor | Data Science | Korean | Yes | |
| The primary goal of this course is to learn data structures in computing. Students will learn various ways of storing data for the purpose of efficiently making use of the data such as algorithm analysis, recursive function, primary data structure, list, set, stack, queue, and tree. Upon completing the course, students will be able to have the ability of implementing core data structures and choosing different data structures for different use cases. | |||||||||
| DSC2013 | Computing 2 for Data Science | 3 | 6 | Major | Bachelor | Data Science | Korean | Yes | |
| The primary goal of this course is to learn computer algorithms. Students will learn various ways of storing data for the purpose of efficiently making use of the data such as priority queue, heap, sorting, dictionary, search tree, hash table, graph, minimum spanning tree, and shortest path. In particular, students will learn implementation, classification, and complexity of algorithms. Upon completing the course, students will be able to design and implement customized algorithms for various practical problems. | |||||||||
| DSC2015 | Data Security | 3 | 6 | Major | Bachelor | 2-4 | Data Science | - | No |
| This course covers major topics regrading the security of data throughout the whole lifecycle of data including data encryption, data transfer, cloud data security, and data anonimity. | |||||||||
| DSC2016 | Ethical and social impact of data | 3 | 6 | Major | Bachelor | 1-2 | Data Science | English | Yes |
| Data ethics refers to systemizing, defending, and recommending concepts of right and wrong conduct in relation to data. This course covers major issues in ethical and social impact of data including topics such as ownership, privacy, and openness an other important topics. | |||||||||
| DSC2017 | Knowledge Graph | 3 | 6 | Major | Bachelor | 2-4 | Data Science | - | No |
| This course covers design, implementation, and applications of knowledge graph. Through successful global use cases of knowledge graph, the course aimed to teach students the importance of semantic data management. | |||||||||
| DSC3001 | Practice in Big Data Analytics | 3 | 6 | Major | Bachelor | 3-4 | Data Science | - | No |
| This course brings together several key information technologies used in manipulating, storing, and analyzing big data. We look at the basic tools for statistical analysis, R, and key methods used in machine learning. We touch on related tools that provide SQL-like access to unstructured data. We analyze so-called NoSQL storage solutions for their critical features: speed of reads and writes, data consistency, and ability to scale to extreme volumes. We examine memory resident databases and streaming technologies which allow analysis of data in real time. Students gain the ability to design highly scalable systems that can accept, store, and analyze large volumes of unstructured data. | |||||||||
| DSC3002 | Practice in Data Visualization | 3 | 6 | Major | Bachelor | 3-4 | Data Science | English | Yes |
| This course teaches information design fundamentals and introduces a variety of visualization tools and techniques. At the end of the course, the student will be able to identify which visualization technique are best suited to deliver high impact messages under a variety of situations. The student will also learn how to present meaningful information in the most compelling and consumable fashion. The course deals with charts and tables as main modes of data visualization. | |||||||||
| DSC3004 | Digital Humanities | 3 | 6 | Major | Bachelor | 3-4 | Data Science | - | No |
| Digital humanities refers to all new types of humanities research, education, and creative projects enabled by information technology. The definition is not limited to studies of traditional humanities topics using information technology as a research method; but it also includes completely new forms of humanities research realized by the use of computers. This course introduces students to current digital humanities projects as well as tools for approaching humanities research in new ways such as digital scholarly editing, the creation of thematic archives, programmatic analysis of large-scale textual corpora, data mining, visualization of research out etc. On completion of this course students will be able to become familiar and conversant with various concepts and methods in the digital humanities develop the critical thinking skills necessary to evaluate digital scholarship | |||||||||
| DSC3008 | Practice in Medical Information Systems | 3 | 6 | Major | Bachelor | Data Science | - | No | |
| This course discusses the key characteristics, standards, and management of medical and healthcare data, and current health information technology-related issues, in particular, mobile technology, bioinformatics, public health informatics, online medical resources and medical information retrieval, medical imaging informatics, and disease management and disease registries. In order to utilize medical and healthcare database systems, the course practices PubMed and CINAHL (Cumulative Index to Nursing and Allied Health Literature). | |||||||||