For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CLA3104 | Liberal Arts Individual Research4 | 2 | 4 | Major | Bachelor | 2-4 | Liberal Arts | - | No |
| 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. | |||||||||
| CLA3105 | Liberal Arts Individual Research5 | 2 | 4 | Major | Bachelor | 2-4 | Liberal Arts | - | No |
| 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. | |||||||||
| CLA3108 | Introduction to AI for Humanities | 3 | 6 | Major | Bachelor | Liberal Arts | - | No | |
| The goals of introducing the field of Artificial Intelligence to students from the humanities is to disseminate knowledge and create awareness on the biggest buzz word going around today. The fundamentals include notions of rationality, knowledge representa-tion and reasoning, machine learning and ethics. Students will be exposed to the enormity of the field which does not only involve hot topics such as deep learning and smart robots. Further, many well-known success stories of AI will be discussed to complement all the hype that has been surrounding this area, from games to curing dis-eases. Finally and perhaps most importantly, students will debate on on the philosophi-cal and ethical issues pertaining to the development of AI solutions. Using real world examples where lessons have been learnt, students will understand the need for and implications of using AI responsibly. The course contents will require NO programming, major algorithms will be explained broadly without requiring students to perform any calculations. - Introduction - Agents, rationality, strong vs. weak AI - Search and Problem Solving - Uninformed searches, informed searches, local search, logic - Machine Learning - Supervised learning, unsupervised learning, neural networks and deep learning - Applications - Robotics, computer vision, natural language processing - Ethics - Case studies of misuse of AI, dilemmas when designing AI systems | |||||||||
| CLA3109 | AI/Data-science for Korean Humanities | 3 | 6 | Major | Bachelor | Liberal Arts | Korean | Yes | |
| This course is for under-graduates who want to become digital humanities researchers or to learn AI/data-science with Korean language corpus to find a career. Students will develop ability to deal with data through theories, Python coding practices. It is a team teaching course of experts with theory and development skills. | |||||||||
| COM3006 | Computer Networks | 3 | 6 | Major | Bachelor | 3-4 | Computer Education | Korean | Yes |
| There are details about the upper layer, protocols, and interfaces, based on the basic concept of data communications. This course is concerned with the mode of operation of the different types of data network that are used to interconnect a distributed community of computers. Also, it is described Internet protocols. | |||||||||
| 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. | |||||||||
| 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. | |||||||||