For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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. | |||||||||
| ECO3001 | Mathematical Economics | 3 | 6 | Major | Bachelor | 3-4 | Economics | English | Yes |
| This course deals with the analytical and the mathematical aspect of economic theory. It also deals with the game-theoretic analysis of various economic situations. Topics include general equilibrium theory, game theory and information economics. | |||||||||
| EEE3057 | Antenna Engineering | 3 | 6 | Major | Bachelor | 3-4 | Electronic and Electrical Engineering | Korean | Yes |
| The course begins with a brief introduction to antennas and propagation. Next, antenna concepts such as gain, directivity, and radiation patterns are introduced. The theory for impedance determination for dipole and loop antennas are presented. Then, the course treats the aperture-type antennas. The receiving properties of antennas are evaluated using the concepts of effective area, effective length, impedance match, and antenna noise temperature. Finally, a broad spectrum of topics related to radio-wave propagation is presented. | |||||||||
| ISS3233 | Statistics in Python | 3 | 6 | Major | Bachelor | 1-4 | English | Yes | |
| This course will cover elementary topics in statistics using Python. The statistics topics include principles of sampling, descriptive statistics, binomial and normal distributions, sampling distributions, point and confidence interval estimation, hypothesis testing, two sample inference, linear regression, and categorical data analysis. Using Python, students will learn basic knowledge in Python programming, data management, data formats and types, statistical graphics and exploratory data analysis, and basic functions for statistical modeling and inference. | |||||||||
| 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 | |||||||||
| IWS3030 | Statistical Foundations for Text Mining with R | 3 | 6 | Major | Bachelor | Winter International Student Experience | - | No | |
| This course provides a comprehensive, practical foundation for extracting insights from text data using statistical methods implemented in the R programming language. The first half of the course covers key statistical principles needed for data inference, such as conditional probability, probability distributions, confidence intervals, hypothesis testing, and regression analysis. The second half directly applies these concepts to essential tasks in text mining. Students will learn to manage and process raw text data, convert it into quantitative features, and develop statistical models to recognize patterns, topics, and sentiments. Key topics include the Bag-of-Words model, TF-IDF weighting, topic modelling with Latent Dirichlet Allocation (LDA), sentiment analysis, and text classification using algorithms like Naive Bayes and Regression. Upon completing this course, students will be able to apply essential statistical concepts, preprocess raw text data, utilize both unsupervised and supervised learning methods, and perform text classification. | |||||||||
| IWS3033 | Fundamentals of Programming Languages | 3 | 6 | Major | Bachelor | Winter International Student Experience | - | No | |
| The Principles of Programming Languages course offers a thorough introduction to the fundamental concepts of programming languages. The course systematically explores design issues of various language constructs and analyzes the design choices made in some of the most common languages. Students will learn about different language categories, the progression of programming languages, and the principles of syntax and semantics in modern programming languages. The course also covers key programming concepts such as lexical and syntax analysis, names, bindings, type checking, scoping, data types, expressions, statements, and control structures. Additionally, it includes topics like subprograms, abstract data types, functional programming languages, and logic programming languages. This course aims to give students a solid foundation in the principles of programming languages and insight into the design decisions that shaped their development. | |||||||||
| IWS3034 | Artificial Intelligence | 3 | 6 | Major | Bachelor | Winter International Student Experience | - | No | |
| This course offers an engaging introduction to the world of artificial intelligence, starting with key concepts such as intelligence, rationality and agents. It will then dive into problem-solving techniques, with a focus on search algorithms, using puzzles, games and real-world examples to illustrate the concepts so that you get a grasp of how computer think. You will also explore the exciting and rapidly evolving field of machine learning, covering cutting-edge areas like computer vision, natural language processing, deep learning and generative AI. A core feature of the course will be discussing the profound impact of AI on society today—highlighting its applications in fields such as healthcare, recruitment and social media, along with the ethical challenges these technologies bring. This course is designed for beginners—ideal for students with little to no background in computing or those with in the early stages of computing-related majors who are keen on pursuing AI studies in the future or the application of it in their respective fields. | |||||||||
| MTH2002 | Number Theory | 3 | 6 | Major | Bachelor | 2-3 | English | Yes | |
| Introduction to number theory. This course will cover primes, unique factorization, congruences, quadratic residues, arithematic functions, Diophantine equations, and Gaussian integers. | |||||||||
| MTH2003 | Vector Calculus | 3 | 6 | Major | Bachelor | 2-3 | - | No | |
| In this class, we study differentiation and integration and extend theses concepts on curves, surfaces and manifolds. Relations between differentiation and integration of vector valued functions are presented. In details, we study differentiation of a function of several variables, inverse function theorem, implicit function theorem, minimum and maximum of a function of several variables, multiple integration, Fubini theorem, change of variables, vector fields, differential forms, integration on chain, Green theorem, Stokes theorem on manifolds. | |||||||||
| MTH2006 | Analysis Ⅱ | 3 | 6 | Major | Bachelor | 2-3 | Korean | Yes | |
| This is a continuing course which follows analysis I. Topics cover: the Riemann-Stieltjes integral, uniform convergence, the stone-weierstrass theorem, special functions, contraction principle, the inverse function theorem, the implicit function theorem, the rank theorem, multivariable calculus, Stokes' theorem, etc. | |||||||||
| MTH2007 | Differential Equations | 3 | 6 | Major | Bachelor | 2-3 | Korean | Yes | |
| Basic theory of elementary differential equation and its application are dealt with : first order ordinary differential equation, constant coefficient linear ordinary differential, high order ordinary differential equation, solution by series(Legendre differential equation, Bessel differential equation), etc. | |||||||||
| MTH2008 | Set Theory | 3 | 6 | Major | Bachelor | 2-3 | - | No | |
| Set theory is for the junior or senior of mathematical sciences as one-semester course. Especially, it is essential for mathematics major students. Sets, union and intersection, cartesian product, functions, image, inverse image, cardinality, countable and uncountable, relations, equivalence classes, partially ordered and totally ordered sets, axiom of choice, Zorn's lemma are treated. | |||||||||
| MTH2011 | Computer Aided Applied Mathematics | 3 | 6 | Major | Bachelor | 2-3 | - | No | |
| The course is intended for the use and the application of Mathematical softwares, especially Mathematica. Our goal is to provide another aspect of the basic and advanced concepts in Mathematics through symbolic and computational mathematical softwares | |||||||||
| MTH2012 | Applied Differential Equations | 3 | 6 | Major | Bachelor | 2-3 | - | No | |
| This course will be a continuation of ordinary differential equations.Topics include : Laplace transformations and its applications ; total differential equations ; partial differential equations of the first order ; linear partial differential equations ; linear higher order partial differential equations with constant coefficients. | |||||||||