For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ADS5002 | Basic Statistics | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code. | |||||||||
| ADS5003 | Bigdata Processing | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| Bigdata system is similar to legacy database system on interface but quite different usually. This course covers necessary techniques for naturally Bigdata processing with consider of Bigdata. Students will understand to Bigdata system and how to handle Bigdata in the system. Students learn Bigdata store, load, process, integration, policy on Hadoop system environment. | |||||||||
| ADS5004 | Data Analysis Language | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| This course provides students with opportunities to develop skills and solve statistical problems using Python and R. Students learn about Python programs and how to use them for efficient data analysis. Understand the software installation and settings required in statistical programming environment, and general programming concepts. This course emphasizes data processing and basic statistical analysis. This course requires basic knowledge of basic statistics and does not require prior experience in basic computer programming. | |||||||||
| ADS5005 | Multivariate Statistics | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| An introduction to multivariate statistical models, well balancing three equally important elements: the mathematical theory, applications to real data, and computational techniques. Traditional multivariate models and their recent generalizations to tackle regression, data reduction and dimensionality reduction, classification, predictor and classifier instability problems. | |||||||||
| ADS5006 | Advanced Machine Learning | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. | |||||||||
| ADS5007 | Artificial Intelligence | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| The course objectives are to learn techniques and theory developed in major areas of Artificial Intelligence. The topics are: problem solving and search, logic and knowledge representation, reasoning, machine learning, soft computing, data mining and miscellaneous topics in the current research. | |||||||||
| ADS5010 | Application of Linear Algebra | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| Linear Algebra is the study of vector spaces and linear transformations on vector spaces. Techniques from Linear Algebra are also used in analytic geometry, engineering, physics, natural science, computer science, and the social sciences. Topics include the use and application of matrices in the solution of systems of linear equations, determinants, real n-dimensional vector spaces, abstract vector spaces and their axioms, linear independence, span and bases for vector spaces, linear transformations, eigenvalues and eigenvectors, matrix factorizations, and orthogonality. Computer explorations using MATLAB is an integral component of this course. | |||||||||
| ADS5013 | Advanced in Database System | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| From this course, students learn fundamental concept and theories of data management system (DBMS). This course introduces principal technique of DBMS, data load, external sort, tree indexing, hash indexing, query optimization, physical design and tuning, transaction, concurrency control, recovery techniques. | |||||||||
| ADS5014 | Advanced in Bigdata Platform | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| This course covers Hadoop and Hadoop Eco System which is a group of applications based on and working with Hadoop. Students learn Hadoop architecture, software stack and principle of its processes like map-reduce. Students study Hadoop eco system, like Hive, hbase, Spark, scoop, flume, kafka, Azkaban, ambari, etc. | |||||||||
| ADS5016 | Natural Language Processing | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. There are a large variety of underlying tasks and machine learning models behind NLP applications. In this course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. this course will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some recent models involving a memory component. | |||||||||
| ADS5017 | Optimization | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| This course introduces linear and nonlinear programming, iterative and dynamic programming, especially for optimal control problems. Discrete and continuous optimal regulators are derived from dynamic programming approach which also leads to the Hamilton-Jacobi-Bellman Equation and the Minimum Principle. Minimum energy problems, linear tracking problems, output regulators and minimum time problems are considered. | |||||||||
| ADS5018 | Application Data Analysis | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| This course introduces cases of data analysis. Students learn how to apply various data analysis method in case. Cases in this course is widespread problem from many domains but they have various and multiple personalities. This course, for these objective, experiences cases of analysis for objective to adjust the way to deal with problems in various condition and view. | |||||||||
| ADS5019 | Deep Learning | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| This course covers deep learning based on artificial neural network which is advanced on various industrial. This course, especially, give students basic understanding of modern neural networks and their applications in computer vision and natural language understanding. Students learn about Convolutional networks, RNNs, LSTM, Dropout and more. This course introduces the major technology trends driving Deep Learning. | |||||||||
| ADS5021 | Advanced in Information Security | 3 | 6 | Major | Master/Doctor | 1-8 | - | No | |
| This course focuses on the fundamentals of information security that are used in protecting both the information present in computer storage as well as information traveling over computer networks. Interest in information security has been spurred by the pervasive use of computer-based applications such as information systems, databases, and the Internet. In this course, we will consider such topics as fundamentals of information security, computer security technology and principles, access control mechanisms, cryptography algorithms, software security, physical security, and security management and risk assessment. | |||||||||
| ADS5022 | Capstone Project | 3 | 6 | Major | Master/Doctor | 1-8 | Korean | Yes | |
| The purpose of the Capstone Project is for the students to apply theoretical knowledge acquired during the Data Science program to a project involving actual data in a realistic setting. This course runs a project to solve actual problems from industrial. Subject of the project is from specific domain in actual. During the project, students engage in the entire process of solving a real-world data science project, from collecting and processing actual data to applying suitable and appropriate analytic methods to the problem. Both the problem statements for the project assignments and the datasets originate from real-world domains. | |||||||||