Curriculum
| Course Number | Summary | English |
|---|---|---|
| BDA710 | Basic Programming and Practice | |
| This course covers basic programming, data collection, processing, analysis, and visualization through Python's basic grammar, data structure, functions, and classes, and also develops programming skills to master Python through simple reality data analysis. | ||
| BDA711 | Database Systems | |
| This course helps understand database management, the relationship between corporate information systems and business action, and data approaches or process approaches for building information systems. It also develops the ability to model with data. It understands the differences between big data, analytics, and existing database management, and future development. | ||
| BDA712 | Machine Learning Programming | |
| It learns how to perform machine learning and deep learning using the Python language. It uses the Scikit-learn library to practice collecting, processing, and analyzing various structured and non-structured data and further develops machine learning and deep learning programming skills by practicing deep learning models based on Tensorflow and Keras. | ||
| BDA713 | Big Data Processing Techniques | |
| It learns the concepts and skills of parallel computing for processing large-scale data and performing big data analysis. In particular, it learns the concept and method of parallel calculation using GPUs, how to install and utilize Hadoop and Spark to utilize multiple computers, and performs real big data analysis projects. | ||
| BDA715 | Process Mining | |
| Process mining is a technique that analyzes various events in a corporate information system and derives the meaningful results needed to operate business processes. Topics related to process mining and mining include business process management, business intelligence, artificial intelligence, and data mining. | ||
| BDA716 | Artificial Intelligence | |
| Artificial intelligence is a technology that uses computers and information technology to imitate human beings or to perform behavior that is better than human. It basically includes knowledge expression and reasoning, expert systems, machine learning and data mining, and natural language processing. We aim to acquire ways to intelligently approach and solve various problems faced in industry by learning the basic concepts and key techniques of deep learning. | ||
| BDA717 | Machine Learning | |
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It learns about the theories and practical uses of machine learning in the field. Students study the basic principles and theoretical background of supervised learning, unsupervised learning, and reinforcement learning, and learn specific algorithms for them. It covers Bayesian, decision trees, artificial neural networks, SVMs, deep learning, and other recent machine learning algorithms, as well as methods used in various fields such as finance, marketing, and production. |
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| BDA718 | Deep Learning | |
| It aims to learn the basic concepts and core techniques of deep learning and learn deep learning models such as CNN, RNN, and GAN. Students also learn how to intelligently approach and solve various problems faced in the industry. | ||
| BDA719 | Unstructured Data Processing | |
| It analyzes large amounts of unstructured data such as text, images, sounds, and videos, which have recently been exploding with the use of social media, and learns about the process of extracting useful information, patterns, and knowledge necessary for management decision-making. It learns techniques such as collecting unstructured data such as text, image, or video using crawling techniques, data processing and conversion, frequency analysis, keyword correlation analysis, sentiment analysis, topic analysis, image mining, and video mining. It also performs practical projects that apply them to actual management cases.It analyzes large amounts of unstructured data such as text, images, sounds, and videos, which have recently been exploding with the use of social media, and learns about the process of extracting useful information, patterns, and knowledge necessary for management decision-making. It learns techniques such as collecting unstructured data such as text, image, or video using crawling techniques, data processing and conversion, frequency analysis, keyword correlation analysis, sentiment analysis, topic analysis, image mining, and video mining. It also performs practical projects that apply them to actual management cases. | ||
| BDA72 | Sustainable Society and SDGs | |
| We understand the Sustainable Development Goals (SDGs) and think about ways to realize a sustainable society. Furthermore, we use big data for SDGs to explore approaches that we can solve and find solutions. | ||
| BDA720 | Big Data Visualization | |
| It is important to visualize and represent the results of big data analysis because visualization enables you to intuitively identify the characteristics of the data quickly, thereby facilitating the understanding and persuasion of the decision-making process in management. Students develop the ability to efficiently and effectively derive suitable visualization data for specific management purposes by utilizing various visualization tools such as R, Python, and Tableau. | ||
| BDA721 | Federated Transfer Learning | |
| Students study the latest research trends in federated transfer learning, which improves learning efficiency by transferring the entire machine-learned model from one subject to another and improves privacy and security by only sharing part of each subject's model. Students also increase their practical skills by applying them to real-life projects. | ||
| BDA722 | Big Data Management and Industry | |
| This course focuses on the use of big data in management and industry rather than big data technology. It examines industry-specific cases and covers trends, future developments, and future prospects. It also studies the concepts and examples of open data and my data beyond big data. | ||
| BDA723 | Sustainable Decision Analysis | |
| This course introduces basic theories and concepts on decision-making problems and decision analysis to improve the ability of managers to effectively and efficiently solve a variety of complex and difficult-to-define types of decision-making problems in the uncertainty that they often face. Students learn various modeling methods and analytical techniques to standardize and solve decision-making problems. | ||
| BDA724 | Sustainable Production and Logistics | |
| This course covers the latest theories and techniques related to the design and analysis of production and logistics systems. The main topics include SCM, modeling techniques for production systems, metaheuristics, application of professional systems, applications of computer simulation, JIT, ERP, etc. The course topic will be newly selected each time the course is launched. Group research and presentations are encouraged. | ||
| BDA725 | Data Security and Ethics | |
| Students learn overall information about data protection and information ethics. It aims to learn about data security, code, system security, network security, and authentication, as well as about personal information protection, physical information protection, disaster recovery planning, and access control. It also deals with ethical issues in various areas, including information ethics, information technology ethics, and information system ethics, within the framework of accountability management. Through this course, students will learn the motivation and ability to maintain a secure and sustainable information society. | ||
| BDA726 | Data and Algorithm Governance | |
| It teaches how to use data and algorithms and processes in the big data era. It deals with policies and processes to manage the availability, usefulness, integrity, and security of data required by an organization and also covers privacy, security, data quality, and compliance with management regulations. Furthermore, it learns algorithm design, maintenance and management, and ethical issues so that big data analysis can be performed effectively and preferably. | ||
| BDA727 | Quantum Computing | |
| It learns about the concept and use of quantum computers, a new computing method for processing large amounts of information. It presents the possibility of creating new industries and starting businesses in the future big data era by understanding high-speed calculation methods and concepts that use atoms rather than semiconductors as storage cells and learning their applicability in various fields such as drug development, medicine, distribution, law, and finance. | ||
| BDA728 | Big Data Thesis Study 1 | |
| This course covers big data research methodologies and thesis writing methods. It also guides the thesis-writing process by sharing and presenting the progress of master's and doctoral theses. | ||
| BDA729 | Big Data Thesis Study 2 | |
| This course covers big data research methodologies and thesis writing methods. It also guides the thesis-writing process by sharing and presenting the progress of master's and doctoral theses. | ||
| BDA73 | Big Data Trends in Industry | |
| We domestically and internationally research and publish papers to study the trends of the big data industry and its academic system. We also hold seminars with hands-on experience to study how theory and practice in the field of big data are combined and developed. | ||
| BDA730 | Sustainable Big Data Projects 1 | |
| Students set topics to solve SDG problems for a sustainable society using the big data application knowledge and skills acquired in various courses. Additionally, students experience teamwork and the process of solving social problems while conducting team projects. | ||
| BDA731 | Sustainable Big Data Projects 2 | |
| Students set topics to solve SDG problems for a sustainable society using the big data application knowledge and skills acquired in various courses. Additionally, students experience teamwork and the process of solving social problems while conducting team projects. | ||
| BDA733 | Advanced Financial Engineering | |
| This course covers various engineering techniques used in investment and financial market analysis, such as asset allocation and portfolio management. Students learn theories on financial engineering and also gain hands-on experience by utilizing programming languages such as MATLAB and Python. | ||
| BDA735 | Network Science and Applications | |
| Students learn recent major research about the structural characteristics of complex networks and the theory of various dynamic phenomena that occur on them, and how to apply the results to social and financial networks. | ||
| BDA739 | Social Network Analysis | |
| It learns the basic concepts and principles of social network analysis and enables network analysis to be derived. It converts actual social phenomena into network data, introduces methods for analyzing social networks, and enables them to be applied in practice. | ||
| BDA74 | Big Data Startup and Commercialization | |
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| BDA75 | Applied Probabilistics and Statistics | |
| Students learn about discrete and continuous probability variables and probability models for analyzing big data. The course covers statistical techniques such as regression analysis, factor analysis, and multivariate analysis, and conducts exercises in statistical analysis using R and Python. | ||
| BDA76 | Optimization Methods | |
| It learns about the basic concepts and key techniques of optimization that often appear in big data analysis. The detailed topics cover mathematical programming, linear programming, integer programming, nonlinear optimization, and dynamic programming. The practical knowledge is trained by practicing solutions to optimization problems along with examples used in big data techniques. | ||
| BDA77 | Meta-Heuristics | |
| NP-hard optimization problems are those that believe that it is impossible to accurately calculate the optimal solution at the right time. In this case, one important approach is to explore how close the approximate solution to the optimal solution can be guaranteed by appropriately limiting computational resources. The lecture focuses on acquiring various approximate solution algorithm design ideas. At the same time, each student goes through the process of applying these ideas to the problem they choose. | ||
| BDA78 | Data Science Mathematics | |
| This course covers the necessary mathematics and methodology to understand data science. It trains basic knowledge about big data analysis, machine learning algorithms, and decision-making techniques using nonlinear and linear statistical models and basic mathematical tools (linear algebra, calculus, mathematical programming, etc.) that support data science. | ||
| BDA79 | Brain Information Processing Theory | |
| This course covers the structure and elements of the brain, the principles of brain information processing, the principles of vision, perception, and transmission of auditory information, brain activity data analysis techniques, the brain-computer interface, the relationship between brain information processing and computer information processing, and the methodology of using brain data in artificial intelligence research. | ||
| GRADS7249 | Big data | |
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| GRADS7256 | AI Research Methodology | |
| - | ||
| IE736 | Financial Optimization for Investment Management | |
| The job of planning, implementing, and overseeing the funds of an individual investor or an institution is referred to as investment management. The purpose of this course is to describe the process of investment management and optimization techniques employed for investment management. We will study topics relevant to investment management including but not limited to: traditional portfolio selection, asset pricing, robust portfolio management techniques, and multi-period portfolio optimization models. | ||
| IE740 | Industry-academic cooperation project II | |
| In this course, students perform a industry-academic cooperation project to define a practical field problem and solve it with industry experts. The students can learn their problem-solving abilities by experiencing the field problems that companies face in real industry. | ||
| IE742 | Analysis of Smart-Technology Market | |
| Consumer preference theory and application to model and analyze smart energy technology market. Understanding the consumer preference analysis process and related main theories and analysis methodologies. The goal of this course is to review the fundamental theory and methodologies of consumer behavior. Particularly, this course covers the theoretical, empirical and applied methods of consumer decision-making process. | ||
| IE755 | Special Topics in Smart Energy | |
| Learn theories to reduce the supply, demand, carbon emission and related costs of energy used in production and manufacturing systems and learn related applications. In order to reduce energy consumption and relevant costs in manufacturing systems, students learn basic theory in manufacturing energy as well as applications. | ||
| IE759 | Introduction to Smart Factory | |
| Smart factory means an dramatically enhanced manufacturing environment of integrating advanced ICT such as IIoT, Cloud, CPPS, Big data and AI to manufacturing. This course provides the core technology, trend and case study of smart factory to improve the understandings of smart factory, which is the core concept of the 4th industrial revolution. | ||
| IE761 | Industry-academic cooperation project I | |
| In this course, the students who conduct academy-industry collaboration projects in smart factory and smart manufacturing will make a research on the related topics and share and present their results of the collaborative projects. | ||
| IE765 | Statistical Learning | |
| The lecture offers a comprehensive exploration of the fundamental concepts of statistical learning, an innovative field that fuses statistical principles with data-driven pattern recognition. The subject matter spans both the theoretical foundations and the practical techniques necessary to leverage statistical learning in real-world scenarios. | ||
| IE767 | Special Topics in Financial Engineering | |
| This course focuses on various data analysis and engineering techniques used in financial market and investment analysis. Recent research topics are introduced and students learn how to implement various models in Python. | ||
| MGMT7010 | Datamining theory and applications | |
| - | ||
| MGMT7103 | Business Analytics | |
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| MGMT7177 | Big Data Programming | |
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