This course introduces the basic knowledges about objects in programming, and then the key concepts of object-oriented programming including classes, instances, inheritance, polymorphism, and encapsulation. Students in this course are expected to become familiar with objected-oriented programming principles and to have capabilities of applying those principles to real-world software problems.

As a laboratory class of the Object-Oriented Programming course, students learn the practical skills for applying their knowledges on object-oriented programming to challenging problems by implementing, testing, and extending a collection of object-oriented programs.

This course is designed to help students understand basic principles of Open Source Software (OSS) and make students become skillful for OSS tools. The course includes OSS case studies and students will be required to participate a co-working project using OSS development tools.

As a laboratory class of the Open Source Software course, students improve their practical skills for utilizing OSS tools, applying OSS development processes, and participating co-working projects.

This course is aimed for students majoring in data science to help with a basic understanding of data science. In this course, students will learn an overview of the concepts, methods, techniques, and tools of data science. They will also acquire the overall knowledge of data science by learning the main topics and practices of data science.

This course focuses on lecturing the methods of statistical analyses and interpretations using R language which is open source s/w. This course covers studying the techniques of R programming and advanced statistical theories, and then practicing various statistical analyses for the real cases data by means of R programming.

This course introduces the basic data structures for designing and implementing computer programs, including arrays, lists, stacks, queues, trees, graphs, and hash tables. Students in this course are expected to become familiar with how to implement, extend, and utilize various data structures with object-oriented programming languages.

As a laboratory class of the Data Structures course, students improve their programming skills with data structures by implementing, testing, extending, and utilizing a collection of data structures with object-oriented programming languages.

This course introduces the methods for developing mobile applications that can run on mobile devices such as smartphones and tablet PCs. Specifically, students learn the basic grammars of Java programming language, the programming skills for solving real-world problems with Java, and the development process of building Java applications on the Google Android platform.

This course addresses issues that arise in dealing with management information as a business resource. Students study systems concepts, information technology, and application software. This course also employs state-of-the-art coverage through numerous practical applications and offers emerging cases from the information systems field. As an introduction to the field of Management Information Systems (MIS), topics covered deal with computer technologies, information development, and impact of information systems on business organization at a variety of levels, from personal information systems to organization information architectures. The course covers both technical and managerial aspects of MIS. Major attention is given to the implications of information systems for achieving competitive advantage.

Manufacturing process data, natural environment data, biometric information, life-log data are gathered from the sensors, known as Internet of Things. People's opinions, locations, and purchase behaviors are collected by the network device. These IoT / SNS data is referred to as big data and the study of data science disciplines. In this course, students learn the process of OPEN API data collection and various data analysis methods.

Based on relational database systems, the class focuses on database system concepts, data modeling, SQL(Structured Query Language), transaction management, data security, and so on. After completing the class, students will understand the purpose and components of database systems and learn how to construct and utilize databases through projects and lab practices.

Many companies are developing mobile apps and providing services for a variety of purposes. In order to provide valuable services to mobile users, mobile planning should be approached strategically and methodically. This course is designed for learning mobile planning methodology such as idea embodiment, functional specification, and UI/UX design. Students will also foster a practical sense of mobile planning by analyzing the case of successful mobile apps.

Learners of the digital age are exposed to large amounts of data through the SNS and IoT. In accordance with the change of this environment, the data representation and information delivery method has progressed enormously. That is, a number of information is visually analyzed, and the need for data visualization method is increasing. Recently with the growth of big data technology market, data visualization has become a critical technology component. Whereas traditional visualization techniques mainly used to show statistical information about the system logs and results of analysis in a graph, big data visualization is focused on data summarization and visualization methodology to help highlight information at a glance.

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. In this course, students learn how to extract the text from a document and create a datamart using the word configuration. Students also use relationships between words to perform a sensitivity analysis or word cloud, and utilize this information to the social network analysis and clustering or classification.

This course focuses on enlarging the understanding of the range and the capabilities of ICT applicable to business area and new ICT trends such as big data, cloud computing, IoT, social media and etc., and lecturing on the methodologies and techniques to develop new products/services/businesses and innovative processes through ICT convergence activities.

This course explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. As an introduction to the field of Data mining, this course presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic may be organized into two parts, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. This course covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. Major attention is given to the implications of data mining for achieving competitive advantage for business.

As building the social relations with various methods and channels, people constitute the social networks. Analysis of the social networks can gain significant insight into business management. In this course, student will learn the concept of social network analysis and acquire the ability to analyze social networks by practicing the methods.

This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. Students will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.

Big data can provide new opportunities and value for business. However, the effective deployment and utilization of big data is essential for achieving this purpose. In this course, student will learn the business models associated with big data and the strategic roadmap for the deployment and utilization of big data by studying the successful practices of big data business.

This course introduces the fundamental principles of data science, and walks students through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect in Business. It also helps students understand and apply the many data-mining techniques in use today. This course provides examples of real-world business problems to illustrate these principles. Students will not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in the company’s data science projects. Students will also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

As continuation of Capstone Design 1, this course provides students the opportunity to work with real-world, open-ended, interdisciplinary challenges. Teams attend lectures given by experts from Industry and Academia on topics including industrial design, manufacturing, market research and marketing, intellectual property, company formation, codes and standards, and ethics, to name a few. At the end of the semester, student teams display and pitch their inventions and marketability to a panel of judges, invited guest, media, and their peers.