DATA VISUALIZATION

데이터 시각화는 데이터를 알기 쉽게 시각화하여 연구자가 의미 있는 정보를 추출하는데 효과적인 의사 결정을 할 수 있도록 하는 유용한 분야이다. 이를 다루는 수업에서 연구 데이터 시각화 기술에 대한 적합한 선택에 초점을 맞추어 기본 기술과 개념을 소개한다. 또한 서지 및 통계 데이터를 소프트웨어 툴을 사용하여서 시각화하는 방법도 중점적으로 다룬다. 이외에도 다음과 같은 주제를 포함한다: 가) 다양한 유형의 데이터 시각화, 나) 통계 도구를 사용한 데이터 시각화, 다) 시각화 도구 및 소프트웨어, 라)서지 데이터 시각화, 라) 빅 데이터를 기반으로 한 시각화.

This course aims to introduce students to data visualization techniques and concepts using the R language. The course focuses on selecting appropriate visualization techniques for different types of data and emphasizes the effective communication of insights through visual representations. Students will gain hands-on experience with R and relevant packages to create a variety of visualizations, including bar charts, column plots, scatter plots, pie charts, heat maps, and network graphs. The course also covers customization options, data flow visualization, and techniques for presenting data effectively.

Use of English: This course will be conducted in English, and students are expected to use English whenever possible, including for homework assignments and other course-related activities. Attendance and Classroom Behavior: Class attendance and active participation are essential for success in this course. Arriving late or frequently stepping out of class is inappropriate, as it can disrupt the learning environment and hinder both individual and group progress. If you anticipate any issues with attendance or participation, or if unexpected challenges arise during the semester, please contact me as soon as possible so we can discuss appropriate accommodations or solutions.

Assignments and Course Requirements: Assignments play a critical role in building your programming skills through practice. Each assignment gives you hands-on experience with different programming concepts and data processing techniques that you'll encounter in library and information settings. Late submissions will affect your grade, so please manage your time carefully and start working on assignments early. Detailed instructions for each assignment will be provided in class and posted on the course website. Handling Absences Due to Early Employment or Internship: If students anticipate being absent for an extended period due to early employment or internship opportunities, it is essential that they inform the instructor in advance. This proactive communication helps to minimize any potential negative impact on their academic progress and ensures appropriate arrangements can be made to support their learning. Exams: Exams are open-book, open notes, and AI tools are permitted on your laptop. You may use cameras for AI image recognition tools. While you can freely use and copy from AI outputs, relying solely on AI won't guarantee a high grade. The exam questions will test your understanding of information retrieval concepts and applications. Any form of communication with other people during the exam is prohibited. Other devices such as cell-phones and tablets, along with email and messaging apps, are also prohibited.

This course is open to students from all majors without any specific prerequisites. It offers a comprehensive curriculum that encompasses essential knowledge and skills applicable to various academic disciplines. Regardless of your field of study, you are encouraged to enroll and actively participate in this course. The content is designed to provide a solid foundation in the subject matter and equip students with practical skills that can be applied in their respective domains.

This course enable students to leverage the power of visual representations to explore, analyze, communicate, and derive insights from data effectively. These skills are in high demand across various industries, including business analytics, data science, market research, and academia.