• Journal of the Korean BIBLIA Society for library and Information Science
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• v.27 no.1
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• pp.263-290
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• 2016

The purpose of this study is to compare seven data science programs in Korea and ten data science programs in the US. Results show that 14 data science programs are housed in graduate schools. 10% of data science courses in Korea and 26% in the US fall under the Math and Statistics Knowledge area, one of the three areas defined by Conway. The syllabus analysis does not show much differences in terms of class contents and grading. The results of this study can be used to design data science programs that are more effective and well-grounded.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.105-115
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• 2021

Many services are provided through big data analysis in various fields such as individuals, private sectors, and governments. There is a growing interest in training data scientists to provide these services. Particularly, interest in big data-based marketing curriculum is high. This study analyzed the domestic and foreign university big data-based marketing-related curriculum to utilize vast and diverse types of information from a marketing perspective in the era of big data. As a result of the analysis of 3,523 subjects related to digital marketing, big data marketing, data analysis, and developers collected according to the analysis criteria, it was analyzed that the specialized curriculum for training data scientists required in the era of the fourth industrial revolution was not appropriate. It is expected that the proposed curriculum in this study will be useful for the development of digital marketing and big data-based marketing curriculum.

• Journal of Service Research and Studies
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• v.9 no.3
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• pp.87-99
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• 2019

Healthcare and data science are often linked through finances as the industry attempts to reduce its expenses with the help of large amounts of data. Data science and medicine are rapidly developing, and it is important that they advance together. Data science is a driving force in transition of healthcare systems from treatment-oriented to preventive care in healthcare 3.0 era. It enables customized precision-based medicine that current healthcare systems cannot facilitate, and discovers more cost-effective treatment. Currently, healthcare big data is in the reality of medical institution, public health, medical academia, pharmaceutical sector as well as insurance agency. With this motivation, the medical college of Soonchunhyang university has performed a 'healthcare data science initiative(HDSI)' since 2014. Most of domestic HDSI programs focus on short-term contents such as mentoring and sharing cases for data science. Therefore, it is difficult to provide education tailored to the level of skills and job competency required at the practical site. Soonchunhyang HDSI implemented specialized strategies for improving resilience and response to changes in the IT education of current healthcare with the emphasis on the need for systematic activation of the practical HDSI. The HDSI has been performed as a part of on industry-academic link program in CK-1. Through quantitative and qualitative analysis, this paper discussed the HDSI process, performance, achievement, and implications.

• Electronics and Telecommunications Trends
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• v.29 no.2
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• pp.33-41
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• 2014

본고에서는 한국을 비롯하여 미국, 일본, 유럽의 최근 빅데이터 특허시장을 분석하였다. 분석결과 빅데이터 특허시장은 미국이 세계시장을 독과점하는 구조로 나타났다. 전 세계적으로 가장 활발한 특허 활동을 전개하고 있는 미국 특허를 대상으로 빅데이터 연구개발 트렌드를 조망해 보면 과거에는 다수 기업들에 의하여 많은 특허출원이 이루어지는 경향을 보였으나, 최근 들어 기존 기업들 간의 경쟁이 심화되면서 대기업 위주로 특허출원시장이 재편되어 가는 경향을 보이고 있다. 한편 과거에는 데이터 분석 및 처리기술에 많은 특허출원이 이루어졌으나 최근에는 데이터 운영 및 관리기술로 옮겨가는 것으로 조사되었으며, 특허출원 건수도 과거에 비하여 대폭 증가하고 있는 경향을 보이고 있다. 우리나라의 경우 실시간 처리기술, 저장기술, 표현기술은 상대적으로 높은 출원 점유율을 보이고 있으나, 데이터 수집 및 분석기술은 상대적으로 점유율이 낮게 나타나 관련 기술 강화를 위한 대책 마련이 시급한 것으로 조사되었다. 정부는 이를 위하여 데이터 사이언티스트 양성을 위한 정책적 지원을 확대할 필요가 있다.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.112-121
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• 2016

This paper focuses on competencies of data scientists and behavioral intention that affect big data analysis performance. This experiment examined nine core factors required by data scientists. In order to investigate this, we conducted a survey to gather data from 103 data scientists who participated in big data competition at Kaggle platform and used factor analysis and PLS-SEM for the analysis methods. The results show that some key competency factors have influential effect on the big data analysis performance. This study is to provide a new theoretical basis needed for relevant research by analyzing the structural relationship between the individual competencies and performance, and practically to identify the priorities of the core competencies that data scientists must have.

• Journal of Radiation Industry
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• v.11 no.1
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• pp.7-11
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• 2017

RI-Biomics is a fusion technology in radiation fields for evaluating in-vivo dynamics such as absorption, distribution, metabolism and excretion (RI-ADME) of new drugs and materials using radioisotopes and quantitative evaluation of their efficacy. RI-Biomics information is being provided by RIBio-Info developed as information system for distributing its information and three requirements for improving RIBio-Info system have been derived through reviewing recent big data trends in this study. Three requirements are defined as resource, technology and manpower, and some reviews for applying big data in RIBio-In system are suggested. Fist, applicable external big data have to be obtained, second, some infrastructures for realizing applying big data to be expanded, and finally, data scientists able to analyze large scale of information to be trained. Therefore, an original technology driven to analyze for atypical and large scale of data can be created and this stated technology can contribute to obtain a basis to create a new value in RI-Biomics field.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.543-549
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• 2018

The importance of prediction is becoming more emphasized, due to the uncertain business environment. In order to implement the predictive model, a number of data engineers and scientists are involved in the project and various prediction ideas are suggested to enhance the model. it takes a long time to validate the model's accuracy. Also It's hard to redesign and develop the code. In this study, development method such as Lego is suggested to find the most efficient idea to integrate various prediction methodologies into one model. This development methodology is possible by setting the same data layout for the development code for each idea. Therefore, it can be validated by each idea and it is easy to add and delete ideas as it is developed in Lego form, which can shorten the entire development process time. Finally, result of test is shown to confirm whether the proposed method is easy to add and delete ideas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.255-258
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• 2022

As environmental pollution continues, attention on renewable energy is on the constant rise in recent days. Although various kinds of renewable energy such as solar, wind power and biomass energy have been generated in Jeju, opening and analyzing cases on related data seem insufficient. Therefore, this study is being conducted to deduce the variables which have high relation with solar panel&s output and to understand machine learning methods that can be applied to solar power generation data by utilizing Kaggle platform, which is actively used by a number of scientists. Then, it is planned to propose a form of solar power generation dataset by researching machine learning methods that could be applied to the data. To be specific, analyzing solar power generation data with the Kaggle platform, this study will provide complements on gathering solar power data in Jeju. This study is anticipated to be utilized on data analysis for developing the solar power industry in Jeju. That is, this study is expected to reveal the room for improvement inherent in existing open datasets in Jeju, so that they could be constructed in a suitable form for machine learning for AI analytics. Through this process, a method to increase efficiency of solar power generation is anticipated to be prepared.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.305-332
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• 2022

This preliminary study examined the degree programs in data science at the School of Information in the States. The focus of this study was the data science degrees offered at the School of Information awarded by the 64 Library and Information Science (LIS) programs accredited by the American Library Association (ALA) in 2022. In addition, this study examined the degrees, majors, minors, specialized tracks, and certificates in data science, as well as the potential careers after earning a data science degree. Overall, eight Schools of Information (iSchools) offered 12 data science degrees. Data science courses at the School of Information focus on topics such as introduction to data science, information retrieval, data mining, database, data and humanities, machine learning, metadata, research methods, data analysis and visualization, internship/capstone, ethics and security, user, policy, and curation and management. Most schools did not offer traditional LIS courses. After earning the data science degree in the School of Information, the potential careers included data scientists, data engineers and data analysts. The researcher hopes the findings of this study can be used as a starting point to discuss the directions of data science programs from the perspectives of the information field, specifically the degrees, majors, minors, specialized tracks and certificates in data science.