• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.1-20
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• 2022

As the global business environment is rapidly changing due to the 4th industrial revolution, new jobs that did not exist before are emerging. Among them, the job that companies are most interested in is 'Data Scientist'. As information and communication technologies take up most of our lives, data on not only online activities but also offline activities are stored in computers every hour to generate big data. Companies put a lot of effort into discovering new opportunities from such big data. The new job that emerged along with the efforts of these companies is data scientist. The demand for data scientist, a promising job that leads the big data era, is constantly increasing, but its supply is not still enough. Although data analysis technologies and tools that anyone can easily use are introduced, companies still have great difficulty in finding proper experts. One of the main reasons that makes the data scientist's shortage problem serious is the lack of understanding of the data scientist's job. Therefore, in this study, we explore the job competencies of a data scientist by qualitatively analyzing the actual job posting information of the company. This study finds that data scientists need not only the technical and system skills required of software engineers and system analysts in the past, but also business-related and interpersonal skills required of business consultants and project managers. The results of this study are expected to provide basic guidelines to people who are interested in the data scientist profession and to companies that want to hire data scientists.

• 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 The Korean Association For Science Education
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• v.28 no.6
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• pp.649-663
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• 2008

The purpose of this study was to compare the processes of making hypotheses concerning the anomalous path of Wukong, a typhoon that came close to Korea recently, between an earth scientist and undergraduate students. Data were obtained through interviews with a practicing earth scientist as well as five undergraduate students. Inquiry reports of the students were also analysed. The result showed that while the earth scientist conducted a case study with already-established models of typhoon, the students were enabled to work on the specific case of Wukong only after they learned general theories on typhoons. Background knowledge played an important role for the scientist and students to formulate scientific hypotheses. Both the earth scientist and undergraduate students generate multiple working hypotheses, and they considered a couple of conditions to select more plausible hypotheses, including theoretical coherence, causative processes, and consistency with empirical data. Despite these similarities, there were differences in the scope and depth of background knowledge between the scientist and students. In addition, it was not likely that the undergraduate students possessed explicit perceptions of the conditions which could make a hypothesis more probable, except for the empirical consistency. Implications for science education and relevant research were discussed.

• Journal of Science Education
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• v.48 no.1
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• pp.63-73
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• 2024

Knowing the learner's image of a subject-related occupation is good data for determining the direction of a teacher's teaching and learning. Existing drawing image analysis tools have the limitation that it takes a long time to analyze images and drawings of a scientist's appearance. The semantic differential method is a widely used method to analyze images of specific objects. However, research using the semantic differential method has the limitation of failing to reflect terms or factors that change over time by using the adjective pairs used in the initial study as they were in accordance with the research content. In this study, we use the semantic differential method to develop a tool to measure middle school students' scientist image and apply it to middle school students to discuss educational implications regarding the usefulness of measuring scientist image.

• Proceedings of the Korean Society of Toxicology Conference
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• 2006.05a
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• pp.47-53
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• 2006

• Journal of The Korean Association For Science Education
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• v.43 no.4
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• pp.351-368
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• 2023

The scientist-led Research and Education (R&E) program aims to strengthen gifted science students' research capabilities under the guidance of scientists. Students' actual research experiences in scientist-led R&E activities range from understanding how scientists conduct research to actively participating in research. To develop R&E that promotes student agency, i.e., student participation, this study aimed to identify the pedagogical characteristics that supported gifted science students' agentic participation in the scientist-led R&E program. We conducted interviews with learners and scientists in three teams undertaking R&E activities every three months. The interview covered their perceptions of R&E activities, student participation, and scientists' support for the activities. The recordings and transcripts of the interviews were used as primary data sources for the analysis. The trajectory of each team's activities, as well as the learners' and scientists' dynamic positioning were identified. Based on this analysis, we inductively identified the pedagogical characteristics that emerged from classes in which the scientists supported the students' learning and engagement in research. Regarding agency, three types of student participation were identified: 1) the sustained exercise of agency, 2) the initial exercise and subsequent discouragement of agency, and 3) the continuous non-exercise of agency. Two pedagogical characteristics that supported the learners' agentic participation were identified: 1) opportunities for students to take part in research management and 2) scientist-student interactions encouraging learners to present expert-level ideas. This study contributes to developing pedagogies that foster gifted science students' agentic participation in scientist-led R&E activities.

• 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.

• The Journal of Korean Association of Computer Education
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• v.23 no.3
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• pp.49-58
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• 2020

Organizing entrance evaluation system for selecting students who can become expert in data science field according to need of the age and social demand is important. This study was conducted for the purpose of analyzing data science field graduate school entrance evaluation system and deriving implications after taking into account the importance of talents possessing convergence competency. For this aim, a total of 22 graduate schools in 7 countries have been selected targeting data scientist postgraduate program around the world. The selected graduate schools have been analyzed based on qualifications, necessary skills prior to entrance, entrance conditions, and selection methods. As a result of the analysis, 'graduate school which I can apply for regardless of possessing undergraduate degree or undergraduate major (63.6 percent)' in qualifications category, 'graduate school which mentioned skills required in completing master's degree prior to entrance (63.6 percent)' in skills required prior to entrance category, 'graduate school which does not mention separate entrance condition (81.8 percent)' in entrance conditions category, and 'graduate school selecting students merely based on document screening (68.2 percent)' in selection methods category took the highest portion. Based on the above, this study summarized the results of the data scientist process and suggested implications for objectifying admission evaluation.

• Journal of The Korean Association For Science Education
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• v.43 no.4
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• pp.369-387
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• 2023

This study aims to interpret the experience of science research in a graduate school laboratory from the perspective of Gilles Deleuze's concepts of "agencement" and "becoming". The research was conducted as an autobiographical narrative inquiry. The research text is written in a way that tells the story of my science research experience and retells it from the perspective of Gilles Deleuze. In Deleuze's view, science research is a constantly flowing agencement. The science research agencement is composed of a mechanical agencement of various experimental tools-machines and researcher-machines as well as a collective agencement of speech acts such as biological knowledge, experiment protocols, and laboratory rules. Furthermore, science research agencement is fluid as events occur all over the agencement. Data, as a change occurring in the material dimension, is an event and sign that raises problems. It has the agency to influence agencement through an intersubjective relationship with researchers, and the meaning of data is generated in this process. The change of agencement compelled me to perform science practice. I have performed repeated science practice, meaning that my body has constantly been connected to other machines. As a result of this connection, my body has been affected, and the capacity of my body that constitutes the agencement has been augmented. In addition, I was able to be deterritorialized from the existing science research agencement and reterritorialized in a new science research agencement with data. This process of differentiation allowed me to becoming-scientist. In sum, this study provides implications for science practice-oriented education by exploring the process of becoming-scientist based on my science research experience.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.31-39
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• 2020

Big data analysis is analyzed and used in a variety of management and industrial sites, and plays an important role in management decision making. The job competency of big data analysis personnel engaged in management analysis work does not necessarily require the acquisition of microscopic IT skills, but requires a variety of experiences and humanities knowledge and analytical skills as a Data Scientist. However, big data education by state-run and state-run educational institutions and job education institutions based on the National Competency Standards (NCS) is proceeding in terms of software engineering, and this teaching methodology can have difficult and inefficient consequences for non-technical majors. Therefore, we analyzed the current Big Data platform and its related technologies and defined which of them are the requisite job competency requirements for field personnel. Based on this, the education courses for big data analysis and visualization techniques were organized for non-technical-based majors. This specialized curriculum was conducted by working-level officials of financial institutions engaged in management analysis at the management site and was able to achieve better educational effects The education methods presented in this study will effectively carry out big data tasks across industries and encourage visualization of big data analysis for non-technical professionals.