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データサイエンス教育に関するいくつかの提言――データサイエンスとマイ・ティーチング・ポートフォリオの対比から――
https://doi.org/10.60171/00003077
https://doi.org/10.60171/00003077bc103b79-e549-4947-8415-c2a384c991a6
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
54-02.pdf (507.4 kB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2023-03-15 | |||||||||
| タイトル | ||||||||||
| タイトル | データサイエンス教育に関するいくつかの提言――データサイエンスとマイ・ティーチング・ポートフォリオの対比から―― | |||||||||
| 言語 | ja | |||||||||
| タイトル | ||||||||||
| タイトル | Some Suggestions of the Data Processing―― From the Contrast between the Data Science and My Teaching Portfolio | |||||||||
| 言語 | en | |||||||||
| 言語 | ||||||||||
| 言語 | jpn | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | AI Artificial Intelligence | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | データサイエンス Data Science | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | データサイエンティスト Data Sicientist | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | マイ・ティーチング・ポートフォリオ My Teaching Portforio | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | ビッグデータ Big Data | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | 自己点検 Self-Inspection | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | 第三者評価 Third Party Eevaluation | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | PDCA Plan-Do-Check-Action | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | departmental bulletin paper | |||||||||
| ID登録 | ||||||||||
| ID登録 | 10.60171/00003077 | |||||||||
| ID登録タイプ | JaLC | |||||||||
| 著者 |
古川, 博仁
× 古川, 博仁
× Furukawa, Hirohito
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | The Information-related subjects in charge by the author were just subjects that was popular at that time in the area of information technology innovation in university education，and I couldn't get a bird's eye view of the future direction of the curriculum based on the "Principles of Data Science" presented in 1974. For the author, it is able to be regarded a regional revitalization study targeting a local government called Kure City to take in data from the Internet and analyze it over a year over the "vacant house problem" in 2014 as the research close to data science. It spent a considerable amount of time to select valid attributes from source data fetched from the Internet. Also, machine learning was not performed here. Data science is as long as science, it has two sides: a deductive method, that is systematic consistency based on logical reasoning and an inductive method, that is empirical demonstrability based on experiments and observations. Data scientists were able to be said that they have cultivated cultivate the two-sided background. From a deductive standpoint, in the field of data science, especially in the field of computer science, data scientists need the ability to find algorithms from theory and program them. When the Internet becomes widespread and handles data that serves its purpose, the provider should clearly indicate the accuracy or reliability of the data. In data analysis by fieldwork, it is normal to perform modeling to capture the phenomenon and deepen the understanding of the phenomenon. In an inductive method, such a method is essential for big data analysis. There are many "basic researches" that ignore actual interests and silently accumulate research. If data science is for profit, such efforts tend to be shunned. I think this is the turning point between data science and "basic research.” If the main focus is on actual profits, I think it is preferable to call data engineering rather than the term data science. Data science is mainly responsible for scientifically analyzing the data of the DC part of turning PDCA, which is indispensable for the administration of individual organizations. Data science has the role of scientifically analyzing the data of the DC part mainly in order to turn PDCA, which is indispensable for the administration of individual organizations. Organizations that handle big data provide evidence to ensure its public nature, regularly prepare reports for self-inspection and are necessary to undergo mutual evaluation by a third-party evaluator or between peers. I suggest that the certifications qualified there should be published to society. In education and literacy, I propose to students. Students should learn the PDCA cycle of the organization, and how to realize data science as they turn the PDCA cycle to achieve the purpose of the organization. It is essential for them to be educated from the perspective of what mutual evaluation should be. |
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| 書誌情報 |
広島文化学園短期大学紀要 巻 54, p. 9-18, 発行日 2021-12-25 |
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| 出版者 | ||||||||||
| 出版者 | 広島文化学園短期大学 | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 18846769 | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA12454339 | |||||||||
| フォーマット | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | application/pdf | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | VoR | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
