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🏛 | [Yokohama Mayor Election] Mr. Yamanaka to be a candidate for unification of the opposition party, adjusted by the Constitutional Democratic Party


Photo Mr. Yamanaka who announced his intention to run for the mayor of Yokohama = June 6, Naka-ku, Yokohama

[Mayor of Yokohama election] Mr. Yamanaka is nominated as a candidate for unification of the opposition party

 
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One of the prefectural councils said, "Because my specialty is data science, I can objectively analyze it numerically.
 

Takeharu Yamanaka (8), a professor at Yokohama City University, was selected as a casino in the election of the mayor of Yokohama (announced on August 8th, vote counting on 22nd) due to the expiration of his term. → Continue reading

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Wikipedia related words

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Specialized field

Data science

Data science(English: data science, abbreviation: DS) orData science[1][2]Is an approach that seeks to use data to derive new scientific and socially beneficial knowledge, in which data is handled in information science, statistics, and so on.algorithmAnd so on.

Let's take a bird's eye view of data science from a statistical, computational, and human perspective.Each perspective is an essential aspect of data science, but the organic combination of these three perspectives is the essence of the discipline of data science (Blei and Smyth, 3).[3]).Lack of awareness of the importance of field knowledge in data analysis to date is believed to be the source of widespread misunderstandings about the discipline of data science (Hernan, Hsu and Healy, 2018).[4]).

Data science has a clear application context, has a transdisciplinary aspect, requires clear social accountability for research results, and is for qualitative assurance of research results. Requires additional criteria for quality control in addition to traditional sitting criteria.Heterogeneity of organizations is also important for the effective promotion of data science.Science that meets these requirements is Mode 2 science claimed by Gibbons et al.[5]Can be recognized as a kind of.

The methods used in data science are diverse and as fieldsMath,statistics,Computer science,Information engineering,Pattern recognition,Machine learning,Data Mining,Database,VisualizationAnd so on.

Data science researchers and practitioners are called data scientists.

As an application of data science,Biology,Medical science,engineering,Economics,Sociology,HumanitiesAnd so on.

History

The term data science has been used for a long time, especially in the 1960s.Peter NaurIt attracted attention because it was used by (Note: The English version also says 1960, but the source is unknown. For the following 1974 book, the official web page by himself[6]Can be confirmed from). In his 1974 book, "Concise Survey of Computer Methods," Naur used the term data science in describing data processing methods and their applications.

Since the latter half of the 2010s, there is a global shortage of data scientists, so the development of systems that can analyze even users without advanced knowledge is progressing.[7].

Meanwhile, in 2012,Harvard Business ReviewThe magazine "The coolest job in the 21st century"[8]The word "data science" isBuzz wordSome people see it as.ForbesEven in magazines, there is no clear definition, so learn at graduate school(English editionWas criticized for being simply replaced.[9]

Income equality

Income equality is increasing in both developed and developing countries with good data science skills.There is a negative correlation between the country's average skill capacity across domains and the percentage of income held by the top 10% of countries.[10].

Related item

note

[How to use footnotes]
  1. ^ Hiroe Tsubaki "Systems science and data science"Horizontal Trunk," Vol. 14, No. 1, Transverse Core Science and Technology Research Association, 2020, pp. 64-69, two:10.11487 / trafst.14.1_64, ISSN 1881-7610 , NOT 130007855120.
  2. ^ Okazaki, Intuition "Data Journalism and Data Science"Journal of the Institute of Electronics, Information and Communication Engineers, Vol. 99, No. 4, 2016, p. 339," ISSN 0913-5693 , NOT 40020802401.
  3. ^ Smyth, Padhraic; Blei, David M. (2017-08-15). “Science and data science” (English). Proceedings of the National Academy of Sciences 114 (33): 8689-8692. two:10.1073 / pnas.1702076114. ISSN 1091-6490 . PMID 28784795. https://www.pnas.org/content/114/33/8689. 
  4. ^ Healy, Brian; Hsu, John; Hernán, Miguel A. (2018-04-28). Data science is science's second chance to get causal inference right: A classification of data science tasks. https://arxiv.org/abs/1804.10846. 
  5. ^ Baber, Zaheer; Gibbons, Michael; Limoges, Camille; Nowotny, Helga; Schwartzman, Simon; Scott, Peter; Trow, Martin (1995-11). “The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies.”. Contemporary Sociology 24 (6): 751. two:10.2307/2076669. ISSN 0094-3061 . https://doi.org/10.2307/2076669. 
  6. ^ Peter Naur: Concise Survey of Computer Methods, 397 p. Studentlitteratur, Lund, Sweden, 1974, ISBN-91 44-07881-1
  7. ^ "NEC Develops" Predictive Analytics Automation Technology "to Automat Large-Scale Data Prediction in Business Systems" (Press Release), NEC Corporation, (March 2016, 12), https://jpn.nec.com/press/201612/20161215_06.html 2021/7/15Browse. 
  8. ^ (Oct 2012), Data Scientist: The Sexiest Job of the 21st Century, , https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ 
  9. ^ "Data Science: What's The Half-Life Of A Buzzword?". Forbes (September 2013, 8). 2019/6/8Browse.
  10. ^ "Announcing the Coursera 2020 Global Skills Index" (English). Coursera Blog (September 2020, 7). 2020/11/11Browse.

Reference books for learning

  •  Kodansha Data Science Introductory Series
  • Daniela Calvetti and Erkki Somersalo: "Mathematics of Data Science: A Computational Approach to Clustering and Classification", SIAM, ISBN 978-1-611976-36-6 (2020).

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