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What is Data Science ? Fundamental Concepts and a Heuristic Example

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Data Science, Classification, and Related Methods

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Data Science is not only a synthetic concept to unify statistics, data analysis and their related methods but also comprises its results. It includes three phases, design for data, collection of data, and analysis on data. Fundamental concepts and various methods based on it are discussed with a heuristic example.

The roundtable discussion “Perspectives in classification and the Future of IFCS” was held at the last Conference under the chairmanship of Professor H. -H. Bock. In this panel discussion, I used the phrase ‘Data Science’. There was a question, “What is ‘Data Science’? ” I briefly answered it. This is the starting point of the present paper.

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References

  • The following references are relevant to the various parts of this paper.

    Google Scholar 

  • Arabie, P., Hubert, L.J. and De Soete, G. (1996) ed.: Clustering and Classification, World Scientific.

    Google Scholar 

  • Benzécri, J.P. (1973): L’Analyse des Données,Dunod.

    Google Scholar 

  • Benzécri, J.P. (1992): Correspondence Analysis Hand-Book, Marcel Dekker.

    Google Scholar 

  • Bock, H.-H. and Polasek, W. (1996) ed.: Data Analysis and Information Systems, Springer.

    Google Scholar 

  • Borg, I. and Shye, S. (1995): Facet Theory, Form and Content, Advanced Quantitative Techniques in the Social Sciences Series 5, Sage Publication.

    Google Scholar 

  • Diday, E., J. Lemaire, J. Pouget and F. Testu (1983): Elements d’Analyse des Données,Dunod.

    Google Scholar 

  • Diday, E., G. Celeux, Y. Lechevallier, G. Govaert and H. Ralambondrainy (1989): Classification automatique et Analyse des Données: Méthodes et environment informatique,Dunod.

    Google Scholar 

  • Diday, E. and Y. Leschevallier (1991): Symbolic -Numeric Data Analysis and Learning, -Versailles Sept 91- Nova Science Publisher.

    Google Scholar 

  • Gaul, W. and Pferfer, D. (1996) ed: From Data to Knowledge, Springer.

    Google Scholar 

  • Guttman, L. (1994): Louis Guttman on Theory and Methodology: Selected Writings, Shlomit Levy ed, Dartmouth.

    Google Scholar 

  • Hayashi, C. (1956): Theory and example of quantification(II). Proc. Inst. Statist. Math., 3, 69–98.

    Google Scholar 

  • Hayashi, C. (1974):.Minimum dimensional analysis MDA. Behaviormetrika, 1, 1–24. Hayashi, C. (1976): Minimum dimensional analysis MDA-OR and MDA-UO, Essays in Probability and Statistics, Ikeda, S., et al. (eds.), 395–412, Shinko Tsusho Co. Ltd. Hayashi, C. ( 1993 ): Treatise on Behaviormetrics, Asakura Shoten.

    Google Scholar 

  • Hayashi, C. (1993): Quantification of Qualitative Data -Theory and Method -, Asakura Shoten.

    Google Scholar 

  • Hayashi, C. (1995): Changing and Enduring Aspects of Japanese National Character, The Institute of Social Research, Osaka, Japan.

    Google Scholar 

  • Hayashi, C. and Suzuki, T. (1986): Data Analysis in Social Surveys, Iwanami Shoten. The English version by Hayashi, C. Suzuki,T. and Sasaki, M., “Data Analysis for Comparative Social Research: International Perspectives” was published by Elsevier, North-Holland in 1992.

    Google Scholar 

  • Hayashi, C. and Hayashi, F. (1995): Comparative Study of National Character, Proceedings of the Institute of Statistical Mathematics Vol. 43, No. 1, 27–80.

    Google Scholar 

  • Jambu, M. (1989): Exploration Informatique et Statistique des Données, Dunod. Jambu, M. ( 1991 ): Exploratory and Multivariate Data Analysis, Academic Press.

    Google Scholar 

  • Lebart, L., Morineau, A. and Warwick, K.M. (1984): Multivariate Descriptive Statistical Analysis,John Wiley.

    Google Scholar 

  • Lebart, L. and Salem, A. (1988): Analyse Statistiques des Données, Textuelles, Dunod.

    Google Scholar 

  • Lebart, L. and Salem, A. (1994): Statistique Textuelle, Dunod.

    Google Scholar 

  • Lebart, L., Morineau, A. and Piron, M. (1995): Statistique Exploratoire Multidimensionnelle,Dunod.

    Google Scholar 

  • Van Cutsem, B. (1994): Classification and Dissimilarity Analysis, Springer.

    Google Scholar 

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Author information

Authors and Affiliations

  1. The Institute of Statistical Mathematics Sakuragaoka, Birijian 304 15-8 Sakuragaoka, Shibuya-ku Tokyo 150, Japan

    Chikio Hayashi

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  1. Chikio Hayashi
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Editors and Affiliations

  1. The Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106, Japan

    Chikio Hayashi , Noboru Ohsumi  & Yasumasa Baba ,  & 

  2. School of Management, Science University of Tokyo, 500 Shimokiyoku, Kuki, Saitama 346, Japan

    Keiji Yajima

  3. Institut für Statistik, Rheinisch-Westfälische Technische Hochschule (RWTH), D-52056, Aachen, Germany

    Hans-Hermann Bock

  4. Faculty of Environmental Science & Technology, Okayama University, 2-1-1 Tsushima-naka, Okayama 700, Japan

    Yutaka Tanaka

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© 1998 Springer Japan

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Hayashi, C. (1998). What is Data Science ? Fundamental Concepts and a Heuristic Example. In: Hayashi, C., Yajima, K., Bock, HH., Ohsumi, N., Tanaka, Y., Baba, Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65950-1_3

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  • DOI: https://doi.org/10.1007/978-4-431-65950-1_3

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-70208-5

  • Online ISBN: 978-4-431-65950-1

  • eBook Packages: Springer Book Archive

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