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Indonesia embraces the Data Science

Author

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  • Situngkir, Hokky

Abstract

The information era is the time when information is not only largely generated, but also vastly processed in order to extract and generated more information. The complex nature of modern living is represented by the various kind of data. Data can be in the forms of signals, images, texts, or manifolds resembling the horizon of observation. The task of the emerging data sciences are to extract information from the data, for people gain new insights of the complex world. The insights may come from the new way of the data representation (function of R(x) over data x), be it a visualizations, mapping, or other. The insights may also come from the implementation of mathematical analysis and/or computational processing (function A(x) over data x) giving new insights of what the states of the nature represented by the data. Both ways implement the methodologies reducing the dimensionality of the data. The relations between the two functions, R(x) and A(x) are the heart of how information in data is transformed mathematically and computationally into new information. The paper discusses some practices, along with various data coming from the social life in Indonesia becoming the variables within R(x) and A(x) to gain new insights about Indonesia in the emerging data sciences. The data sciences in Indonesia has made Indonesian Data Cartograms, Indonesian Celebrity Sentiment Mapping, Ethno-Clustering Maps, social media community detection, and a lot more to come, become possible. All of these are depicted as the exemplifications on how “data science” has become integral part of the technology bringing data closer to people.

Suggested Citation

  • Situngkir, Hokky, 2015. "Indonesia embraces the Data Science," MPRA Paper 66048, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:66048
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    References listed on IDEAS

    as
    1. William S. Cleveland, 2001. "Data Science: an Action Plan for Expanding the Technical Areas of the Field of Statistics," International Statistical Review, International Statistical Institute, vol. 69(1), pages 21-26, April.
    2. Situngkir, Hokky, 2011. "Spread of hoax in Social Media," MPRA Paper 30674, University Library of Munich, Germany.
    3. John H. Miller & Scott E. Page, 2007. "Social Science in Between, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    4. Scott E. Page, 2007. "Prologue to The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies," Introductory Chapters, in: The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies, Princeton University Press.
    5. Situngkir, Hokky & Surya, Yohanes, 2004. "Neural network revisited: perception on modified Poincare map of financial time-series data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 100-103.
    6. Hokky Situngkir & Yohanes Surya, 2005. "On Stock Market Dynamics through Ultrametricity of Minimum Spanning Tree," Macroeconomics 0505010, University Library of Munich, Germany.
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    Cited by:

    1. Situngkir, Hokky & Maulana, Ardian & M. Dahlan, Rolan, 2015. "A Portrait of Diversity In Indonesian Traditional Cuisine," MPRA Paper 68385, University Library of Munich, Germany.

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    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • G0 - Financial Economics - - General
    • I2 - Health, Education, and Welfare - - Education
    • M0 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General
    • Z1 - Other Special Topics - - Cultural Economics

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