IDEAS home Printed from https://ideas.repec.org/a/wsi/nmncxx/v13y2017i02ns1793005717400038.html
   My bibliography  Save this article

Data Science: A New Paradigm in the Age of Big-Data Science and Analytics

Author

Listed:
  • Claude E. Concolato

    (Department of Computer Science and Information Technology, University of District of Columbia, Washington, DC 20008, USA)

  • Li M. Chen

    (Department of Computer Science and Information Technology, University of District of Columbia, Washington, DC 20008, USA)

Abstract

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

Suggested Citation

  • Claude E. Concolato & Li M. Chen, 2017. "Data Science: A New Paradigm in the Age of Big-Data Science and Analytics," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 119-143, July.
    • Handle: RePEc:wsi:nmncxx:v:13:y:2017:i:02:n:s1793005717400038
    DOI: 10.1142/S1793005717400038
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S1793005717400038
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S1793005717400038?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhaohao Sun & Paul P. Wang, 2017. "Big Data, Analytics, and Intelligence: An Editorial Perspective," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 75-81, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Situngkir, Hokky, 2015. "Indonesia embraces the Data Science," MPRA Paper 66048, University Library of Munich, Germany.
    2. Costa, Carlos & Santos, Maribel Yasmina, 2017. "The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age," International Journal of Information Management, Elsevier, vol. 37(6), pages 726-734.
    3. Daphne R. Raban & Avishag Gordon, 2020. "The evolution of data science and big data research: A bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1563-1581, March.
    4. Nils Hachmeister & Katharina Weiß & Juliane Theiß & Reinhold Decker, 2021. "Balancing Plurality and Educational Essence: Higher Education Between Data-Competent Professionals and Data Self-Empowered Citizens," Data, MDPI, vol. 6(2), pages 1-15, January.
    5. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
    6. Ulrich Rendtel & Willi Seidel & Christine Müller & Florian Meinfelder & Joachim Wagner & Jürgen Chlumsky & Markus Zwick, 2022. "Statistik zwischen Data Science, Artificial Intelligence und Big Data: Beiträge aus dem Kolloquium „Make Statistics great again“ [Statistics between data science, artificial intelligence and big da," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(2), pages 97-147, June.
    7. Stephan R. Sain, 2023. "Data science and climate risk analytics," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    8. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    9. Shalini R. Urs & Mohamed Minhaj, 2023. "Evolution of data science and its education in iSchools: An impressionistic study using curriculum analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(6), pages 606-622, June.
    10. Göran Kauermann & Helmut Küchenhoff, 2016. "Statistik, Data Science und Big Data [Statistics, data science, and big data]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 141-150, October.
    11. Jennifer Lewis Priestley & Robert J. McGrath, 2019. "The Evolution of Data Science: A New Mode of Knowledge Production," International Journal of Knowledge Management (IJKM), IGI Global, vol. 15(2), pages 97-109, April.
    12. Marynia Kolak, 2018. "Ian Foster, Rayid Ghani, Ron S Jarmin, et al. (eds), Big data and social science: A practical guide to methods and tools," Environment and Planning B, , vol. 45(2), pages 388-389, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:nmncxx:v:13:y:2017:i:02:n:s1793005717400038. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/nmnc/nmnc.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.