Ten Research Challenge Areas in Data Science

Authors

  • Jayesh Dattatray Kharat  Department of Computer Engineering, Zeal Collage Engineering and Research Pune, Maharashtra, India
  • Paurnima Kawale  Department of Computer Engineering, Zeal Collage Engineering and Research Pune, Maharashtra, India
  • Rashmi Ashtagi  Department of Computer Engineering, Zeal Collage Engineering and Research Pune, Maharashtra, India

Keywords:

artificial intelligence, causal reasoning, computing systems, data life cycle, deep learning, ethics, machine learning, privacy, trustworthiness

Abstract

To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society. We preface our enumeration with meta-questions about whether data science is a discipline. We then describe each of the 10 challenge areas. The goal of this article is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving.

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International Journal of Scientific Research in Science and Technology

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Published

2022-03-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

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How to Cite

[1]
Jayesh Dattatray Kharat, Paurnima Kawale, Rashmi Ashtagi "Ten Research Challenge Areas in Data Science " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 2, pp.462-469, March-April-2022.