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Quant data science meets dexterous artistry

International Journal of Data Science and Analytics volume 7, pages 81–86 (2019)Cite this article

Abstract

Data science is a bridge discipline connecting fundamental science, applied disciplines, and the arts. The demand for novel data science methods is well established. However, there is much less agreement on the core aspects of representation, modeling, and analytics that involve huge and heterogeneous datasets. The scientific community needs to build consensus about data science education and training curricula, including the necessary entry matriculation prerequisites and the expected learning competency outcomes needed to tackle complex Big Data challenges. To meet the rapidly increasing demand for effective evidence-based practice and data analytic methods, research teams, funding agencies, academic institutions, politicians, and industry leaders should embrace innovation, promote high-risk projects, join forces to expand the technological capacity, and enhance the workforce skills.

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Affiliations

  1. Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, 426 N. Ingalls Str., Ann Arbor, MI, 48109, USA

    Ivo D. Dinov

  2. Department of Computational Medicine and Bioinformatics, University of Michigan, 426 N. Ingalls Str., Ann Arbor, MI, 48109, USA

    Ivo D. Dinov

  3. Michigan Institute for Data Science, University of Michigan, 426 N. Ingalls Str., Ann Arbor, MI, 48109, USA

    Ivo D. Dinov

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  1. Ivo D. Dinov
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Correspondence to Ivo D. Dinov.

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Dinov, I.D. Quant data science meets dexterous artistry. Int J Data Sci Anal 7, 81–86 (2019). https://doi.org/10.1007/s41060-018-0138-6

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  • DOI: https://doi.org/10.1007/s41060-018-0138-6

Keywords

  • Data science
  • Predictive analytics
  • Artistry
  • Qualitative and quantitative skills
  • Big data