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How Data are Classified: Numerical Data00:59

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Behavioral Assessment of Manual Dexterity in Non-Human Primates
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Quant Data Science meets Dexterous Artistry.

Ivo D Dinov1

  • 1Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, Department of Computational Medicine and Bioinformatics, Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA.

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Data Science requires consensus on core methods for handling big data. Collaboration and investment are crucial for advancing data science education and workforce skills to meet growing demands.

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Area of Science:

  • Data Science as an interdisciplinary field connecting science, applied disciplines, and the arts.

Background:

  • Established demand for novel data science methods.
  • Lack of agreement on core aspects of representation, modeling, and analytics for large, heterogeneous datasets.

Purpose of the Study:

  • To highlight the need for community consensus on data science education and training.
  • To identify prerequisites and learning outcomes for addressing big data challenges.

Main Methods:

  • This study is a conceptual analysis and synthesis of the current state of data science.

Main Results:

  • Disagreement persists on fundamental data science principles for big data.
  • A need exists for standardized curricula, prerequisites, and competency outcomes in data science education.

Conclusions:

  • Achieving consensus in data science education is vital.
  • Stakeholders must foster innovation, support high-risk research, expand technological capacity, and enhance workforce skills to meet the increasing demand for data analytics.