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Data science as a language: challenges for computer science-a position paper.

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Area of Science:

  • Computer Science
  • Data Science
  • Scientific Research

Background:

  • Data Science is increasingly integral to modern scientific inquiry.
  • Computer Science currently lacks a formal language for datafied sciences.
  • Existing frameworks for supervised learning are robust, but unsupervised learning foundations require development.

Purpose of the Study:

  • To propose Data Science as a language for datafied sciences, analogous to mathematics in physics.
  • To identify and discuss three fundamental challenges for Computer Science arising from this perspective.
  • To explore potential research directions within Computer Science to address these challenges.

Main Methods:

  • Conceptual analysis framing Data Science as a computational language.
  • Identification of three core challenges in Computer Science related to Data Science.
  • Discussion of existing Computer Science research directions as potential solutions or inspirations.

Main Results:

  • Data Science is conceptualized as a Computer Science language for datafied sciences.
  • Three primary challenges are identified: (1) defining the language of computation, (2) establishing robust foundations for unsupervised learning, and (3) developing conceptual toolkits for data-driven scientific questions.
  • Algorithmic Information Theory is highlighted as a potential foundation for unsupervised learning, though lacking uncertainty measures.

Conclusions:

  • Viewing Data Science as a language presents significant, untapped research problems for Computer Science.
  • Addressing these challenges is crucial for advancing datafied sciences.
  • The interdisciplinary nature of these problems offers exciting opportunities for Computer Scientists.