Data Science Life Cycle: A disciplined approach to Data Science


This post was originally published by Kevin Allen at Towards Data Science

A life cycle approach encourages and enables a unification of views regarding data science and gives us a footing from which to adapt and evolve the practice and teaching of data science to research projects and to institutional strengths. There are commonalities to nearly all data science efforts, for example, data wrangling, data inference, code writing, artefact creation and sharing. A common intellectual framework can facilitate knowledge sharing about data science as a discipline across different the fields and domains using data science methods in their research.

  1. Organization into an understandable form.
  2. Reliance upon the test of experience as the ultimate standard of validity.

Without a flexible yet unified overarching framework we risk missing opportunities for discovering and addressing research issues within data science and training students in effective scientific methodologies for reliable and transparent data-enabled discovery. Data science brings new research topics, for example, computational reproducibility; ethics in data science; cyberinfrastructure and tools for data science. Without the Data Science Life Cycle approach, we risk an implementation of data science that too closely hews to a view that reflects the perspective of a particular discipline and could miss opportunities to share knowledge on data science research and teaching broadly across disciplines. In addition, a Data Science Life Cycle approach can give university leadership a framework to leverage their existing resources on campus as they strategize support for a cross-disciplinary data science curriculum and research agenda. The life cycle approach allows data science research and curriculum efforts to support the development of a scientific discipline, enabling progress toward fulfilling Tukey’s three criteria for a science.

1. Berman, F. et al. Realizing the potential of data science. Commun. ACM 61, 4, (Apr. 2018), 67–72;

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This post was originally published by Kevin Allen at Towards Data Science

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