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A Guide to Teaching Data Science.

Stephanie C Hicks1,2, Rafael A Irizarry1,2

  • 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA.

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|May 21, 2019
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Summary
This summary is machine-generated.

Data science education needs an update, prioritizing real-world applications and practical experience. A case-study approach teaches essential skills like creating, connecting, and computing for data science success.

Keywords:
active learningapplied statisticscomputingdata sciencereproducibilityteaching principles

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Area of Science:

  • Statistics and Data Science Education

Background:

  • Increasing demand for data science training exceeds traditional statistics curricula.
  • Existing recommendations emphasize increased computing but often overlook practical applications.

Purpose of the Study:

  • To propose a revised data science curriculum prioritizing applications and practical problem-solving.
  • To provide a guide for developing and teaching an introductory data science course based on case studies.

Main Methods:

  • Developed and taught a graduate-level data science course centered on real-world case studies.
  • Focused on cultivating statistical thinking and key data science skills: creating, connecting, and computing.

Main Results:

  • The case-study approach effectively teaches students essential data science skills.
  • The developed guide offers practical insights for educators and statisticians.

Conclusions:

  • Data science education should integrate applications and practical experience.
  • A case-study-driven curriculum fosters crucial skills for data science professionals.