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An Experience-Centered Approach to Training Effective Data Scientists.

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Data science education should mirror medical residencies, focusing on practical problem-solving with real-world data. This hands-on approach ensures graduates gain essential professional skills for data science careers.

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Area of Science:

  • Data Science Education
  • Applied Computer Science
  • Professional Training

Background:

  • Data science is an applied field, yet education often emphasizes theory over practical application.
  • Current data science curricula use simplified datasets, not reflecting real-world professional challenges.

Purpose of the Study:

  • Propose a novel data science master's program inspired by medical residencies.
  • Enhance data science training to better align with professional practice.

Main Methods:

  • Implement a residency-style model for data science master's students.
  • Integrate practical, project-based work with industry, government, or nonprofit partners.
  • Supplement practical experience with coursework in data science theory and methods.

Main Results:

  • Students will gain practical experience with authentic data and professional constraints.
  • The proposed model addresses gaps in current data science education.
  • The program can be adapted for shorter formats and to augment existing master's programs.

Conclusions:

  • A residency-inspired model provides a more realistic and effective data science education.
  • This approach equips students with crucial skills for professional data science practice.
  • Adapting this model enhances the professional relevance of data science training across disciplines.