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Area of Science:

  • Computer Science
  • Data Science
  • Human-Computer Interaction

Background:

  • AI coding assistants are popular but their utility in data science tasks is under-researched.
  • Exploring alternative analytical paths is crucial for robust data science conclusions.
  • The role of AI assistants in facilitating path exploration in data science is unknown.

Purpose of the Study:

  • To investigate how AI coding assistants impact data scientists' workflow.
  • To determine if AI assistants can support exploration of alternative data science paths.
  • To evaluate the acceptance and helpfulness of AI code recommendations, including alternatives.

Main Methods:

  • A mixed-methods study was conducted with data scientists using an AI coding assistant.
  • Quantitative analysis assessed acceptance and helpfulness of AI recommendations (including alternatives).
  • Qualitative insights were gathered on user interactions and challenges.

Main Results:

  • Including the data science step in prompts significantly improved recommendation acceptance.
  • The presence of alternative recommendations did not significantly impact acceptance or helpfulness.
  • Significant differences were observed in recommendation acceptance and usefulness between descriptive and predictive tasks.

Conclusions:

  • AI assistants can support data science tasks, with prompt engineering being key.
  • Current AI assistants may not effectively facilitate the exploration of diverse analytical paths.
  • User sentiment towards AI assistance in data science is generally positive.