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• Generative AI can now execute many routine data science workflows including cleaning, summarizing, visualizing, modeling, and drafting reports.technical 90%
• The competencies that matter most in data science remain irreducibly human: problem formulation, measurement and design, causal identification, statistical and computational reasoning, ethics and accountability, and sensemaking.technical 90%
• Computing with data (GDS3 in Donoho's framework) has been substantially automated by Generative AI.technical 85%
• Data gathering, preparation, and exploration (GDS1) and science about data science (GDS6) still require essential human input despite Generative AI advances.limitation 85%
• Data science curricula should focus on the human core while teaching students how to work effectively within iterative prompt-output-prompt cycles using retrieval-augmented generation.method 90%
• Advances in Generative AI should sharpen, rather than diminish, the focus on human reasoning in data science education.result 90%
• Learning outcomes and assessments in data science education should explicitly evaluate reasoning and judgment.method 85%
• Generative AI reveals an irreducible human core at the center of data science.result 90%

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