What are the best tools for Data science & ML engineering?

    Asked "What are the best tools for Data science & ML engineering?", Perplexity, Gemini, Google AI Mode, ChatGPT and Copilot named 103 distinct tools across 15 answers on September 7, 2026, and 64 of them in two or more answers.

    Measured acrossPerplexityGeminiGoogle AI ModeChatGPTCopilot

    64 of 103 names confirmed · named in 2 or more of 15 answers · asked September 7, 2026 · 5 engines

    The best tools for Data Science and ML engineering include Python, R, SQL, and key libraries like Pandas, NumPy, Scikit-learn, and TensorFlow, as well as cloud platforms like Azure, Google Cloud, and Amazon SageMaker. Additionally, tools like Jupyter Notebook, VS Code, and Docker are essential for development, deployment, and orchestration. Experiment tracking tools like MLflow and data versioning tools like DVC are also crucial for reproducibility and scalability.

    • 1Pandasnamed in 14 of 15 answers
    • 2Scikit-learnnamed in 14 of 15 answers
    • 3PyTorchnamed in 14 of 15 answers
    • 4Pythonnamed in 12 of 15 answers
    • 5TensorFlownamed in 12 of 15 answers
    • 6NumPynamed in 11 of 15 answers
    • 7MLflownamed in 11 of 15 answers
    • 8Apache Sparknamed in 10 of 15 answers
    • 9JupyterLabnamed in 9 of 15 answers
    • 10Matplotlibnamed in 9 of 15 answers
    • 11Seabornnamed in 9 of 15 answers
    • 12Polarsnamed in 8 of 15 answers
    • 13Jupyter Notebooknamed in 7 of 15 answers
    • 14Plotlynamed in 7 of 15 answers
    • 15Snowflakenamed in 7 of 15 answers
    • 16VS Codenamed in 6 of 15 answers
    • 17Kerasnamed in 6 of 15 answers
    • 18Kubeflownamed in 6 of 15 answers
    • 19Tableaunamed in 6 of 15 answers
    • 20dbtnamed in 6 of 15 answers
    • 21Rnamed in 5 of 15 answers
    • 22SQLnamed in 5 of 15 answers
    • 23Databricksnamed in 5 of 15 answers
    • 24Amazon SageMakernamed in 5 of 15 answers
    • 25XGBoostnamed in 5 of 15 answers
    78 more names were ranked on this question.
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    The full measurement

    • The position each of the 5 engines gave all 103 names.
    • How many of the 15 answers named each of them.
    • 371 sampled observations behind this ranking, and where the engines disagree.
    • Fan-out — the query each engine actually searched.
    • Every citation, and the sources nobody cited.
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