Go is a strong fit for data science when you need high-performance production systems, but Python remains the primary choice for exploratory analysis and model training due to its richer ecosystem. Use Go to build scalable data pipelines, serve trained models via high-throughput HTTP APIs, or handle concurrent data processing tasks where Python's Global Interpreter Lock (GIL) becomes a bottleneck. For most data scientists, the optimal workflow involves training models in Python and deploying them using Go services.
Go for Data Science
Is It a Good Fit
Go excels at deploying and scaling data science models in production but is less suitable for initial model training compared to Python.