Python has turned into a data science and machine learning mainstay, while Julia was built from the ground up to do the job.
Of the many use cases Python covers, data analytics has become perhaps the biggest and most significant. The Python ecosystem is loaded with libraries, tools, and applications that make the work of scientific computing and data analysis fast and convenient.
But for the developers behind the Julia language — aimed specifically at “scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing”—Python isn’t fast or convenient enough. It’s a trade-off, good for some parts of this work but terrible for others.
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