Why Java devs should switch to Python or R for data science | TheServerSide

"Java can handle large workloads, and even if it hits limitations, peripheral JVM languages such as Scala and Kotlin can pick up the slack. But in the world of data science, Java isn't always the go-to platform. The front end of data science has recently been dominated by the languages Python and R, says Vivek Ravisankar, CEO and co-founder of HackerRank, a developer skills platform."

"Developed by statisticians for statisticians, R was designed to make data analysis and statistics easier to do, said Maria Khalusova, developer advocate at JetBrains. R has a number of unique statistics packages, and its matrix calculation capabilities are quite strong compared to Java. R is often praised for its rich ecosystem, specifically around data visualization and specialized statistical methods. It is popular among folks who started their careers in statistics and advanced analytics."

"As a general-purpose language, Python has an advantage over R, Khalusova said. Python is more production-friendly, and it's easier to learn -- both for beginners and those who switch to it from other programming languages. That accessibility may be why Python has been able to grow its rich data science ecosystem so rapidly. Python supports a number of advanced machine learning libraries and frameworks, such as scikit-learn and TensorFlow. Python is also backed by the mature SciPy stack, which includes NumPy, SciPy, Matplotlib and pandas."

Java can handle large workloads and peripheral JVM languages like Scala and Kotlin can mitigate some limitations, but Java is not the primary choice for front-end data science. Python and R lead the front end due to open-source access, academic support, and rich ecosystems. R, created by statisticians, provides specialized statistical packages, strong matrix calculations, and excellent data-visualization and specialized-method support, though it is more specialized and has limitations. Python, as a general-purpose language, is production-friendly, easier to learn, and supports major machine learning libraries and the mature SciPy stack, enabling fast numerical and technical computing and rapid development.

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