Source – https://www.theserverside.com/
Java devs looking to explore or work in data science may need another language up their sleeves. Python and R are common Java alternatives for data science.
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. “Python and R are both open source and free to use, giving them both a rich ecosystem and plenty of support from academic communities.”
Java developers who plan to explore data science calculation may do well to learn a little Python and R.
R and Python basics
The R programming language has implicit benefits when it comes to data science. 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. R is a specialized language, however, and it has limitations.
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. This makes it well-equipped for numerical and technical computing. Its appeal for data science is how quickly developers can get started with Python. “For data science experts looking to start writing application code, this is the most straightforward route,” said Simon Ritter, deputy CTO of Azul Systems, which develops Java runtimes.
While Java, as well as Kotlin and Scala, can be used for data science, it’s more likely to play a role behind the scenes, Ravisankar says.
“Java is not built for data science — most Java applications were built for web servers and large-scale distributed applications,” Ravisankar says. “Java is statically typed and strictly follows the object-oriented paradigm.”
In contrast, Python follows a multiprogramming paradigm, which makes it easy for developers to write concise code using syntactic sugar. Python was not built specifically for data science workloads, but it does include many features that make it easy to code against data science workloads such as read-eval-print loops, notebooks and math libraries.
The community and tools around Python and R have continued to grow, further cementing their lead in data science coding.