The Best Programming Language for Data Science: Python vs Julia vs R – Analytics Insight

searching for the best programming language for data science

Programming language is practically the backbone of data science and in the modern advancement of technology, we have a lot of languages available at our expense. But the question is which one among them is the most suitable for a data scientist. Currently, the latest group of programming languages that data scientists use are Python, Julia, and R. All these languages have their unique attributes and they also have areas of expertise. For example, the Python ecosystem is loaded with libraries, tools, and applications that make the work of scientific computing and data analysis fast and convenient, but Julia aims to give scientists and data analysts not only fast and convenient development but also blazing execution speed. On the other hand, R language enhances the speed of statistical computing like no other.

Advantages of Python

Released in 1991, Python is a programming language that is used for web development, software development, mathematics, and systematic scripting. In Python, the first element of an array is accessed with a zero such as string [0] in Python for the first character in a string. It helps in the way of adoption by a more general-use audience with ingrained programming habits. Python has a faster startup which keeps it ahead of Julia and R. The breadth and usefulness of Python’s culture of third-party packages remains one of the language’s biggest attractions. Aside from gaining improvements to the Python interpreter (including improvements to multi-core and parallel processing), Python has become easier to speed up. The mypyc project translates type-annotated Python into native C, far less clunkily than Cython. It typically yields four-fold performance improvements, and often much more for pure mathematical operations.

Advantages of Julia

First appearing in 2012, Julia is a high-level, high-performance, dynamic programming language. While it is a general-purpose language and can be used to write any application, many of its features are well suited for numerical analysis and computational science. Julia’s JIT compilation and type declarations mean it can routinely beat “pure,” unoptimized Python by orders of magnitude. Python can be made faster by way of external libraries, third-party JIT compilers (PyPy), and optimizations with tools like Cython, but Julia is designed to be faster right out of the gate. A major target audience for Julia is users of scientific computing languages and environments like Matlab, R, Mathematica, and Octave. Julia’s syntax for math operations looks more like the way math formulas are written outside of the computing world, making it easier for non-programmers to pick up on. Flux is a machine learning library for Julia that has many existing model patterns for common use cases. Since it’s written entirely in Julia, it can be modified as needed by the user, and it uses Julia’s native just-in-time compilation to optimize projects from the inside out.

Advantages of R

First released in 1993, R is a programming language and free software environment for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is available under an open-source license, which means that anyone can download and modify the code. This freedom is often referred to as “free as in speech.” R is also available free of charge. Anybody can access the source code, modify it, and improve it. As a result, many excellent programmers have contributed improvements and fixes to the R code. For this reason, R is very stable and reliable. R performs a wide variety of functions, such as data manipulation, statistical modeling, and graphics. The one really big advantage of R, however, is its extensibility. Developers can easily write their software and distribute it in the form of add-on packages.

From the above study, it becomes clear that it is impossible to choose one best language as the best performance of the language depends mostly on the field of work it is being used to. So the data scientist must choose the suitable language as per the nature and requirements of his work.