Using Python, R, or Julia in Data Science Projects

Python is an interpreted, object-oriented, high-level, and multi-paradigm programming language. Python is a general-purpose programming language created in 1991 by Guido van Rossum as a successor to his previous language ABC.  For data analysis and interactive, exploratory computing, data visualization, and other data science operations, Python has been widely used. However, there are gaps that Python does not fill completely, such as scalability, implicitly parallelism, and performance (depends on the context). Created in 2009 and launched in 2012, Julia is an open-source, high-performance, high-level, and dynamically-typed programming language. It is a flexible dynamic language, appropriate for scientific and numerical computing, its performance is comparable to traditional statically-typed languages since it is compiled, unlike python that is interpreted. Why Not a Benchmark?

We can not forget the R language, it is a language for statistical computing and graphics (gnu project), R provides a wide variety of statistical, such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. The R is highly extensible through its packages. We do insert the R language in Benchmark or not?