Modern technology has many programming languages. But the question is, which one is best for a data scientist? Today, we’re going to take a closer look at the two programming languages popular among IT circles, namely Python and Julia, presented by Analytics Insight.
Python vs Julia
Julia is a multi-paradigm, mostly functional programming language designed for machine learning and statistical programming. Python is another multi-paradigm programming language used for machine learning, although Python is generally considered object-oriented.
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On the other hand, Julia is based more on the functional paradigm. This language is the new noise in the IT sector, mostly recognized for its speed, and is gaining popularity among data scientists.
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The benefits of Python
Created in 1991, Python is a programming language that is used for website development, software development, mathematics, and systematic scripting. In Python, the first element of an array is accessed with null, such as string  in Python for the first character in a string. This helps by way of adoption by a more general-purpose audience with ingrained programming habits.
Python is popular among developers because of its power, adaptability and understandable syntax that is easy to understand and master. Nearly 70% of developers say they use Python to build high-performance AI and ML algorithms for natural language processing and sentiment analysis. Python, along with R, is the preferred language for Data Science. The breadth and utility of Python’s third-party package culture remains one of the language’s biggest attractions, noted Analytics Insight.
Last but not least, Python is easier to accelerate. The mypyc project translates type-annotated Python to native C, much less clumsily than Cython. Typically, a fourfold performance improvement is achieved, and often much more for purely mathematical operations.
The benefits of Julia
Julia is a high-level, high-performance dynamic programming language. Many of its features are suitable for numerical analysis and computational science. Julia’s JIT compilation and type declarations mean that it can regularly outperform “pure”, unoptimized Python by orders of magnitude. Julia was designed to be faster from the start.
Julia’s primary target audience is users of scientific computing languages and environments such as Matlab, R, Mathematica, and Octave. The syntax of mathematical operations in Julia more closely resembles the way mathematical formulas are written outside the world of computers, making it easier for non-programmers to adopt.
Flux is a machine learning library for Julia that has many existing models for common use cases. Because it’s written entirely in Julia, it can be tweaked to suit the user’s needs and leverages Julia’s native just-in-time compilation to optimize projects from the inside out, Analytics Insight notes.
Julia is a high-level, high-performance dynamic programming language designed primarily for technical computing. The main advantages of Julia when dealing with complex data models are its simplicity, excellent performance and speed. But potential for converting Science’s formulaic language into code is an obstacle for scientists: Julia supports the use of Greek letters, which allows the direct use of mathematical formulas in code rather than translating such recipes into the coding language.