What is Julia’s programming language? What are its uses? Is Python or Julia better? What are the differences between these two languages? Is the claim that Julia is the future of machine learning and data science true? All these questions are answered in this article.
Maybe you are active in the field of machine learning and data science with Python and you want to know if you need to expand your knowledge and learn Julia programming language. If you are a person interested in machine learning and data science and between Python and Julia and other similar languages, you don’t know which language to learn, this article is for you.
Table of Contents
History of the Julia programming language
It was in 2009 that four MIT students, Jeff Besanson, Stephen Karpinski, Viral Shah, and Alan Edelman, decided to create a new programming language. A language that combines the speed and efficiency of languages like C and Fortran with the simplicity of Python and R.
In other words, the goal of the creators of Julia’s language was to gather the strengths of different languages into one language.
In February 2012, the first public version of the Julia language was announced and made available to programmers as open-source.
The Julia language was continuously improved and new features were added over the following years.
In 2022, the 10th anniversary of Julia’s introduction, its official website announced that the language had been downloaded almost 35 million times, which is almost 5 times the cumulative downloads of the previous three years. This shows that the popularity of the Julia language is growing.
What is Julia’s programming language?
Julia is a high-level, dynamic, fast, and easy programming language specifically designed for scientific computing, numerical analysis, machine learning, and data science. Problems have become.
Features of the Julia language
One of the main attractions of the Julia programming language is undoubtedly its speed. This language uses a just-in-time (JIT) compiler, which increases its execution speed. However, the JIT compiler still has infrastructure problems where the first execution of the code is delayed. According to Julia’s documentation, these issues are being tracked and fixed.
The basics of programming with Julia are simple and clear. Also, this language, like Python, has wide applications.
Julia is a new language. Therefore, this language is not yet popular enough like other languages like Python and has a small ecosystem. Of course, we should not forget that Julia is still growing and its popularity is expected to increase in the coming years.
The Julia programming language is very attractive to people who are not professional programmers but need mathematical and numerical analysis in their work (such as mathematicians). This appeal is because the syntax of mathematical operation codes in Julia is simple and similar to the way it is used in the non-computer world and ordinary mathematics. As a result, using Julia to perform mathematical calculations is easier for non-programmers than Python.
Who uses Julia?
Julia’s programming language is used in various scientific and industrial fields. One of the main applications of Julia is in scientific and numerical calculations that require high performance and high accuracy. Because of its processing speed and high performance, Julia is very suitable for big data analysis, complex simulations, and precise numerical calculations. For example, in physics, chemistry, computational biology, and engineering, Julia is used to perform complex simulations and modeling.
In addition, Julia is also used in the field of machine learning and artificial intelligence. Due to its high speed and the ability to perform complex matrix calculations, this language is very suitable for training machine learning models and neural networks. Due to Julia’s easy interoperability with other languages such as Python, R, and C, as well as the presence of diverse and powerful libraries, this language has become a popular choice for data researchers and engineers.
Python programming language
Python programming language was introduced in the early 90s. The goal of Guido van Rossum, the creator of Python, was to design a high-level language that would be simple, functional, and versatile.
The Python language has very extensive libraries that allow the programmer to use it in various projects. From website design to machine learning and artificial intelligence, from statistical analysis to data science, Python libraries, and frameworks have made the coding and development process very easy for programmers.
All these things have made Python the first choice for people who want to start programming. That is why the Python programming language has been recognized as the most popular programming language in the world for several years in a row.
Python language features
Along with all the strengths of Python, such as simplicity, practicality, and numerous libraries mentioned above, this language has other features that can sometimes be beneficial to the programmer and sometimes detrimental to the programmer:
Python is an interpreted language, meaning that the code written in it is executed line by line by an interpreter without the need to be compiled. This feature is generally good because programs are performed live. But in programs that require heavy processing or complex calculations, it causes slow execution.
Python is a flexible language. That is, in Python, the programmer does not need to define the data type such as integer or string. Rather, Python automatically detects the data type based on the values used. This feature greatly simplifies the programmer’s job, as he can code without worrying about technical details. However, it should not be forgotten that this flexibility sometimes causes excessive memory consumption and run-time errors.
Python is indeed a general-purpose programming language. Unfortunately, this language is not suitable for developing software and mobile applications.
Comparing Python and Julia
According to the characteristics of the two programming languages Python and Julia, a comprehensive comparison of these two programming languages has been made in this section.
Speed
Before comparing these two programming languages, let’s examine the concept of speed in programming from two aspects: program execution speed and program startup time. These two concepts are different from each other.
Program execution speed means that a program can perform computational or processing operations at a high speed. In this context, Julia is usually faster than Python because Julia is specifically optimized for scientific and numerical computing, and its code is compiled directly into machine language. This makes it faster to perform heavy and complex calculations with Julia.
While app startup time refers to the amount of time it takes for an app to start working from the moment you run it. Python performs better in this field.
However, the developers of both these languages are improving the speed. For example, the Python interpreter has improved in the field of parallel and multi-core processing.
The number and variety of libraries
The number of Python libraries is very large, the number of Python libraries is estimated to be more than 137,000. These libraries cover a wide range of topics including data science, machine learning, web development, natural language processing, and more.
While Julia has fewer libraries compared to Python. However, the number of Julia libraries is increasing, and it is estimated that there are currently more than 10,000 Julia libraries. Julia libraries are mostly used in the fields of scientific and numerical calculations and machine learning.
popularity
Python is the most popular programming language in the world. The community of Python users and developers is huge. While Julia’s community hasn’t reached that size yet, it’s slowly growing. According to the TIOBE website, Python ranks first in the list of the most popular programming languages in the world. Julia’s language is ranked 29th on this list.
However, the popularity of Julia language is increasing and according to the statistics about 76% of Python programmers and developers in a survey said that they would like to choose Julia language as their next programming language.
Is Julia better or Python?
Considering all that has been said, Python and Julia are two functional programming languages. Each of the languages has its advantages and disadvantages.
However, Python is still the most popular programming language, especially in the field of machine learning and data science. Although other languages such as R and Julia have come to be used in these areas, the importance of learning Python cannot be ignored.
As a result, if you have no knowledge of programming and want to enter the boundless world of coding, and you are also interested in artificial intelligence, machine learning, and working with data, do not hesitate to participate in a comprehensive Python programming course. Python is the most popular programming language in the world and it is expected that this popularity will continue for years to come.
If you are currently a Python programmer, that is, you are familiar with all the basics of programming with Python and its libraries, and you feel that some features of Python limit your work, you can expand your programming knowledge by learning other languages such as R and Julia.