Julia or Python: Which Programming Language Must Data Scientists Choose?

Before we delve deeper, let us understand the terms data science, Julia, Python separately to gain a profound view of which programming language would suit data scientists.

What is Data Science?

Data science refers to a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. It is used to make decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. It is used to scope out the right questions from the dataset. It is a multidisciplinary field that works at the raw level of data (structured, unstructured, or both) to make predictions, identify patterns and trends, build data models, and create more efficient machine learning algorithms. Data scientists work in the realm of the unknown. Some of the data science techniques are regression analysis, classification analysis, clustering analysis, association analysis, and anomaly detection.

Understanding the Python Programming Language

It is an object-oriented, high-level programming language with built-in data structures combined with dynamic typing and dynamic binding. Simply put, it is used to save data and process text, images, and numbers, and solve scientific equations. It acts as a stepping stone to understanding other languages. Python provides a productive coding environment, unlike C# and Java, and it helps coders stay organized and productive. It is the best platform to rely on for general purpose tasks such as data mining, big data, and automation. It helps develop prototypes in an efficient manner.

What is Julia?

Julia is a high-performance, high-level, flexible programming language. that is suitable for scientific and numerical computing. Its performance can be compared to that os the traditional statically-typed languages. Julia was created in 2009 by a four-person team and was revealed in 2012 to the public. Julia aims to address the shortcomings of the Python programming language and other languages that are used for scientific computing and data processing. Julia is a multi-paradigm that combines the features of functional, imperative, and object-oriented programming. It offers good performance, multiple dispatch, and optional typing that are achieved using type interface and just-in-time (JIT) compilation.  Just like languages such as R, MATLAB, and Python, it provides ease and expressiveness for high-level numeric computing. It also supports general programming by building upon the lineage of mathematical programming languages and also by borrowing from dynamic languages such as Lisp, Python, Lua, Ruby, and Perl.

Julia differs from the other typical dynamic languages in the following ways:

  • A rich language of types to construct and describe objects that can also be used to make type declarations.


  • Julia Base and the standard library, including primitive operations like integer arithmetic, are written in Julia itself. The core language of Julia imposes very little.


  • Automatic generation of specialized, efficient code for various argument types.


  • The ability to define function behaviour through many combinations of argument types.


  • Good performance, similar to statically-compiled languages such as C.


Comparison of Julia and Python

Let us now compare Julia and Python based on a few features.

  • Speed– Julia beats Python in the speed and performance category. It is as fast as the C language. Julia is not interpreted and hence, is a fast programming language. Julia is a perfect solution to performance problems as it provides great speed without any optimization and handcrafted profiling techniques. Julia’s code runs at an unmatched and brilliant speed. Nowadays, Python has also increased its speed.


  • Libraries– This is one of the drawbacks of Julia as its packages are not very well-maintained. It takes too long to plot data initially. As Julia is relatively new, it will need new libraries to flourish. Python has a rich set of libraries. Third-party libraries also support Python.


  • Community– Community is very important for any language to be active and massive. There must be a community devoted to the language. Though Julia’s community is ever-growing and enthusiastic, the size of the community is small as it is a new language. Python has been around for ages and boasts of a large community that works to its advantage. Julia’s programmer community is at a nascent stage. Python’s large community is an advantage for developers as it provides multiple resources for solving problems and doubts.


  • Parallelism– Both Python and Julia are capable of running applications in parallel. However, Python’s methods need serialization and deserialization of data for paralleling between threads. Julia’s parallelization is more refined. Julia boasts of less top-heavy parallelization syntax when compared to Python.


Why is Python better for Data Science?

Python has emerged as a popular language for data science applications. Python is a perfect option when there are data analysis tasks that need to be integrated with web apps or when statistical code must be incorporated into the production database. Python’s full-fledged programming nature makes it a perfect fit for implementing algorithms. Its libraries or packages such as NumPy, SciPy, and pandas are rooted for specific data science jobs as they produce good results.

Reasons for Python being preferred over other data science tools

Let us now look at why Python is a preferred data science tool.

  • Easy to learn– Python is easy to learn when compared to other languages like R. Python promotes a shorter learning curve and provides a syntax that is easy to learn.


  • Scalable– In comparison with other languages such as R, it has emerged as a scalable language as it provides flexibility while solving problems.


  • Choice of Libraries– Python has a rich set of libraries that are well-known in the data science community. Python’s libraries keep growing, and they provide a robust solution while addressing problems of a specific nature.


The data science landscape is changing at a rapid pace. The tools used by data scientists to extract value from data science have also grown in numbers. Python is a language that is revered by enthusiasts. Python is, no doubt, inching ahead to become the most popular language in the data science world.


Though Julia has many advantages and features in its kitty, it is an immature programming language when compared to well-established languages such as Python. The areas of code conversion and speed are much easier and better in Julia than in Python, but Python is speeding up with time. Even though Julia is regarded as a superior, top-tier language, it still has a long way to go in terms of mass consumption. Python will still continue to be a top choice of language for students, universities, and in turn, industry adaptation and job requirements. To sum up, Python is certainly the better choice for data science and machine learning-based projects, while Julia would be suitable for projects that are heavy on Maths.

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