Data Science Languages: How Python trumped the rest.

With so much happening in the field of Artificial Intelligence (AI), the current buzz words are Data Science, Machine Learning and Deep Learning. One aspect that we find commonly with all these terms is the programming language Python, which as per Google Trends is currently the most popular language and more so in the AI sphere. In this post we shall explore why Python all of a sudden became the big thing it is now and how it has almost monopolised Data Science. But before that we shall quickly look at some other languages which are used in Data Science.


Developed by statisticians and very popular in statistical analysis, R is the go-for language for data analytics. The main downside of R is centered around the same, that while it is adequate for statistical programming, it is not a general purpose language. In other words, it is not usually used for purposes outside statistical programming.


Structured Query Language or SQL is a language to retrieve data from databases. As data is the lifeline of Data Science, knowing how to update, query and manipulate databases is beneficial for every Data Scientist.


Scala is a Java based general purpose language having both Object Oriented and Functional programming features. Scala can be used with Spark, a Big Data platform, making it the ideal choice while working with large volumes of data. A key feature of Scala is its ability to enable parallel processing. But the major drawback of Scala is that it is not easy to learn and master and hence not recommended for beginners.


Julia is a recently developed high-level dynamic programming language mainly employed where high performance scientific computing is required. It is a general purpose language with a syntax similar to Python, providing fast execution, arguably even faster than C code, making it the preferred choice while dealing with high-volume datasets.


Developed and licensed by MathWorks, Matlab is a giant when it comes to numerical computing and is well acclaimed in both academia and industry. Matlab is not only fast, but also provides stable and solid algorithms for complex mathematical computations.

Apart from these other languages are also used for Data Science, like Lua, Java, C, C++ and so on.


Before going into why Python is the most favoured language by Data Scientists, let’s have a very quick glance at some of its features.

Created by Guido van Rossum and appeared in 1990, Python is older than many people know it to be. It is an interpreted, high-level, general-purpose programming language supporting multiple programming paradigms: procedural, object oriented and functional. It is also dynamically typed, which means, many common programming behaviours are executed at the runtime itself, contrary to the case of static languages where these are performed during compilation. Another important feature of Python is garbage collection, an automatic memory management where the garbage collector attempts to reclaim the memory used by objects that are no longer used by the program. If you are not that into programming languages, knowing that Python can be used for a wide variety of applications and is versatile shall suffice.

Now let’s look at what all makes Python click, as far as Data Science is concerned.

Short Learning Curve

Syntax-wise Python is easy to learn and also factors in readability, providing an obvious and easy to understand code. Apart from this, what other languages take a pile of code can be accomplished with a few lines of code in Python, thus ensuring that one has to spend less time dealing with the complexities of the code.

 Excellent Collection of Pre-built Libraries

One of the greatest strengths of Python when it comes to Data Science is its collection of pre-built libraries. Libraries are modules published by different sources to perform different actions, so that the developer does not have to code them from the scratch every time. Some of the most famous of Python libraries are: Numpy, Pandas, Scikit-learn, Tensorflow, Matplotlib and so on.

Open Source

Python is completely open source and has the support of a strong and active community world over. In the Python communities and forums, Data Scientists interact and help each other out in solving problems and finding errors, fast pacing the programming.

Platform Independence

Another advantage that Python has is its platform independence, allowing it to run on any platform including Windows, MacOS, Linux, Unix, etc. With slight changes in code, the applications can be made to run from different platforms, so that developers do not have to spend time testing on these platforms.

Top-notch Visualizations

Visualizing data is essential in Data Science and Python provides a number of libraries that help with this, of which some are: Matplotlib, Seaborn, Bokeh, Plotly, etc. These enable Data Scientists of built Histograms, Charts, Plots among others for better presentation and comprehension of data.

These are some of the reasons why Python is currently reigning the Data Science arena, despite being a slow language, due to the fact that it is an interpreted language. That is, the Python code is executed line by line resulting in slower execution compared to other languages. But, this is thoroughly mitigated by the advantages that Python brings to the table, especially in Data Science sphere. With big players like Google, Facebook, Microsoft and the likes choosing Python, the popularity of which is only going to grow and can assuredly be termed a must-know for every Data Scientist.

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