9 Top-Notch Programming Languages for Data Science & Machine Learning
Have you ever had a question about which programming language is best for data science and machine learning? To become a data scientist or a machine learning expert, you will have to learn various programming languages. So, in this article, we will be talking about the best programming languages you should learn to become a data science or machine learning expert.
Python is the most popular and the most used programming language in the field of data science. Python is considered as one of the easiest languages to work with, its simplicity and a wide choice of the library just make it more convenient.
Python is an easy-to-use open-source language and supports various paradigms, from structured to functional and procedural programming. Python is the number one choice when it comes to machine learning and data science.
You can’t talk about Data Science without mentioning R. R is again considered as one of the best languages for data science because it was developed by statisticians for statisticians to deal with such needs.
R is typically used for statistical computing and graphics. There are numerous applications of R in data science and have multiple useful libraries for data science. R is very handy in conducting ad hoc analysis and for exploring data sets and plays an important role in Logistic Regression.
JavaScript is an object-oriented programming language that is used in data science. There are hundreds of Java libraries available today that can cover every kind of problem that a programmer may come across.
Java is a versatile language that can manage multiple tasks at once. It also helps in embedding things from electronics to web applications and desktops and can be easily scaled up for large applications. Popular processing frameworks like Hadoop also run on Java.
This elegant programming language is comparatively new, created recently back in 2003. It was initially designed with the purpose to address issues with Java but nowadays Scala is applied in numerous places ranging from web programming to machine learning.
As the name suggests, it is an effective and scalable language for handling big data. In modern-day organizations, Scala supports functional programming, object-oriented, and as well as synchronized and concurrent processing.
Structured Query Language or SQL, is a domain-specific language that has become a very popular programming language for managing data. Although SQL is not exclusively used for data science procedures, knowing SQL queries and tables is really helpful for data scientists to deal with database management systems. SQL is remarkably convenient for storing, manipulating, and retrieving data in relational databases.
Julia was developed for speedy numerical analysis and high-performance computational science which makes it an optimal language for data science. One thing that makes Julia undisputed is its speed. It is extremely fast and can work even faster than Python, R, JavaScript, or MATLAB.
It can quickly implement different mathematical concepts and excellently deals with matrices. Julia can be used for both front-end and back-end programming.
Julia comes with various data manipulation tools and mathematical libraries. Julia can also integrate with other programming languages like R, Matlab, Python, C, C++, Java, Fortran, etc. either directly or through packages.
Perl is widely used to handle data queries. Perl supports both object and procedural-oriented programming. Perl uses lightweight arrays that don’t need a high level of focus from the programmer and it is proved to be very efficient as compared to some other programming languages.
The best part about Perl is that it smoothly works with different mark-up languages like XML, HTML and also supports Unicode.
C++ has a unique spot in the data scientist’s toolkit. There is a layer of a low-level programming language on top of all modern data science frameworks and that programming language is C++. You could say that C++ has a very big role in executing the high-level code fed to the framework. This language is very simple yet extremely powerful. And guess what? C++ is one of the fastest languages out on the battlefield. And as it is a low-level language, it allows the machine learning and data scientists practitioners to have a more extensive command of its applications.
Some of the biggest pros of C++ are that it enables System programming and helps to increase the processing speed of your application. Though knowing C++ isn’t essential for data science, but it helps you to find the solutions when all other languages fail.
MATLAB comes with native support for the image, sensor, video, binary, telemetry, and other real-time formats. It offers a full set of machine learning and statistics functionality, plus a few advanced methods like system identification, nonlinear optimization, and thousands of prebuilt algorithms for image and video processing, control system design, financial modeling.
Well, if you look at it there are hundreds of programming languages in the world today, and the use-case of each language depends on what you want to do with it. Each of them has its own importance and features. So, it’s always up to you to choose the language based on your objectives and preferences for each project.
To become an expert in data science or machine learning, learning a programming language is a crucial step. Data scientists should consider the pros and cons of the various programming languages before making a decision for their projects. Now that you know about the best programming languages for data science and machine learning, it’s time for you to go ahead and practice them!