Top Programming Languages for a Data Scientist

Every prospective data scientist must take the crucial step of learning to code. But learning to programme logical operations might be intimidating, especially if you've never done it.

There are countless programming languages available that were developed for various objectives. Some offer excellent productivity and performance to process significant amounts of data, making them better suitable for data science.

 

Who Are Data Scientists?

We must first consider what data scientists do daily to select the appropriate programming language. A technical specialist who manipulates, analyses, and extracts information from data using mathematical and statistical methods is known as a data scientist. 

The field of data science encompasses a wide range of disciplines, including network analysis, natural language processing, geospatial analysis, machine learning, and deep learning. Data scientists depend on the power of computers to complete their tasks. Data scientists can communicate with and give instructions to machines via programming.

 

Why Do Data Scientists Need to Know Programming?

Contrary to popular belief, data scientists only need higher levels of expertise than software engineers. In reality, the skills required for data scientists can match a different level of software engineering expertise than developers. 

A thorough understanding of data structures and algorithms is unquestionably advantageous. However, the ideal data scientist will sometimes write tens of thousands of lines of code and sophisticated data structures. 

To use the available pre-set functions in that programming language, all you need to know is the fundamental syntax of the language in question and some basic mathematical theory.

The top data scientist programming languages for 2023 will be examined in this article, along with each language's advantages and weaknesses.

 

What Programming Languages Do Data Scientists Use?

The best-in-class data science programming languages are discussed below:

 

• Java

 

Java is one of the world's most widely used programming languages, ranking #2 in the PYPL Index and #3 in the TIOBE Index. It is an open-source, object-oriented language renowned for its superior effectiveness and performance. Numerous software programmes, websites, and technologies rely on the Java environment.

Although Java is a favoured option when creating websites or applications from scratch, Java has emerged as a critical player in the data science sector in recent years. The Java Virtual Machines, which offer a solid and practical framework for well-liked big data tools like Hadoop, Spark, and Scala, are mostly to be praised for this.

 

• Python 

 

Python is a general-purpose, open-source programming language with many uses, including web development, video game creation, and the data science business. Python can be used to complete any data science work you can imagine. 

This is primarily due to the abundance of libraries in the area. Python can carry out many tasks, from data preprocessing, visualisation, and statistical analysis to deploying machine learning and deep learning models, thanks to its thousands of robust packages and sizable user base.

 

• MATLAB

 

Since its introduction in 1984, MATLAB has been widely used in educational and scientific research. It offers solid tools for performing complex mathematical and statistical operations, making it an excellent choice for data science.

Though the fact that MATLAB is proprietary, it is expensive to obtain a licence depending on the purpose (academic, personal, or business use), making it less alluring than other programming languages available for free to use.

 

• C/ C++

 

Knowing C and its near relative C++, can be helpful when tackling computationally demanding data science tasks.

Because C and C++ are significantly quicker than other programming languages, they are excellent choices for creating big data and machine learning applications. Several essential parts of well-known machine learning libraries, such as PyTorch and TensorFlow, are developed in C++, which is not an accident.

The low-level nature of C and C++ makes them among the most complex languages to learn. Therefore, once you have a firm grasp of programming principles, mastering them is a wise choice, even though you might have yet to make your first choice when entering the field of data science.

 

• Scala

 

Scala, which stands for "Scalable Language," is a contemporary, multi-paradigm open-source programming language. This language is intended to represent common programming standards adequately. Additionally, Scala enables higher-order functions, supports nested functions, and provides a lightweight syntax for constructing anonymous functions. 

Besides this, Scala includes built-in support for pattern matching, which offers algebraic functionality common to many functional languages.

 

Which Statistical Programming Languages are Important to Data Scientists?

Data scientists value statistical programming languages because they give them a quick and convenient way to compute expected statistical outputs. The data scientists can process their data with accessible tools rather than using an improper programming language and frequently creating workarounds. 

It makes sense for data scientists to have their programming languages in hand because many of their computation needs are solved with the same.

 

• Julia

 

Julia is an emerging star in data science. Julia has made an impression on the field of numerical computing despite being one of the recent languages on this list (it was launched in 2011). When compared to other languages for data analysis, Julia—sometimes referred to be the inheritor of Python—is a potent tool.

 

• SAS

 

SAS (Statistical Analytical System) is a software environment for advanced numerical computation and corporate intelligence. There is a broad market for SAS developers because SAS has been around for a while and is widely used by significant businesses across various industries.

 

• SQL

 

A domain-specific language called SQL (Structured Query Language) enables programmers to interact with, change, and extract data from databases. You need to know databases and SQL to succeed as a data scientist.

If you are familiar with SQL, you may work with relational databases, including well-known ones like SQLite, MySQL, and PostgreSQL. SQL is a relatively flexible language because the syntax for basic queries is comparable despite the minor variations across various relational databases.

Do you want to learn SQL? Get ready to master queries by looking at our various SQL courses and skill tracks offered by Skill-Lync.

 

Conclusion 

Our suggestion for beginners in the data science training program is to start with either Python or SQL.

To find out which one you prefer best, sign up for our free introduction to Python and introduction to SQL tutorials. 

From there, persistence and practice will be essential for success.