The position of data scientist did not even exist a few decades ago. The data was already organized, cleaned, and subjected to analyses by employees. However, data scientists in the modern era have outstanding skills in high demand by businesses. Given their strength and complexity, the most popular data science programming languages should be understood by anyone who wishes to participate in this expanding field and become a data science professional. Today, keeping up with the most recent developments in the tech sector is essential. It is quite challenging to choose the top programming language for data science because there are so many of them. We will discuss each one and why they are the most popular programming languages for data science.
Top Programming Languages
Today, the most widely used and well-liked programming language is Java. It is also regarded as one of the most popular languages for data analytics. Because of the Java Virtual Machine technology, the language is particularly simple to implement on several systems. The Java Virtual Machine is used extensively in the open-source big data stack.
Some Java advantages include:
- Portable and automated memory management for quick debugging
- Ability to design visually engaging content
- Many libraries, including the Java Machine Learning Library, are supported by Java
The year 1983 saw the creation of C++ by Bjarne Stroustrup. It is also known as “the fastest programming language,” which is one of the main reasons it is widely utilized for the creation of desktop applications, video games, and search engines. Google Chrome, for example, is based on C++.
Because C++ offers a faster response time, it is employed for applications where development time is extremely important. The main applications of this C++ data science include the development of sophisticated goods like cloud systems, business software, and banking software.
Python is the most widely-used programming language for data science due to its scalability, flexibility, and simplicity. It contains very little coding and easy syntax. It also provides a large number of libraries that are always accessible.
Python is open source and can be changed in any way that programmers deem necessary. This language, which is regarded as the best for data science, is always evolving to improve performance and make the syntax clearer. It works nicely with different programming languages and is platform-neutral. This general-purpose, high-level programming language is primarily used in data science and provides a staggering array of specialized libraries. Some of the powerful Python libraries, all of which can be learned in a data science course, are:
- Scikit Learn
#4: SQL and NoSQL
Relational databases can be queried, handled, and processed with the use of SQL, or Structured Query Language. A NoSQL database stores unstructured data in the form of a document. NoSQL databases require a proprietary language different from SQL for querying.
This programming language’s syntax and organizational structure handle the analytical tasks. It is one of the most alluring programming languages for businesses because of its ability to manage enormous and complicated data volumes. It includes packages that guarantee easier management of the analysis. These are a few of the packages:
The Best of the Best
As you can see, there are only a few programming languages to know for data science. Each is important to understand fully, but several are more in demand than others. Out of the five programming languages discussed, we conclude that Python, R, and SQL are the most desirable in 2022.